abstract: # Background
  .
  Chondrosarcoma (Chs) is the third most frequent primary malignant tumour of bone and can be primary or secondary, the latter results mainly from the malignant transformation of a benign pre-existing tumour.
  .
  # Methods
  .
  All the cases diagnosed as Chs (primary tumours, recurrences and/or metastasis and xenotransplanted Chs) from the files of our Department were collected. Only cases with paraffin blocks available were selected (Total 32 cases). Six Tissue Microarrays (TMAs) were performed and all the cases and biopsies were distributed into the following groups: a) only paraffin block available from primary and/or metastatic tumours (3 TMAs), b) paraffin block available from primary and/or metastatic tumours as well as from the corresponding Nude mice xenotransplant (2 TMAs), c) only paraffin block available from xenotransplanted Chs (1 TMA). A reclassification of all the cases was performed; in addition, conventional hematoxylin-eosin as well as immunohistochemistry staining (S100, SOX-9, Ki-67, BCL-2, p53, p16, CK, CD99, Survivin and Caveolin) was analyzed in all the TMA.
  .
  # Results
  .
  The distribution of the cases according to the histopathological pattern and the location of tumours were as follows: fourteen Grade I Chs (all primaries), two primary Grade II Chs, ten Grade III Chs (all primaries), five dedifferentiated Chs (four primaries and one primary with metastasis), and two Chs from cell cultures (Ch grade III). One recurrent extraskeletal myxoid Chs was included as a control in the TMA. Although there was heterogeneity in immunohistochemistry results of the different material analyzed, S100, SOX-9, Caveolin and Survivin were more expressed. The number of passages in xenotransplants fluctuated between 1 and 13. Curiously, in Grade I Chs, these implanted tumours hardly grew, and the number of passages did not exceed one.
  .
  # Conclusion
  .
  The study of Chs by means of TMA techniques is very important because it will improve the assessment of different antibodies applied in the immunohistochemical assays. Xenotransplanted tumours in TMA improve knowledge concerning the variability in the morphological pattern shown by these tumours during the evolution in nudes.
author: Isidro Machado; Francisco Giner; Empar Mayordomo; Carmen Carda; Samuel Navarro; Antonio Llombart-Bosch
date: 2008
institute: 1Department of Pathology, University of Valencia, Valencia, Spain
references:
title: Tissue microarrays analysis in chondrosarcomas: light microscopy, immunohistochemistry and xenograft study

# Introduction

The development of Tissue Microarray (TMA) technology offers the advantage to screening large tissue cohorts for biomarker expression and to examine serial sections obtained from the same tumour specimen and xenograft tumours \[1-8\]. TMAs are especially suitable for the reproduction of morphological and immunohistochemical (IHC) results in different laboratories \[2,4,5\]. Chondrosarcoma (Chs) is the third most frequent primary malignant tumour of bone exceeded only by myeloma and osteosarcoma. It is characterised by the production of cartilage and can be primary or secondary, the latter results mainly from the malignant transformation of benign pre-existing tumours \[9,10\]. Several clinical and histopathological subtypes are recognized and the final diagnoses involve a large group of physicians including pathologists, radiologists and surgeons \[10-12\]. The conventional Chs can be classified according the location in bone in two groups: central Chs; located inside the medullar cavity and the peripheral Chs; located in the surface of the bone \[9\]. The histopathology is quite similar in both locations and is classified in three grades: Grade I, II and III. Grade I Chs are characterized by cells with small densely straining nuclei and chondroid or myxoid background, in Grade II Chs the nucleus are of moderate size with increased cellularity and low mitotic rate, Grade III Chs reveal large nuclei in areas with increased cellularity with moderate mitotic rate and scant chondroid or myxoid matrix\[9\]. Mesenchymal Ch, dedifferentiated Ch and clear cell Ch are described as infrequent variants of Chs and the clinical presentation, radiological findings and histopathological features are different to conventional forms \[9-12\]. The differential diagnosis is quite difficult between low grade Chs and benign chondral conditions, but usually the clinical data, radiography and morphological picture defines a definitive diagnosis \[9,10\].

Cytogenetic studies show heterogeneity related to karyotypic complexity, nevertheless, alterations in p16 tumour suppressor gene and p53 are related with Ch progression \[9,10,13,14\].

Xenograft models in bone tumour are of great importance because nude tumours constitute an easy source of material for tumour characterization using histopathological, IHC, electron microscopy, cytogenetic and molecular biology criteria \[6,7,15\]. Usually the histology of the tumours is preserved and they can also be followed over subsequent generations in various tumours passages \[7,15\]. Xenograft models in Chs are performed infrequently and being followed for successive generations is even more sporadic. Few publications exist on TMA study in Chs and the combination with a xenograft study is even more unusual. Therefore, the aims of this study are firstly the analysis of heterogeneity in Chondrosarcomas by means of TMA technology using histopathological and IHC criteria and secondly to analyze their successive xenografts after xenotransplantation into nude mice followed for several passages to describe the histopathological and IHC pattern variations occurring in the original tumour and their successive xenotransplants.

# Materials and methods

## Samples sources

All the cases diagnosed as Chs (primary tumours, recurrences and/or metastasis and xenotransplanted Chs) from the files of our Department were collected. Only cases with paraffin blocks available were selected. All the clinical data of the patients were reviewed. (Total: 32 cases). A reclassification of all the cases according to the new criteria for Chs diagnosis (WHO) was performed.

## Assembly of TMA

Six TMAs were performed and all the cases and biopsies were distributed into the following groups: a) only paraffin block available from primary and/or metastatic tumours (3 TMA), b) paraffin block available from primary and/or metastatic tumours as well as from the corresponding Nude mice xenotransplant (2 TMA), c) only paraffin block available from xenotransplanted Chs (1 TMA). A hematoxylin and eosin stained (H/E) section from each primary tumour and xenograft was prepared and areas of representative non-necrotic neoplasm circled on coverslip. The TMAs were assembled using a manual tissue arrayer (Beecher Instruments, Sun Prairie, WI). Normally, two cores (1 mm in thickness) of each biopsy were performed; nevertheless, more than 2 cores were made if the biopsy revealed a different pattern. All TMAs included two cores of normal kidney or liver as control tissues. Following TMA construction, H/E stained section of the TMA recipient block was prepared and reviewed to confirm the presence of intact neoplasm. Several sections of 5 μ were prepared in order to perform H/E stain as well as different IHC staining.

## Immunohistochemical analysis

IHC analysis was performed using anti-CD99 antibody (clone 12E7, DakoCytomation) at a 1:50 dilution, anti-S100 polyclonal antibody (DakoCytomation) at a 1:200 dilution, anti-SOX-9 polyclonal antibody (Santa Cruz Biotechnology, Santa Cruz, CA) at 1:100 dilution, anti-survivin polyclonal antibody (Santa Cruz Biotechnology, Santa Cruz, CA) at 1: 50 dilution, anti-p16 antibody (clone F12, Santa Cruz Biotechnology, Santa Cruz, CA) at 1:100 dilution, anti-p53 antibody (clone DO7, Novocastra) at 1:50 dilution, pan-CK (AE1/AE3) antibody (DakoCytomation) at 1:50 dilution, anti-Ki-67 antibody (MIB-1, DakoCytomation) at 1:50 dilution, anti-Caveolin (CAV) polyclonal antibody (Santa Cruz Biotechnology, Santa Cruz, CA) at 1:200 dilution, anti-Bcl-2 antibody (clone 124, Novocastra) at 1:50 dilution. Antigen retrieval was performed by pressure cooker boiling for 3 minutes in 10 mmol/L of citrate buffer (pH 6.0). The LSAB method (DakoCytomation) was used, followed by revelation with 3,3'-diaminobenzidine. Cytoplasmic and/or membrane staining was considered positive for CD99, S100, CK, CAV and Bcl-2 antibodies, nuclear staining was considered positive for SOX-9, Survivin, p53, p16 and Ki-67 antibodies. Sections were examined and immunoreactivity was defined as follow: negative, fewer than 5% of tumour cells stained; poorly positive (+), 5% to 10% of tumours cells stained; moderately positive (++), 10% to 50% of tumours cells stained and strongly positive (+++), more than 50% of the tumours cells were stained. All sections were evaluated independently by 3 pathologists (IM, SN and ALLB). The agreement of staining intensity scoring by all was recorded, and in cases of disagreement, intensity and score was determined by consensus.

## Xenotransplant

Male nude mice, were purchase from IFFA-CREDO (Lyon, France), kept under specific pathogen-free conditions throughout the experiment, and provided with vinyl isolates plus sterilized food, water, cage and bedding. The specimens for xenotransplant were obtained at surgery (OT) and placed in a culture medium (RPMI 1640) plus antibiotic at 37 ℃ until transplantation, usually 6 hours after surgery. Fragments of non-necrotic tumour, about 3 to 5 mm in size, were transplanted into the subcutaneous tissue in the backs of two nude mice. The new tumour transfers were made by following the same procedure as in the initial xenotransplant and always under highly sterile conditions. Material from different passages was obtained in order to perform all TMAs. Additional material was obtained for electron microscopy, culture, and frozen sections.

# Results

Grade I and Grade III Chs were the most frequent histopathological patterns, the distribution of the cases were as follows: fourteen Grade I Chs (all primaries), two primary Grade II Chs, ten Grade III Chs (all primaries) (Figure 1), five dedifferentiated Chs (four primaries and one primary with metastasis) and two Chs from cell cultures (Ch grade III). One recurrent extraskeletal myxoid Chs was included as a control in the TMA. Most of the tumours were located in bone and/or soft tissue. The results of IHC study are given in Table 1. Although there was heterogeneity in IHC results of the different material analyzed, S100, SOX-9, Caveolin and Survivin were more expressed (Figure 1).

