What would classify those findings of the University of Sheffield experiment as aliens?

What would classify those findings of the University of Sheffield experiment as aliens?

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It's in the news that some experiment was able to capture some alien specimens during a meteor shower:

Despite all the skepticism that has already been said about this finding, what would be a proof that such specimem came from outer space? Is there any established protocol to assure that some form of life didn't come from Earth?

I wouldn't say that there is an established protocol - claims of alien life are not commonly testable or even legitimate - but if something were discovered and deemed "alive," a fairly good sign it was alien would be if its genetic makeup were based on something other than DNA or RNA. That would be a dead giveaway the organism had side-stepped every known organism on Earth and while that doesn't prove alien-ness, it would certainly hint strongly of a non-Terran genesis. There are arguments for and against carbon/CHNOPS-based life but that is nowhere near as definitive.

Also, there's another really good technique. I don't like to resort to ad hominem attacks, but ideally the discovery should be by someone who has not already made verifiable false claims on the same topic.

Richard Bentall was born in Sheffield in the United Kingdom. After an unpromising school career at Uppingham School in Rutland and then High Storrs School in his home town, he attended the University College of North Wales, Bangor as an undergraduate before registering for a PhD in Experimental Psychology at the same institution.

After being awarded his doctorate, he moved to the University of Liverpool to undertake professional training as a clinical psychologist. He later returned to his alma mater of Liverpool to work as a lecturer, after a brief stint working for the National Health Service as a forensic clinical psychologist. Later, he studied for an MA in Philosophy Applied to Healthcare from the University of Wales, Swansea. He was eventually promoted to Professor of Clinical Psychology at the University of Liverpool. In 1999, he accepted a position at the University of Manchester, collaborating with researchers based there who were working in understanding the treatment of psychotic experiences. [1] After returning in 2007 to a professorial position at Bangor University, where he retains an honorary professorship, he returned to the University of Liverpool in 2011, before moving to the University of Sheffield in 2017. His research continues to focus on the psychological mechanisms of severe mental illness and social factors that affect these mechanisms, [2] which has led to a recent interest in public mental health. In 1989, he received the British Psychological Society's Division of Clinical Psychology 'May Davidson Award', an annual award for outstanding contributions to the field of clinical psychology, in the first ten years after qualifying. [3] In 2014 he was elected a Fellow of the British Academy, the United Kingdom's national academy for the humanities and social sciences. [4]

He has previously published research on differences between human and animal operant conditioning and on the treatment of chronic fatigue syndrome. However, he is best known for his work in psychosis, especially the psychological processes responsible for delusions and hallucinations and has published extensively in these areas. [5] His research on persecutory (paranoid) delusions has explored the idea that these arise from dysfunctional attempts to regulate self-esteem, so that the paranoid patient attributes negative experiences to the deliberate actions of other people. His research on hallucinations has identified a failure of source monitoring (the process by which events are attributed to either the self or external sources) as responsible for hallucinating patients' inability to recognise that their inner speech (verbal thought) belongs to themselves. Along with many other British researchers, he has used these discoveries to inform the development of new psychological interventions for psychosis, based on cognitive behavioural therapy (CBT). This work has included randomised controlled trials of CBT for first episode patients and patients experiencing an at risk mental state for psychosis.

In a 1992 thought experiment, Bentall proposed that happiness might be classified as a psychiatric disorder. [6] The purpose of the paper was to demonstrate the impossibility of defining psychiatric disorder without reference to values. The paper was mentioned on the satirical television program Have I Got News for You and quoted by the novelist Philip Roth in his novel Sabbath's Theater.

He has edited and written several books, most notably Madness Explained, which was winner of the British Psychological Society Book Award in 2004. In this book, he advocates a psychological approach to the psychoses, rejects the concept of schizophrenia and considers symptoms worthwhile investigating in contrast to the Kraepelinian syndromes. (Refuting Kraepelin's big idea that serious mental illness can be divided into discrete types is the starting chapter of the book.) A review by Paul Broks in The Sunday Times summarised its position as: "Like Szasz, Bentall is firmly opposed to the biomedical model, but he also takes issue with extreme social relativists who would deny the reality of madness." In the book, Bentall also argues that no clear distinction exists between those diagnosed with mental illnesses and the "well". While this notion is more widely accepted in psychiatry when it comes to anxiety and depression, Bentall insists that schizotypal experiences are also common. [7]

In 2009 he published Doctoring The Mind: Is Our Current Treatment Of Mental Illness Really Any Good? A review of this book by neuro-scientist Roy Sugarman argued that it allied itself with the anti-psychiatry movement in its critiques of biological psychiatry. [8] The review in PsycCRITIQUES was more nuanced, pointing out that Bentall did not reject psycho-pharmacology, but that he was concerned over its overuse. [9]

In 2010, Bentall and John Read co-authored a literature