Inmunohistochemical profile. N/A = Not assessable

| **Antibodies** | **N/A** | **Negative (-)** | **Poorly positive (+)** | **Moderately positive (++)** | **Strongly positive (+++)** |
|----|----|----|----|----|----|
| **S100** | 3 | 0 | 0 | 1 | 28 |
| **SOX-9** | 1 | 2 | 7 | 5 | 17 |
| **Survivin** | 2 | 2 | 4 | 3 | 21 |
| **Caveolin** | 1 | 1 | 6 | 12 | 12 |
| **CD99** | 2 | 8 | 12 | 5 | 5 |
| **p16** | 5 | 10 | 8 | 3 | 6 |
| **p53** | 2 | 16 | 6 | 6 | 2 |
| **Ki-67** | 4 | 7 | 5 | 9 | 7 |
| **Bcl-2** | 3 | 16 | 7 | 3 | 3 |
| **CK** | 1 | 28 | 2 | 1 | 0 |

Concerning xenografts, the number of passages in xenotransplants fluctuated between 1 and 13. Curiously, in low grade – Grade I Chs, these implanted tumours hardly grew, and the number of passages did not exceed one. Nevertheless, in Grade II or Grade III Chs the number of passages were greater. An example of dedifferentiated Chs evolution in nudes is described in Table 2 and (Figures: 2, 3, 4, 5, 6).

Model of Xenograft in Ch. Evolution in Nude mice

| **Material** | **De-differentiation** | **S100** | **SOX-9** | **Ki-67** | **p53** |
|----|----|----|----|----|----|
| **Original Tumour** | \+ | ++ | +++ | \+ | \+ |
| **Nu385-P0** | ++ | ++ | ++ | ++ | ++ |
| **Nu385-P1** | +++ | \+ | \+ | ++ | +++ |
| **Nu385-P2** | +++ | \+ | \+ | +++ | +++ |
| **Nu385-P3** | +++ Osteoid | \+ | \+ | +++ | +++ |

# Discussion

TMA-based morphological and IHC evaluation of Chs primary tumour and xenograft proved to be a high throughput source in demonstrating the phenotypic variability of original and xenograft Chs. Grading Chs has proven prognostic value, nevertheless the differential diagnosis between low grade Chs and benign chondroid lesions is quite difficult and requires an integration of clinical, radiology and histopathology information \[9-12\]. Grade I Ch was the most frequent in this cohort, as reported in the literature and most of the tumours appeared in bone and soft tissue \[16\]. At the histopathological level, the distinction between Grade I, II, III and dedifferentiated Chs was relatively easy using the updated criteria described \[9,10\]. S100 immunoreactivity in chondroid foci and the transcription factor SOX-9 (regulator of chondrogenesis) has been reported as the most sensitive and confirmatory markers in Chs diagnosis, both were the most expressed in the study. SOX-9 staining differentiates Chs from various small cells round tumour such as small cell osteosarcomas, non-Hodgkin lymphomas and Ewing/PNET family tumours \[17\]. Recently, the expression of Bcl-2 and parathyroid-like hormone (PTHLH) as two antibodies that supported Ch diagnosis and differentiating them from other benign conditions as osteochondromas has been described \[18\], although in the present study, Bcl-2 expression was infrequent in the original tumours and in their corresponding xenograft. Curiously, CAV and Survivin were overexpressed in Chs, therefore apoptosis and cell interactions could be related to tumour chondrogenesis. Further studies with larger series could provide new insight into the pathogenesis of Chs.

Xenograft materials are widely used by researchers although in the case of Chs tumours; few studies have been published using TMA technology \[19\]. As many Chs tumours reveal different patterns in the same biopsy, it is not surprising that the xenograft tumour showed different histopathological pictures in some cases compared with the original tumour. Usually, Grade I Chs showed no variation in histology and IHC between the primary tumours and their xenograft. Intriguingly, in Grade I Chs, these implanted tumours hardly grew, and the number of passages did not exceed one, whereas in Grade III; dedifferentiated Chs as well as in Chs from culture, the numbers of successive passages were higher, and light microscopy as well as IHC study revealed differences between primary tumour and their xenograft. Tumour cells in Chs culture could acquire newer genetic and/or epigenetic alterations that increase the aggressive phenotype; in addition, these cells in culture grow quite fast and replace stromal and necrotic cells from the original tumour. Original tumours (Grade III or dedifferentiated Ch) developed morphological transformation to a poorly differentiated subtype in some of the successive xenotransplanted tumours, in addition differences between IHC expression of chondrogenic differentiation markers (S100, SOX-9), cell cycle regulators (p53, p16) and proliferation marker (Ki-67) were detected. The dedifferentiated Ch xenograft model displayed a morphological transition over subsequent passages in nude mice, initially the tumour showed a biphasic pattern characterized by areas with typical chondroblastic differentiation surrounding by areas with marked dedifferentiation. Over the successive passages, the tumour acquired more dedifferentiation and the last tumours passages revealed osteoid material similar to osteosarcoma. The approximate period between passages was three months. In addition, S100 and SOX-9 expression showed relatively decreased expression and Ki-67, p53 and p16 revealed increased expression in the successive xenograft. Some of these changes may represent newer genetic and/or epigenetic alterations developed in more aggressive subclones or could be induced by the establishment of the xenograft as occurs in tumour progression\[6,7\]. TMAs in xenografted tumours offer an invaluable tool for performing further research in order to study newer markers of cell differentiation, activation, genetics and cell signalling in Chs. Despite the increasing use of TMAs, limitations remain, particularly in the case of tumours, due to intratumoral heterogeneity of protein expression \[1,5\]. Therefore in order to ensure TMA representativeness, more representative areas of sections of the original donor block should be selected, as well as increasing the number of cores collected and the size of the single cores \[5\]. Nowadays, TMAs are becoming an indispensable tool in the study of cancer progression and provide newer insights concerning the biology of several tumours.

# Conclusion

The analysis in TMA of Chs xenograft followed for successive generations provides new information concerning the biology and morphology of these tumours. The histopathology remained similar to the original tumour in most of the cases, but occasionally, as in the model displayed, the tumours acquire more dedifferentiation after several transfer. IHC studies using TMA techniques are useful for the assessment of the antibodies related to Chs.

### Acknowledgements

This work was supported by grants PI06/1576 , RD06-0020/0102 from the Instituto Carlos III de Madrid, Spain and Contract n°: 018814 (EuroBoNet) from the 6thFP of the EC. We would like to acknowledge Elisa Alonso for her collaboration and technical assistance with the pathology.

This article has been published as part of *Diagnostic Pathology* Volume 3 Supplement 1, 2008: New trends in digital pathology: Proceedings of the 9th European Congress on Telepathology and 3rd International Congress on Virtual Microscopy. The full contents of the supplement are available online at <http://www.diagnosticpathology.org/supplements/3/S1>


author: Jelle T. Prins\*To whom correspondence should be addressed: Jelle T. Prins, University of Groningen, Groningen, Netherlands, E-mail: <firstname.lastname@example.com>
date: 2010-04-22
institute: 1University of Groningen, Groningen, Netherlands
title: PhD Thesis: burnout among Dutch medical residents

# Introduction

Medical residents (postgraduate trainees in a medical specialty) fulfil an important role in the Dutch health care system. They take their share of the responsibility for efficient patient care in hospitals, mental health care centres, rehabilitation centres and other medical institutions. Medical residents see the period of postgraduate training as a phase in which finding a balance between training, work and private life plays an important part. Not much was known about the extent to which the stress felt by residents causes them to develop symptoms of burnout. It was also unknown which factors determine the actual development of burnout. The research which was carried out therefore revolved around the incidence of burnout among medical residents in the Netherlands. It also looked into the potential risk-heightening or risk-lowering effects of a number of demographic and work-related factors. The aims of the studies were the following:

-   to determine the prevalence of burnout among medical residents;

-   to study the effect of individual and work-related factors on burnout;

-   to examine the relationship between burnout and the quality of care.

In order to answer the research questions, two cross-sectional studies were carried out. The first study involved medical residents at University Medical Centre Groningen; the second all 5245 medical residents in the Netherlands.

# First Study

For the first study on the prevalence of burnout, medical residents at University Medical Centre Groningen in the Netherlands were approached. The Utrecht BurnOut Scale (UBOS/MBI-HHS) was used. This self-assessment form was sent to 292 medical residents. The response rate was 54%. The results show that 13% of the respondents claimed to experience moderate to severe burnout. The highest percentage of burnout was found among residents in psychiatry. Looking into the effects on burnout of emotional, informative and appreciative support experienced by residents from supervisors, fellow residents, nurses and patients showed that medical residents appeared to be less satisfied with the perceived emotional support from supervisors than with the support they received from colleagues and nurses. A significant relationship was established between dissatisfaction with emotional and appreciative support from supervisors and emotional exhaustion felt by residents. A link was also established between dissatisfaction with emotional support from supervisors and increased feelings of depersonalization. Medical residents also reported on the degree of reciprocity (the balance between pain and gain in relationships) that medical residents experienced in their working relationships with for example their supervisors. Only 13% of the residents experienced over-benefit in their relationships with their supervisors, 41% claimed under-benefit and 46% reported a good balance between give and take in the relationship (reciprocity). Medical residents who experienced under-benefit in the relationship with their supervisors reported significantly more emotional exhaustion and depersonalization than residents who experienced reciprocity in the relationship. Contrary to what we assumed, there did not appear to be any significant link between number of years in training and the perceived reciprocity in relationships.

# National study

For the national study, all medical residents registered with the Medische Registratie Commissie (Medical Registration Committee) in 2005 were approached (N=5245); 41% responded. Of the respondents, 20.6% were classified as having burnout and 14.6% and 6% of these had moderate and severe symptoms respectively. Eleven per cent of the respondents appeared to be highly engaged, 23.2% of the residents scored above the cut-off point on vigour, 36.4% on dedication, and 27.8% on absorption. Medical residents with a partner and/or children scored significantly lower on depersonalization than residents who did not have partners and/or children. The percentage of residents with symptoms of burnout was lowest in the group of residents in general surgery, followed by residents in obstetrics & gynaecology and in the supportive specialties (such as radiology, pathology). Residents in general surgery were much more engaged and vigorous than residents in other surgical specialties, internal medicine, other medical specialties, supportive specialties and psychiatry. It also appeared that residents in general surgery were more dedicated and more absorbed in their work than residents in the supportive specialties.

In the national study 94% of the residents admitted to having made one or more errors which had no negative consequences for their patients, 71% claimed to have performed procedures that they were not actually competent to carry out and 56% admitted to having made one or more errors that had negative consequences for the patient **during their training so far**. Seventy five per cent of the respondents felt that the quality of the treatment they had given was inadequate. Male residents reported more errors than female residents. The group of residents in general surgery reported the highest numbers of errors in procedures compared with residents in other specialties. Medical residents in internal medicine also reported more errors than their colleagues in a number of other specialties. Medical residents in psychiatry reported the highest number of errors relating to time problems. Medical residents with symptoms of burnout appeared to report significantly more errors than residents who did not satisfy the criteria for burnout. Highly engaged residents reported fewer errors than their less engaged colleagues.

# Conclusions

The two empirical studies included in the dissertation revealed that burnout among medical residents is no exception. The last and most comprehensive study made clear that nationwide 20.6% of medical residents experienced burnout. The Central Bureau for Statistics (CBS) in the Netherlands reported that 8-11% of the Dutch labour force was burned out. When the CBS criteria for assessing burnout were applied to the residents in the national study, the burnout percentage jumped to 41% – a prevalence that is four times higher than that in the national labour force. Although interesting from a research point of view, these percentages are troublesome from a health care perspective, especially since burnout was not only found to influence personal distress but also the quality of delivered care.

The conclusion that many residents experience an unbalanced relationship with their supervisor, which affects their well being in a negative way, is worrisome. A relationship with a supervisor is usually seen as a resource which should have positive connotations with well-being. Medical residents experience the relationship with their supervisors not only as one in which they under-benefit but they are also dissatisfied with the social support they receive from their supervisors. These factors contribute directly to the development of burnout in medical residents.

Medical residents, like other young 'high potentials', are believed to excel in different areas at the same time: work, training and private life. It could be that in the present culture medical residents are overburdened. Balancing work, training and private life can lead to unintended effects for medical residents. One might question if the responsibility for keeping in balance during residency should rest on medical residents only. In the new competency driven training programmes medical residents have to be able to organize a balance between patient care and self development. However it can be questioned if today's residents are being taught the art of balancing during their training.

# The author

Prins J.T. Burnout among Dutch medical residents, University of Groningen, juni 17th 2009 (see Figure 1 (Fig. 1) and 2 (Fig. 2)), Promotor: prof. dr. H.B.M. van de Wiel, copromotor dr. J.E.H.M. Hoekstra-Weebers. 143 pages


author: M Khoury; V Escriou; A Galy; R Yao; C Largeau; D Scherman; C Jorgensen; F Apparailly
date: 2007
institute: 1Inserm, U 844, INM, Hôpital Saint Eloi, Montpellier, France; 2Université Montpellier1, UFR de Médecine, Montpellier, France; 3Inserm, U 640, Paris, France; 4CNRS, UMR8151, Paris, France; 5Université Paris Descartes, Faculté de Pharmacie, Paris, France; 6Ecole Nationale Supérieure de Chimie de Paris, Paris, France; 7Inserm, U 790, Genethon, Evry, France; 8Université Paris-Sud 11, Orsay, France; 9CHU Lapeyronie, service Immuno-Rhumatologie, Montpellier, France
title: Combined anti-inflammatory tritherapy using a novel small interfering RNA lipoplex successfully prevents and cures mice of arthritis

# Background

TNFα is a key cytokine in rheumatoid arthritis (RA) physiopathology. We recently demonstrated that a new cationic liposome formulation allowed intravenous delivery of a small interfering RNA (siRNA) targeting TNFα and efficiently restoring the immunological balance in an experiment model of RA. Since 30% of patients do not respond to anti-TNF biotherapies, however, there is a need to develop alternative therapeutic approaches.

# Objective

Strong association of other proinflammatory cytokines with the pathogenesis of RA prompted us to investigate which cytokine other than TNFα could be targeted for therapeutic benefit using RNA interference.

# Methods

Two siRNA sequences were designed for IL-1β, IL-6 and IL-18 proinflammatory cytokines, and their efficacy and specificity were validated *in vitro* on J774.1 mouse macrophage cells, measuring both mRNA and protein levels following a lipopolysaccharide challenge. For *in vivo* administration, siRNAs were formulated as lipoplexes with the RPR209120/DOPE liposome and a carrier DNA, and were injected intravenously in DBA/1 mice having collagen-induced arthritis. The clinical course of the disease was assessed by paw thickness over time, and radiological and histological scores were obtained at euthanasia. The cytokine profiles were measured by ELISA in sera and knee-conditioned media. The immunological balance was assessed using antitype II collagen assays. The distribution of siRNAs was evaluated by fluorometry in GFP transgenic mice over time after anti-GFP siRNA lipoplex injections.

# Results

The designed siRNA sequences silenced 70–75% of the lipopolysaccharide-induced IL-1β, IL-6 and IL-18 mRNA expression in macrophages compared with a control siRNA. Each siRNA affected the targeted cytokine specifically, without modifying other proinflammatory cytokine mRNAs. In the collagen-induced arthritis model, weekly injections of siRNA lipoplexes significantly reduced the incidence and severity of arthritis, abrogating joint swelling, and destruction of cartilage and bone, in both preventive and curative settings. The most striking therapeutic effect was observed when combining the three siRNAs targeting IL-1β/IL-6/IL-18 at once. Such tritherapy was associated with downregulation of both inflammatory and autoimmune components of the disease, and overall parameters were improved compared with the TNFα siRNA lipoplex-based treatment. The siRNA formulation was widely distributed, delivering the siRNA to several organs with a strong efficacy in the liver and spleen.

# Conclusion

Tritherapy targeting IL-1β/IL-6/IL-18 seems highly effective to reduce all pathological features of RA including inflammation, joint destruction and Th1 response. These data show that cytokines other than TNFα can be targeted to improve symptoms of RA and reveal novel potential drug development targets. The systemic administration of anticytokine siRNA cocktails as a lipoplex could represent a novel and promising anti-inflammatory alternative therapy in RA.


author: Philip E. Bourne; Virginia Barbour\* E-mail: <firstname.lastname@example.com>[^1]
date: 2011-06
institute: 1Skaggs School of Pharmacy and Pharmaceutical Science, University of California San Diego, La Jolla, California, United States of America; 2Public Library of Science, Cambridge, United Kingdom
title: Ten Simple Rules for Building and Maintaining a Scientific Reputation

> *While we cannot articulate exactly what defines the less quantitative side of a scientific reputation, we might be able to seed a discussion. We invite you to crowd source a better description and path to achieving such a reputation by using the comments feature associated with this article. Consider yourself challenged to contribute.*

At a recent Public Library of Science (PLoS) journal editors' meeting, we were having a discussion about the work of the Committee on Publication Ethics (COPE; <http://www.publicationethics.org/>), a forum for editors to discuss research and publication misconduct. Part of the discussion centered on the impact such cases have on the scientific reputation of those involved. We began musing: What on earth is a scientific reputation anyway? Not coming up with a satisfactory answer, we turned to a source of endless brainpower—students and other editors. Having posed the question to a group of graduate students, PLoS, and other editors, we got almost as many different answers as people asked, albeit with some common themes. They all mentioned the explicit elements of a reputation that relate to measurables such as number of publications, H factor, overall number of citations etc., but they also alluded to a variety of different, qualitative, factors that somehow add up to the overall sense of reputation that one scientist has for another.

What these students and editors identified en masse is one important side of a scientific reputation that is defined by data; but they also identified a much more nebulous side, that, while ill-defined, is a vital element to nurture during one's career. A side defined to include such terms as fair play, integrity, honesty, and caring. It is building and maintaining this kind of less tangible reputation that forms the basis for these Ten Simple Rules. You might be wondering, how can you define rules for developing and maintaining something you cannot well describe in the first place? We do not have a good answer, but we would say a reputation plays on that human characteristic of not appreciating the value of something until you do not have it any more.

A scientific reputation is not immediate, it is acquired over a lifetime and is akin to compound interest—the more you have the more you can acquire. It is also very easy to lose, and once gone, nearly impossible to recover. Why is this so? The scientific grapevine is extensive and constantly in use. Happenings go viral on social networks now, but science has had a professional and social network for centuries; a network of people who meet each other fairly regularly and, like everyone else, like to gossip. So whether it is a relatively new medium or a centuries-old medium, good and bad happenings travel quickly to a broad audience. Given this pervasiveness, here are some rules, some intuitive, for how to build and maintain a scientific reputation.

# Rule 1: Think Before You Act

Science is full of occasions whereupon you get upset—a perceived poor review of a paper, a criticism of your work during a seminar, etc. It is so easy to immediately respond in a dismissive or impolite way, particularly in e-mail or some other impersonal online medium. Don't. Think it through, sleep on it, and get back to the offending party (but not a broader audience as it is so easy to do nowadays with, for example, an e-mail cc) the next day with a professional and thoughtful response, whatever the circumstances. In other words, always take the high road whatever the temptation. It will pay off over time, particularly in an era when every word you commit to a digital form is instantly conveyed, permanently archived somewhere, and can be retrieved at any time.

# Rule 2: Do Not Ignore Criticism

Whether in your eyes, criticism is deserved or not, do not ignore it, but respond with the knowledge of Rule 1. Failure to respond to criticism is perceived either as an acknowledgement of that criticism or as a lack of respect for the critic. Neither is good.

# Rule 3: Do Not Ignore People

It is all too easy to respond to people in a way that is proportional to their perceived value to you. Students in particular can be subject to poor treatment. One day a number of those students will likely have some influence over your career. Think about that when responding (or not responding). As hard as it is, try to personally respond to mail and telephone calls from students and others, whether it is a question about your work or a request for a job. Even if for no other reason, you give that person a sense of worth just by responding. Ignoring people can take other serious forms, for example in leaving deserving people off as paper authors. Whether perceived or real, this can appear that you are trying to raise your contribution to the paper at the expense of others—definitely not good for your reputation.

# Rule 4: Diligently Check Everything You Publish and Take Publishing Seriously

Science does not progress in certainties—that is one of its joys but also what makes it such a hard profession. Though you cannot guarantee that everything you publish will, in 50 years' time, be shown to be correct, you can ensure that you did the work to the accepted standards of the time and that, whether you were the most junior or senior author, you diligently checked it (and checked it again…) before you submitted it for publication. As a first author you may well be the only one who appreciates the accuracy of the work being undertaken, but all authors have a responsibility for the paper. So, however small or big your contribution, always be upfront with your co-authors as to the quality and accuracy of the data you have generated. When you come to be a senior author, it is so easy to take a draft manuscript at face value and madly publish it and move on. Both actions can come back to haunt you and lead to a perception of sloppy work, or worse, deception. As first author, this mainly lets down your other authors and has a subtle impact on your growing reputation. As the senior author of an error-prone study, it can have a more direct and long-lasting impact on your reputation. In short, take publication seriously. Never accept or give undeserved authorship and in addition never leave anyone out who should be an author, however lowly. Authorship is not a gift—it must be earned and being a guest or gift author trivializes the importance of authorship. Never agree to be an author on a ghostwritten paper. At best these papers have undeclared conflicts of interest; at worst potential malpractice.

# Rule 5: Always Declare Conflicts of Interest

Everyone has conflicts of interest, whether they are financial, professional, or personal. It is impossible for anyone to judge for himself or herself how their own conflict will be perceived. Problems occur when conflicts are hidden or mismanaged. Thus, when embarking on a new scientific endeavor, ranging from such tasks as being a grant reviewer, or a member of a scientific advisory board, or a reviewer of a paper, carefully evaluate what others will perceive you will gain from the process. Imagine how your actions would be perceived if read on the front page of a daily newspaper. For example, we often agree to review a paper because we imagine we will learn from the experience. That is fine. Where it crosses the line is when it could be perceived by someone that you are competing with the person whose work you are reviewing and have more to gain than just general knowledge from reviewing the work. There is a gray area here of course, so better to turn down a review if not sure. Failure to properly handle conflicts will eventually impact your reputation.

# Rule 6: Do Your Share for the Community

There is often unspoken criticism of scientists who appear to take more than they give back. For example, those who rarely review papers, but are always the first to ask when the review of their paper will be complete; scientists who are avid users of public data, but are very slow to put their own data into the public domain; scientists who attend meetings, but refuse to get involved in organizing them; and so on. Eventually people notice and your reputation is negatively impacted.

# Rule 7: Do Not Commit to Tasks You Cannot Complete

It tends to be the same scientists over and over who fail to deliver in a timely way. Over an extended period, this becomes widely known and can be perceived negatively. It is human nature for high achievers to take on too much, but for the sake of your reputation learn how to say no.

# Rule 8: Do Not Write Poor Reviews of Grants and Papers

Who is a good reviewer or editor is more than just perception. Be polite, timely, constructive, and considerate and, ideally, sign your review. But also be honest—the most valued reviewers are those who are not afraid to provide honest feedback, even to the most established authors. Editors of journals rapidly develop a sense of who does a good job and who does not. Likewise for program officers and grant reviews. Such perceptions will impact your reputation in subtle ways. The short term gain may be fewer papers or grants sent to you to review, but in the longer term, being a trusted reviewer will reflect your perceived knowledge of the field. Although the impact of a review is small relative to writing a good paper in the field yourself, it all adds up towards your overall reputation.

# Rule 9: Do Not Write References for People Who Do Not Deserve It

It is difficult to turn down writing a reference for someone who asks for one, even if you are not inclined to be their advocate; yet, this is what you should do. The alternative is to write a reference that (a) does not put them in a good light, or (b) over exalts their virtues. The former will lead to resentment; the latter can impact your reputation, as once this person is hired and comes up short, the hirer may question aspects of your own abilities or motives.

# Rule 10: Never Plagiarize or Doctor Your Data

This goes without saying, yet it needs to be said because it happens, and it is happening more frequently. The electronic age has given us tools for handling data, images, and words that were unimaginable even 20 years ago, and students and postdocs are especially adept in using these tools. However, the fundamental principle of the integrity of data, images, and text remains the same as it was 100 years ago. If you fiddle with any of these elements beyond what is explicitly stated as acceptable (many journals have guidelines for images, for example), you will be guilty of data manipulation, image manipulation, or plagiarism, respectively. And what is more, you will likely be found out. The tools for finding all these unacceptable practices are now sophisticated and are being applied widely. Sometimes the changes were done in good faith, for example, the idea of changing the contrast on a digital image to highlight your point, but one always needs to think how such a change will be perceived and in fact whether it might, even worse, give the average reader a false sense of the quality of that data. Unfortunately, even if done in good faith, if any of these practices are found out, or even raised as a suspicion, the impact on one's career can be catastrophic.

In summary, there are a number of dos and don'ts for establishing a good reputation—whatever that might be. Do not hesitate in giving us your thoughts on what it means to be a reputable scientist.

[^1]: Philip E. Bourne is Editor-in-Chief of *PLoS Computational Biology*. Virginia Barbour is Chief Editor of *PLoS Medicine* and Secretary of COPE.


abstract: # Background
  .
  Abel and Trevors have delineated three aspects of sequence complexity, Random Sequence Complexity (RSC), Ordered Sequence Complexity (OSC) and Functional Sequence Complexity (FSC) observed in biosequences such as proteins. In this paper, we provide a method to measure functional sequence complexity.
  .
  # Methods and Results
  .
  We have extended Shannon uncertainty by incorporating the data variable with a functionality variable. The resulting measured unit, which we call Functional bit (Fit), is calculated from the sequence data jointly with the defined functionality variable. To demonstrate the relevance to functional bioinformatics, a method to measure functional sequence complexity was developed and applied to 35 protein families. Considerations were made in determining how the measure can be used to correlate functionality when relating to the whole molecule and sub-molecule. In the experiment, we show that when the proposed measure is applied to the aligned protein sequences of ubiquitin, 6 of the 7 highest value sites correlate with the binding domain.
  .
  # Conclusion
  .
  For future extensions, measures of functional bioinformatics may provide a means to evaluate potential evolving pathways from effects such as mutations, as well as analyzing the internal structural and functional relationships within the 3-D structure of proteins.
author: Kirk K Durston; David KY Chiu; David L Abel; Jack T Trevors
date: 2007
institute: 1Department of Biophysics, University of Guelph, Guelph, ON, N1G 2W1, Canada; 2Department of Computing and Information Science, University of Guelph, Guelph, ON, N1G 2W1, Canada; 3Program Director, The Gene Emergence Project, The Origin-of-Life Foundation, Inc., 113 Hedgewood Drive Greenbelt, MD 20770-1610, USA; 4Department of Environmental Biology, University of Guelph, Guelph, ON, N1G 2W1, Canada
references:
title: Measuring the functional sequence complexity of proteins

# Background

There has been increasing recognition that genes deal with information processing. They have been referred to as "subroutines within a much larger operating system". For this reason, approaches previously reserved for computer science are now increasingly being applied to computational biology \[1\]. If genes can be thought of as information-processing subroutines, then proteins can be analyzed in terms of the products of information interacting with laws of physics. It may be possible to advance our knowledge of proteins, such as their structure and functions, by examining the patterns of functional information when studying a protein family.

Our proposed method is based on mathematical and computational concepts (e.g., measures). We show here that, at least in some cases in sequence analysis, the proposed measure is useful in analyzing protein families with interpretable experimental results.

Abel and Trevors have delineated three qualitative aspects of linear digital sequence complexity \[2,3\], Random Sequence Complexity (RSC), Ordered Sequence Complexity (OSC) and Functional Sequence Complexity (FSC). RSC corresponds to stochastic ensembles with minimal physicochemical bias and little or no tendency toward functional free-energy binding. OSC is usually patterned either by the natural regularities described by physical laws or by statistically weighted means. For example, a physico-chemical self-ordering tendency creates redundant patterns such as highly-patterned polysaccharides and the polyadenosines adsorbed onto montmorillonite \[4\]. Repeating motifs, with or without biofunction, result in observed OSC in nucleic acid sequences. The redundancy in OSC can, in principle, be compressed by an algorithm shorter than the sequence itself. As Abel and Trevors have pointed out, neither RSC nor OSC, or any combination of the two, is sufficient to describe the functional complexity observed in living organisms, for neither includes the additional dimension of functionality, which is essential for life \[5\]. FSC includes the dimension of functionality \[2,3\]. Szostak \[6\] argued that neither Shannon's original measure of uncertainty \[7\] nor the measure of algorithmic complexity \[8\] are sufficient. Shannon's classical information theory does not consider the meaning, or function, of a message. Algorithmic complexity fails to account for the observation that 'different molecular structures may be functionally equivalent'. For this reason, Szostak suggested that a new measure of information–functional information–is required \[6\]. Chiu, Wong, and Cheung also discussed the insufficiency of Shannon uncertainty \[9,10\] when applied to measuring outcomes of variables. The differences between RSC, OSC and FSC in living organisms are necessary and useful in describing biosequences of living organisms.

Consider two main uses for the proposed method for measuring FSC, which incorporates functionality: 1) comparative analysis of biosequence subgroups when explicit time lag is known; and 2) typicality analysis between biosequence subgroups when there is no explicit time lag. In the first case, such as an evolutionary time scale, an increase or decrease in FSC between an earlier gene or protein and a later gene or protein can be measured, evaluating its possible degradations and/or functional effects due to various changes such as insertions, deletions, mutations and rearrangements. In the second case, a large set of aligned sequences representing a protein family can be subdivided according to phylogenetic relationships derived from typicality of species groupings and the FSC for each subgroup measured. This is important when evaluating the emergence or evolution of viral or microbial strains with novel functions such as in the comparisons of the Chlamydia family genomes \[11\]. An analysis may reveal the extent of the difference in FSC between one functional group and the other, as well as the modular interactions of the internal relationship structure of the sequences \[12\].

The ability to measure FSC would be a significant advance in the ability to identify, analyze, compare, and predict the metabolic utility of biopolymeric sequences. Mutational drift, emerging pathogenic viral and microbial species/strains, generated mutations, acquired heritable diseases and mutagenic effects could all be evaluated quantitatively. Furthermore, *In vitro* experiments using SELEX \[13-15\] to study transitions in possible early ribozyme family growth could then be evaluated in a quantitative, as well as qualitative and intuitive fashion. Evolutionary changes, both actual and theoretical, can also be evaluated using FSC.

It is known that the variability of data can be measured using Shannon uncertainty \[16\]. However, Shannon's original formulation when applied to biological sequences does not express variations related to biological functionality such as metabolic utility. Shannon uncertainty, however, can be extended to measure *the joint variable* (*X, F*), where *X* represents the variability of data, and *F* functionality. This explicitly incorporates empirical knowledge of metabolic function into the measure that is usually important for evaluating sequence complexity. This measure of both the observed data and a conceptual variable of function jointly can be called *Functional Uncertainty* (*H*~*f*~) \[17\], and is defined by the equation:

H

(

X

f

(

t

)) = -∑

P

(

X

f

(

t

)) log

P

(

X

f

(

t

))

where *X*~f~ denotes the conditional variable of the given sequence data (*X*) on the described biological function *f* which is an outcome of the variable (*F*). For example, a set of 2,442 aligned sequences of proteins belonging to the ubiquitin protein family (used in the experiment later) can be assumed to satisfy the same specified function *f*, where *f* might represent the known 3-D structure of the ubiquitin protein family, or some other function common to ubiquitin. The entire set of aligned sequences that satisfies that function, therefore, constitutes the outcomes of *X*~f~. Here, functionality relates to the whole protein family which can be inputted from a database. The advantage of using *H*(*X*~f~(*t*)) is that changes in the functionality characteristics can be incorporated and analyzed. Furthermore, the data can be a single monomer, or a biosequence, or an entire set of aligned sequences all having the same common function. The significance of the statistical variations can then be evaluated if necessary \[8\]. The state variable *t*, representing time or a sequence of ordered events, can be fixed, discrete, or continuous. Discrete changes may be represented as discrete time states.

Functional bioinformatics is emerging as an important area of research \[18-20\]. Even though the term 'biological function' has been freely used for specific experimentation, there is no generally consistent usage of the term. According to Karp \[21\], biological functionality can refer to biochemical specified reactions, cellular responses, and structural properties of proteins and nucleic acids. It can be defined or specified at the global level (i.e., the entire organism), locally at the sub-molecular level, or applicable to the whole molecule. Hence confusion exists in interpreting its meaning. Karp \[21\] recognized that biological function is a complex concept. Using Webster, he refers to 'specially fitted' action or 'normal and specific contribution' of a part to the economy of the whole. Function can be related to cellular components (e.g., macromolecules, proteins or small molecules) that interact and catalyze biochemical transformations. In specific applications, it can be a local function of an enzyme such as the substrate that is acted on, or the ligands that activate or inhibit the enzyme. In more systemic integrated functions, it may refer to pathways, single or multiple, in a hierarchical, nested scope \[22,23\]. In general, it is a challenge 'to define a single best set of biologically acceptable rules for performing this decomposition' \[21\].

In our approach, we leave the specific defined meaning of functionality as an input to the application, in reference to the whole sequence family. It may represent a particular domain, or the whole protein structure, or any specified function with respect to the cell. Mathematically, it is defined precisely as an outcome of a discrete-valued variable, denoted as *F=*{*f*}. The set of outcomes can be thought of as specified biological states. They are presumed non-overlapping, but can be extended to be fuzzy elements. In order to get a meaningful calculation in measuring FSC, the measure should be statistically significant in practice \[7,10\], much larger than zero when relating to the sequences. When sequences are chosen that are unrelated to the function to be analyzed, or are simply arbitrarily ordered or randomly generated sequences, then the measure of FSC will be small and statistically not significant. For example, if many sequences that do not share the same function *f*, are mistakenly included within an aligned set representing some particular function, we should expect the measure of FSC of that set to be degraded, possibly even to a very small value. However, when the specified functionality is chosen meaningfully (even in part), then FSC can be interpreted.

Consider further, when a biosequence is mutated, the mutating sequence can be compared at two different time states going from *t*~i~ to *t*~j~. For example, *t*~i~ could represent an ancestral gene and *t*~j~ a current mutant allele. Different sequences sharing the same function *f* (as outcomes of the variables denoted respectively as *X*~f~, *Y*~f~) can also be compared at the same time *t*. Within a sequence, any change of a monomer in the sequence represents a single step change that may or may not affect the overall function. Sequence reversals, gene splits, lateral transfers, and multiple point mutations can also be quantified between the two states *t*~i~, *t*~j~. The limits of the change in functional uncertainty between the two states can then be evaluated at *t* = *t*~i~ and *t* = *t*~*j*~.

The change in functional uncertainty (denoted as Δ*H*~f~) between two states can be defined as

Δ

H

(

X

g

(

t

i

),

X

f

(

t

j

)) =

H

(

X

g

(

t

j

)) -

H

(

X

f

(

t

i

))

where *X*~f~ (*t*~i~) and *X*~g~ (*t*~j~) can be applied to the same sequence at two different times or to two different sequences at the same time. Δ*H*~f~ *can then quantify the change in functional uncertainty between two biopolymeric states with regard to biological functionality*. Unrelated biomolecules with the same function or the same sequence evolving a new or additional function through genetic drift can be compared and analyzed. A measure of Δ*H*~f~ can increase, decrease, or remain unchanged.

Biological function is mostly, though not entirely determined by the organism's genetic instructions \[24-26\]. The function could theoretically arise stochastically through mutational changes coupled with selection pressure, or through human experimenter involvement \[13-15\]. A time limit can be set in some situations to evaluate what changes to *X*~f~(*t*~i~) might be possible within that limit. For example, an estimation of the evolutionary limits projected over the next 10 years could be computed in this approach for any particular strain of the HIV virus. The specifics of the function (as an outcome of the function variable) can remain constant, or it can be permitted to vary within a range of efficiency. The limit may be determined by what is permitted metabolically. There is often a minimum limit of catalytic efficiency required by the organism for a given function.

The *ground state g* (an outcome of *F*) of a system is the state of presumed highest uncertainty (not necessarily equally probable) permitted by the constraints of the physical system, when no specified biological function is required or present. Certain physical systems may constrain the number of options in the ground state so that not all possible sequences are equally probable \[27\]. An example of a highly constrained ground state resulting in a highly ordered sequence occurs when the phosphorimidazolide of adenosine is added daily to a decameric primer bound to montmorillonite clay, producing a perfectly ordered, 50-mer sequence of polyadenosine \[3\]. In this case, the ground state permits only one single possible sequence. Since the ground state represents the state of presumed highest uncertainty permitted by the physical constraints of the system, the set of functional options, if there are any, will therefore be a subset of the permitted options, assuming the constraints for the physical system remain constant. If the ground state permits only one sequence, then there is no possibility of change in the functional uncertainty of the system.

The *null state*, a possible outcome of *F* denoted as ø, is defined here as a special case of the ground state of highest uncertainly when the physical system imposes *no constraints at all*, *resulting in the equi-probability of all possible sequences or options*. Such sequencing has been called "dynamically inert, dynamically decoupled, or dynamically incoherent" \[28,29\]. For example, the ground state of a 300 amino acid protein family can be represented by a completely random 300 amino acid sequence where functional constraints have been loosened such that any of the 20 amino acids will suffice at any of the 300 sites. From Eqn. (1) the functional uncertainty of the null state is represented as

H

(

X

ø

(

t

i

))= - ∑

P

(

X

ø

(

t

i

)) log

P

(

X

ø

(

t

i

))

where (*X*~ø~(*t*~i~)) is the conditional variable for all possible equiprobable sequences. Consider the number of all possible sequences is denoted by *W*. Letting the length of each sequence be denoted by *N* and the number of possible options at each site in the sequence be denoted by *m*, *W* = *m*^*N*^. For example, for a protein of length *N* = 257 and assuming that the number of possible options at each site is *m* = 20, *W* = 20^257^. Since, for the null state, we are requiring that there are no constraints and all possible sequences are equally probable, *P*(*X*~ø~(*t*~i~)) = 1/*W* and

H

(

X

ø

(

t

i

))= - ∑(1/

W

) log (1/

W

) = log

W

.

The change in functional uncertainty from the null state is, therefore,

Δ

H

(

X

ø

(

t

i

),

X

f

(

t

j

)) = log (

W

) -

H

(

X

f

(

t

i

)).

Physical constraints increase order and change the ground state away from the null state, restricting freedom of selection and reducing functional sequencing possibilities, as mentioned earlier. The genetic code, for example, makes the synthesis and use of certain amino acids more probable than others, which could influence the ground state for proteins. However, for proteins, the data indicates that, although amino acids may naturally form a nonrandom sequence when polymerized in a dilute solution of amino acids \[30\], actual dipeptide frequencies and single nucleotide frequencies in proteins are closer to random than ordered \[31\]. For this reason, the ground state for biosequences can be approximated by the null state. The value for the measured FSC of protein motifs can be calculated by relating the joint (*X*, *F*) pattern to a stochastic ensemble, the null state in the case of biopolymers that includes any random string from the sequence space.

## A. Functional uncertainty as a measure of FSC

The measure of Functional Sequence Complexity, denoted as ζ, is defined as the change in functional uncertainty from the ground state *H*(*X*~g~(*t*~i~)) to the functional state *H*(*X*~f~(*t*~i~)), or

ζ = Δ

H

(

X

g

(

t

i

),

X

f

(

t

j

)).

The resulting unit of measure is defined on the joint data and functionality variable, which we call *Fits (*or *F*unctional *bits)*. The unit Fit thus defined is related to the intuitive concept of *functional* information, including genetic instruction and, thus, provides an important distinction between functional information and Shannon information \[6,32\].

Eqn. (6) describes a measure to calculate the functional information of the whole molecule, that is, with respect to the functionality of the protein considered. The functionality of the protein can be known and is consistent with the whole protein family, given as inputs from the database. However, the functionality of a sub-sequence or particular sites of a molecule can be substantially different \[12\]. The functionality of a sub-molecule, though clearly extremely important, has to be identified and discovered. This problem of estimating the functionality as well as where it is expressed at the sub-molecular level is currently an active area of research in our group.

To avoid the complication of considering functionality at the sub-molecular level, we crudely assume that each site in a molecule, when calculated to have a high measure of FSC, correlates with the functionality of the whole molecule. The measure of FSC of the whole molecule, is then the total sum of the measured FSC for each site in the aligned sequences.

Consider that there are usually only 20 different amino acids possible per site for proteins, Eqn. (6) can be used to calculate a maximum Fit value/protein amino acid site of 4.32 Fits/site. We use the formula log (*20*) - *H*(*X*~f~) to calculate the functional information at a site specified by the variable *X*~f~ such that *X*~f~ corresponds to the aligned amino acids of each sequence with the same molecular function *f*. The measured FSC for the whole protein is then calculated as the summation of that for all aligned sites. The number of Fits quantifies the degree of algorithmic challenge, in terms of probability, in achieving needed metabolic function. For example, if we find that the Ribosomal S12 protein family has a Fit value of 379, we can use the equations presented thus far to predict that there are about 10^49^ different 121-residue sequences that could fall into the Ribsomal S12 family of proteins, resulting in an evolutionary search target of approximately 10^-106^ percent of 121-residue sequence space. In general, the higher the Fit value, the more functional information is required to encode the particular function in order to find it in sequence space. A high Fit value for individual sites within a protein indicates sites that require a high degree of functional information. High Fit values may also point to the key structural or binding sites within the overall 3-D structure. Since the functional uncertainty, as defined by Eqn. (1) is proportional to the -log of the probability, we can see that the cost of a linear increase in FSC is an exponential decrease in probability.

For the current approach, both equi-probability of monomer availability/reactivity and independence of selection at each site within the strand can be assumed as a starting point, using the null state as our ground state. For the functional state, however, an *a posteriori* probability estimate based on the given aligned sequence ensemble must be made. Although there are a variety of methods to estimate *P*(*X*~f~(*t*)), the method we use here, as an approximation, is as follows. First, a set of aligned sequences with the same presumed function, is produced by methods such as CLUSTAL, downloaded from Pfam. Since real sequence data is used, the effect of the genetic code on amino acid frequency is already incorporated into the outcome. Let the total number of sequences with the specified function in the set be denoted by *M*. The data set can be represented by the N-tuple *X* = (*X*~1~, ... *X*~N~) where *N* denotes the aligned sequence length as mentioned earlier. The total number of occurrences, denoted by *d*, of a specific amino acid "aa" in a given site is computed. An estimate for the probability that the given amino acid will occur in that site *X*~*i*~, denoted by *P*(*X*~*i*~ = "aa") is then made by dividing the number of occurrences *d* by *M*, or,

P

(

X

i

= "aa") =

d

/

M

.

For example, if in a set of 2,134 aligned sequences, we observe that proline occurs 351 times at the third site, then *P* ("proline") = 351/2,134. Note that *P* ("proline") is a conditional probability for that site variable on condition of the presumed function *f*. This is calculated for each amino acid for all sites. The functional uncertainty of the amino acids in a given site is then computed using Eqn. (1) using the estimated probabilities for each amino acid observed. The Fit value for that site is then obtained by subtracting the functional uncertainty of that site from the null state, in this case using Eqn. (4), log20. The individual Fit values for each site can be tabulated and analyzed. The summed total of the fitness values for each site can be used as an estimate for the overall FSC value for the entire protein and compared with other proteins.

## B. Measuring changes in FSC

In principle, some proteins may change from a non-functional state to a functional state gradually as their sequences change. Furthermore, iso-enzymes in some cases may catalyze the same reaction, but have different sequences. Also, certain enzymes may demonstrate variations in both their sequence and their function. Finally, a single mutation in a functional sequence can sometimes render the sequence non-functional relative to the original function. An example of this effect has been observed in experiments with the ultrabiothorax (Ubx) protein \[17,33\].

From Eqn. (6), the FSC of a biosequence can be measured as it changes with time, as shown in Figure <a href="#F1" data-ref-type="fig">1</a>. When measuring evolutionary change in terms of FSC, it is necessary to account for a change in function due to insertions, deletions, substitutions and shuffling. Evolutionary change that involves a change from a non-functional state that is not in the null state, or from an existing function *f*~a~ to a modified function *f*~b~ that is either different in terms of efficiency or function, is given by

ζ

E

= Δ

H

(

X

fa

(

t

i

),

X

fb

(

t

j

)).

The sequences (corresponding to *X*~fa~ with initial function *f*~*a*~) have two components to it, relative to that of *X*~fb~ (with resulting mutated function *f*~*b*~). The *static component* is that portion of the subsequence that must remain within the permitted sequence variation of the original biosequence with function *f*~a~ while, at the same time, enabling the new function *f*~b~. The *mutating component* is the portion of *X*~fa~ that must change to achieve either the new function *f*~b~, where the new function is to be understood as either a new level of efficiency for the existing function, or a novel function different from *f*~a~. This is a convenient simplification, assuming that the two components are separate according to the aligned sites. Currently we are also studying scenarios when the two components may be mixed, possibly at different times. The mutating component can be assumed to be in the null state relative to the resulting sequences of *X*~fb~. Since the mutating component is the only part that must change, we can ignore the static component *provided we include the probability of it remaining static* during the mutational changes of the mutating component.

In practice, the sequence space for possible novel functional states may not be known. However, by considering particular proteins, estimated mutation rates, population size, and time, an estimated value for the probability can be chosen and substituted into the relevant components of Eqn. (9) to limit search areas around known biosequences that are observed, such as protein structural domains, to see what other possible states within that range might have some selective advantage. In this way, possible evolutionary paths for the formation of certain protein families might be reconstructed. For example, using this method, it might be possible to predict, say, future viral strains within certain limits.

Intuitively, the greater the reduction in FSC a mutation produces, the more likely the mutation is deleterious to the given function. This can be evaluated using known mutations introduced individually into a set of aligned, wild-type sequences to measure the change in FSC. The results could then be ranked. Operating under the hypothesis that mutations producing the greatest decrease in FSC are most likely to be deleterious, experimental investigations into certain genes with certain mutations could be prioritized according to how negatively they affect FSC.

# Results and Discussion

For the 35 protein families analyzed, a measure of FSC in Fits for each site was computed from their aligned sequence data on PFAM. The results for the families, as well as an array of randomly (uniformly) generated sequences and an ordered 50-mer polyadenosine sequence are shown in Table <a href="#T1" data-ref-type="table">1</a>. They reveal significant aspects of FSC described below.

FSC of Selected Proteins

|  | **length (aa)** | **Number of Sequences** | **Null State (Bits)** | **FSC (Fits)** | **FSC Density Fits/aa** |
|----|----|----|----|----|----|
| Ankyrin | 33 | 1,171 | 143 | 46 | 1.4 |
| HTH 8 | 41 | 1,610 | 177 | 76 | 1.9 |
| HTH 7 | 45 | 503 | 194 | 83 | 1.8 |
| HTH 5 | 47 | 1,317 | 203 | 80 | 1.7 |
| HTH 11 | 53 | 663 | 229 | 80 | 1.5 |
| HTH 3 | 55 | 3,319 | 238 | 80 | 1.5 |
| Insulin | 65 | 419 | 281 | 156 | 2.4 |
| Ubiquitin | 65 | 2,442 | 281 | 174 | 2.7 |
| Kringle domain | 75 | 601 | 324 | 173 | 2.3 |
| Phage Integr N-dom | 80 | 785 | 346 | 123 | 1.5 |
| VPR | 82 | 2,372 | 359 | 308 | 3.7 |
| RVP | 95 | 51 | 411 | 172 | 1.8 |
| Acyl-Coa dh N-dom | 103 | 1,684 | 445 | 174 | 1.7 |
| MMR HSR1 | 119 | 792 | 514 | 179 | 1.5 |
| Ribosomal S12 | 121 | 603 | 523 | 359 | 3.0 |
| FtsH | 133 | 456 | 575 | 216 | 1.6 |
| Ribosomal S7 | 149 | 535 | 644 | 359 | 2.4 |
| P53 DNA domain | 157 | 156 | 679 | 525 | 3.3 |
| Vif | 190 | 1,982 | 821 | 675 | 3.6 |
| SRP54 | 196 | 835 | 847 | 445 | 2.3 |
| Ribosomal S2 | 197 | 605 | 851 | 462 | 2.4 |
| Viral helicase1 | 229 | 904 | 990 | 335 | 1.5 |
| Beta-lactamase | 239 | 1,785 | 1,033 | 336 | 1.4 |
| RecA | 240 | 1,553 | 1,037 | 832 | 3.5 |
| Bac luciferase | 272 | 1,900 | 1,176 | 357 | 1.3 |
| tRNA-synt 1b | 280 | 865 | 1,210 | 438 | 1.6 |
| SecY | 342 | 469 | 1,478 | 688 | 2.0 |
| EPSP Synthase | 372 | 1,001 | 1,608 | 688 | 1.9 |
| FTHFS | 390 | 658 | 1,686 | 1,144 | 2.9 |
| DctM | 407 | 682 | 1,759 | 724 | 1.8 |
| Corona S2 | 445 | 836 | 1,923 | 1,285 | 2.9 |
| Flu PB2 | 608 | 1,692 | 2,628 | 2,416 | 4.0 |
| Usher | 724 | 316 | 3,129 | 1,296 | 1.8 |
| Paramyx RNA Pol | 887 | 389 | 3,834 | 1,886 | 2.1 |
| ACR Tran | 949 | 1,141 | 4,102 | 1,650 | 1.7 |
| Random sequences | 1000 | 500 | 4,321 | 0 | 0 |
| 50-mer polyadenosine | 50 | 1 | 0 | 0 | 0 |

**Results for 35 protein families** Shown above are the 35 protein families analyzed, their sequence length (column 1), the number of sequences analyzed for each family (column 2), the Shannon uncertainty of the Null State *H*~ø~ (Eqn. 4) for each protein (column 3), the FSC value ζ in Fits for each protein (column 4), and the average Fit value/site (FSC/length, column 5). For comparison, the results for a set of uniformly random amino acid sequences (RSC) are shown in the second from last row, and a highly ordered, 50-mer polyadenosine sequence (OSC) in the last row. All values, except for the OSC example, which was calculated from the constrained ground state required to produce OSC, were computed from the null state. The Fit values obtained can be discussed as the measure of the change in functional uncertainty required to specify any functional sequence that falls into the given family being analyzed.

First, as observed in Table <a href="#T1" data-ref-type="table">1</a>, although we might expect larger proteins to have a higher FSC, that is not always the case. For example, 342-residue SecY has a FSC of 688 Fits, but the smaller 240-residue RecA actually has a larger FSC of 832 Fits. The Fit density (Fits/amino acid) is, therefore, lower in SecY than in RecA. This indicates that RecA is likely more functionally complex than SecY. The results for the array of random sequences and for a 50-mer polyadenosine sequence formed on Montmorillonite show that Δ*H*~f~ distinguishes FSC from RSC and OSC. The results for the array of random sequences are shown in the second from the last row of Table <a href="#T1" data-ref-type="table">1</a>, and indicate that random sequences, which are an example of RSC, tend to have an FSC of approximately 0 Fits. The results of the highly ordered 50-mer polyadenosine, which is an example of OSC, are shown in the last row of Table <a href="#T1" data-ref-type="table">1</a>, and indicate an FSC of approximately 0 Fits. This is consistent with Abel and Trevors' prediction that neither OSC nor RSC can contain the functional sequence complexity observed in biosequences.

A plot of FSC vs. protein length for the 35 selected protein families is shown in Figure <a href="#F2" data-ref-type="fig">2</a>. The x-intercept was found to be approximately 23 amino acids. For the 35 protein families analyzed, there were no points corresponding to any protein families in the lower right area of the plot. The right hand plot in Figure <a href="#F2" data-ref-type="fig">2</a> shows the average number of Fits/site for the 35 protein families analyzed. For our small sample of 35 protein families, we found no points between 0 and 1.3 Fits/site.

To demonstrate some ways in which our approach can be applied to proteins, ubiquitin was chosen. A sample plot of FITs/site and amino acid conservation for each site, is shown in Figure <a href="#F3" data-ref-type="fig">3</a> for Ubiquitin. Data for the first 5 sites in the aligned set was not available from PFAM. The conservation value was obtained by subtracting the total number of different options observed at a given site, from the total possible options. In this case, 20 different amino acids. If all 20 amino acids were permitted at a site, then the conservation value was 0. A maximum value of 19 would obtain if only 1 amino acid was observed at the site. From Figure <a href="#F3" data-ref-type="fig">3</a>, it can be observed that a high conservation value usually corresponded to a high measured FSC value. The measurement is also affected by the number of amino acids observed which could be different for different sites. For example, site 37 shows 20 observed amino acids, but still a relatively high value of 2.9 Fits. Conservation of a site reflects the degree of variations which is affected by both the number of observed amino acids and their frequency of observation in the alignment \[12\]. For example, if a site is observed to be dominated by only a few amino acids even though all the amino acid types are observed, its measured FSC could still be high.

From the plot in Figure <a href="#F3" data-ref-type="fig">3</a>, one can observe which sites have higher measured FSC value. An arbitrary lower cutoff limit of 3.32 Fits/site was chosen to indicate high measured FSC sites, presumed to be significantly associated with the functionality of the whole molecule. A more rigorous statistical analysis can also be used as well as other types of measures based on dependency \[12\]. All sites exceeding that lower cutoff value were examined. A total of seven sites were found that had a high value that was between 3.32 Fits/site and the maximum of 4.32 Fits/site. These sites were located on a 3-D model of ubiquitin (1AAR.pdb) as shown in Figure <a href="#F4" data-ref-type="fig">4</a>. Out of the 7 sites, 6 where located on the binding domain containing the binding site Lys-48 \[34\]. The site with the maximum FIT value was site 47, immediately next to the binding site. Surprisingly, the binding site itself was poorly conserved, with an amino acid conservation value of only 1, albeit a relatively high value of 2.91 Fits. Five of the remaining 6 sites were found to be clustered in the area of the binding site, with bonds between Leu-50 and Leu-43, as well as between Gly-41, Lys-27, and Asp-52, as shown in Figure <a href="#F5" data-ref-type="fig">5</a>. Since these sites had the highest FSC values in ubiquitin, we infer that they play a critical role in either binding, or in the structure of the binding site domain for that protein. The fact that of the top seven sites, one was immediately adjacent to the binding site, and five others were located on the structure supporting the binding site lends support for our hypothesis that high FIT values can be used to locate key functional components of a protein family.

In this paper, we have presented an important advance in the measurement of the FSC of biopolymers. It was assumed that aligned sequences from the same Pfam family, could be assigned the same functionality label. Even though the same functionality may not be applicable to individual sites, site independence and significance was assumed and the measured FSC of each site was summed. However, further extension of the method should be considered \[12,35\]. For example, if dependency of joint occurrences is detected between the outcomes of two variables *X*~3~ and *X*~4~ in the aligned sequences, then the N-tuple representation of the sequences could be transformed into a new R-tuple *Y*~R~ where these outcomes of *X*~3~ and *X*~4~ are represented as outcome by a single variable *Y*~3~ as shown in Figure <a href="#F6" data-ref-type="fig">6</a>. An outcome of the two variables in *X*~3~ and *X*~4~ correspond to a hypercell in Y~R~. A more accurate estimate of FSC could then be calculated. We are currently considering this more general scenario.

The measurement in Fits of the FSC provides significant information about how specific each monomer in the sequence must be to provide the needed/normal biofunction. The functional information measures the degree of challenge involved in searching the sequence space for a sequence capable of performing the function. In addition, Fits can be summed for every sequence required to achieve a complete functional biochemical pathway and integrated cellular metabolism, including regulatory proteins. In principle, it may be possible to estimate a FSC value for an entire prokaryotic cell where the genome has been sequenced and all translated proteins are known. Simpler genomes in viruses may be an excellent example for this kind of analysis. That is, further analysis of the FSC values will provide a starting point to reveal important information about the processes for an entire organism such as the virus.

# Conclusion

A mathematical measure of functional information, in units of Fits, of the functional sequence complexity observed in protein family biosequences has been designed and evaluated. This measure has been applied to diverse protein families to obtain estimates of their FSC. The Fit values we calculated ranged from 0, which describes no functional sequence complexity, to as high as 2,400 that described the transition to functional complexity. This method successfully distinguishes between FSC and OSC, RSC, thus, distinguishing between order, randomness, and biological function.

# Methods

The following is a brief summary of the methods used (additional file <a href="#S1" data-ref-type="supplementary-material">1</a>). A more detailed description is available as an online supplement. Eqn. (6) was applied to 35 protein families or protein domain families, to estimate the value of the FSC for any protein included within that family. A program was written, using Python, to analyze the two-dimensional array of aligned sequences for a protein family and is available online (additional files <a href="#S2" data-ref-type="supplementary-material">2</a>, <a href="#S3" data-ref-type="supplementary-material">3</a>, <a href="#S4" data-ref-type="supplementary-material">4</a>, <a href="#S5" data-ref-type="supplementary-material">5</a>, <a href="#S6" data-ref-type="supplementary-material">6</a>, <a href="#S7" data-ref-type="supplementary-material">7</a>, <a href="#S8" data-ref-type="supplementary-material">8</a>, and <a href="#S9" data-ref-type="supplementary-material">9</a>). The data for the arrays was obtained from the Pfam database \[36\]. Eqn. (7) was used to estimate the probability for each amino acid at each site.

# Competing interests

The author(s) declare that they have no competing interests.

# Authors' contributions

KD helped develop the formulation and measure of FSC, wrote the software, carried out the analysis and drafted the manuscript. DC developed, together with KD, the basic formula of functional entropy and evaluated the different mathematical forms. DC provided suggestions to the experimental design and its interpretations. DLA's contributions included defining/delineating the three subsets of sequence complexity and their relevance to biopolymeric information, contributing to the first draft of the paper, critiquing KD's quantification methodology, contributing references, and coining the term "fits" for "functional bits" as the unit of measure of Functional Sequence Complexity (FSC). JT participated in the design and coordination of the study. All authors read and approved the final manuscript.

# Supplementary Material

###### Additional File 1

Methods. Additional details of the methods used in this project

Click here for file

###### Additional File 2

Main Program. A copy of the coding for the main program used in this paper

Click here for file

###### Additional File 3

AminoFreq. A required module for the main program

Click here for file

###### Additional File 4

ColTot. A required module for the main program

Click here for file

###### Additional File 5

Convert. A required module for the main program

Click here for file

###### Additional File 6

DistEnt. A required module for the main program

Click here for file

###### Additional File 7

FormArray. A required module for the main program

Click here for file

###### Additional File 8

StripName. A required module for the main program

Click here for file

###### Additional File 9

P53DNADom. A sample data set for the reader

Click here for file

### Acknowledgements

The research is supported by the National Sciences and Engineering research Council (NSERC) of Canada, Discovery Grant, the Korean Research Foundation Grant (KRF-2004-042-C00020) of one of the authors.


author: Neil Hall
date: 2012
institute: 1Centre for Genomic Research, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
title: Why science and synchronized swimming should not be Olympic sports

The brief, for my intermittent comment column for *Genome Biology*, was to "give a UK perspective" while "keeping it interesting for an international audience". That's a tough brief. There is little reason for people to take an interest in events taking place in a rainy windswept island, famous mostly for bad food, greedy bankers and reality shows. Recently, though, because of the London 2012 Olympics, the focus of the world's attention has briefly flitted in our direction. So, while basking in our reflected Olympic glory, I will start with sport.

In the UK we can be rightly proud of the fact that (as the head of the International Olympic Committee pointed out) we as a nation have done much to codify many Olympic sports. But this probably points to a national failing: we love rules, we love measuring performance and hence we love inventing sports. That's why we invented cricket; it has loads of obscure rules and loads of complex performance statistics. A game that can take 5 days and has to stop for rain or bad light (in England!) was not invented for the drama or spectacle. Our obsession with performance metrics is not just limited to sport; the government has taken to measuring and publishing the performance of everything from schools and hospitals to police forces and train operators. They have even tried to quantify our national happiness (<http://www.ons.gov.uk/ons/rel/wellbeing/measuring-national-well-being/summary-of-proposed-domains-and-measures/summary-of-proposed-domains-and-measures-of-national-well-being.html>).

Sportsmen and -women can obsess about measuring their performance and gauging it against their past performance and the performance of others, but researchers are quite different. In academia we don't like other people judging what we do (or even defining what we do) and we tend not to like metrics designed to measure our performance. But many years ago, UK government decided to ignore these protestations from our ivory towers and created a mechanism for measuring research quality called the Research Excellence Framework (or REF for short). REF is the reason that some readers may have noticed UK-based collaborators acting increasingly strangely, maybe looking stressed and distant, obsessing about impact factors and questioning the value of everything they do against impenetrable metrics.

# REF 101

For the benefit of non-UK-based readers I will need to give some background. In the UK we have only recently taken to crucifying our youth with a lifetime of debt to fund their education. Hence, there are still large sums of money coming from central government to universities. Each year, £1billion will be paid directly to institutions based on research quality. The sum of money each university receives is decided on by the REF, and here, in brief, are the rules.

## 1. Publish rarely but well

The REF occurs every 7 years, although it changes its name more than The Artist Formerly Known As Prince. It used to be called the Research Assessment Exercise and before that the Research Selectivity Exercise. The nuances of the process have evolved, but a key metric in each assessment is the quality of the research outputs (papers). Each academic can submit just four papers published in the last 7 years to be judged for their 'excellence in originality, significance and rigor', against these murky definitions:

• 4 star - Quality that is world leading

• 3 star - Quality that is internationally excellent

• 2 star - Quality that is recognized internationally

• 1 star - Quality that is recognized nationally

To me, these read like color descriptions on paint tins (linen white, antique white, foam white, cloud white, and so on) and if you are trying to work out what the difference is between 'world leading' and 'internationally excellent', you are not alone. Most UK academics are currently kept awake with such thoughts. This is probably a futile exercise as the star ratings are assigned by REF panels, each with about 20 members, and each member will have to read hundreds of papers, many of which are outside their area of expertise. It is hard to imagine that this review process is not guided by journal impact factors or the esteem with which the submitting academic is held in by the panel. However, these ratings are important as the stars for all the papers submitted by each department will be added up to evaluate their research quality.

Most of you will also be thinking that four papers in 7 years is not a lot... and it isn't. So, the reason your UK collaborators are not publishing the stuff they presented at conferences last year is because they are hoping to roll it in with more data into a more prestigious journal later.

## 2. Hire for the short term

Inexplicably, although the process is designed to measure the performance of universities, the star ratings for the papers go to the investigator, regardless of where they were working at the time of publication. This means that you can buy in researchers with a good set of publications simply to use them in your REF audit. This has created what amounts to a 'transfer deadline' and a highly skewed academic job market in the UK in which universities hire on a 7-yearly cycle, and increases in professorial salaries have outstripped those for junior faculty. This is reminiscent of sports stars; a few individuals will be paid extremely well but very little cash goes to the grass roots.

I should acknowledge that I have been a beneficiary of this system. When I realized that if I lived in the USA any longer my son would soon complete his metamorphosis into Bart Simpson, I started to look for work in the UK. It turned out that this was just before the cyclical REF (then called the RAE). And, as I had been working as a project leader at genome centers during a period when *Nature* would essentially publish raw electropherograms (or as I call it, 'The good old days'), I was nailed-on 'Four Star'. Therefore, my prospective employer could hire me on a healthy professorial salary and provide me with a good start up in the knowledge that the government would refund the cost in return for my papers.

As a scientist, I was delighted; as a taxpayer, I should have been outraged.

## 3. Spend, spend, spend

At this point I know some of you think I am making this up, but the other metric that decides how much money you receive is how much money you have spent. This does sound like a futile positive feedback loop that could only have been invented by a Lehman Brothers investment banker, but it genuinely is a measure used to score the 'Research Environment', which is a key metric in the REF. According to how this metric is measured, a department that has a large research spend is considered to be a better environment than those with a lower spend. As I run a genomics lab, I am obviously thrilled with this metric because I can certainly generate a good research environment as judged by the REF, but some people may argue that a great research environment is instead one that produces good papers without spending huge sums of money.

# Measuring the unmeasurable

I would like to think that anyone reading this would conclude that the REF is a shockingly bad way to judge research performance. And it's fair to say I have paraphrased the rules (there is a 106 page document on just the panel working methods for those wanting more detail: <http://www.ref.ac.uk/pubs/2012-01/>). I have also highlighted the easy targets for ridicule. I can totally understand that governments who fund research would like to measure performance, but I question their ability to do so and I would challenge anyone to come up with a better system.

Athletics is sport in its purest form and has basic measurements (height, length and speed) that can be used to define who is the best. Usain Bolt has a single, very simple KPI (that's a Key Performance Indicator for those not on university management committees). But there are other 'sports' such as gymnastics, diving, dressage and synchronized swimming that are much less clear cut. I personally think that anything that uses a judging panel to score artistic interpretation should not be a sport. Now imagine what would happen if, instead of judging panels, the synchronized swimmers scored each other anonymously. I expect that things could get quite divisive.

Like many others, I really enjoyed my 4-yearly dose of synchronized swimming during the Olympics. It was dramatic, entertaining and thrilling, but making it a sport is missing the point. You may as well make ballet and stand-up comedy Olympic sports as well. Unlike a 100 m sprint, the measurement of performance is far too subjective. At some point one has to accept that some things can have intrinsic value but they can't be objectively measured.

Science quality is difficult to measure, yet we all know when we see something amazing or dramatic, whether it is a Higgs boson or a Neanderthal genome, and choosing which of these is best is pointless. In science, as with ballet, comedy or synchronized swimming, there should be no winners; because you can't measure who won.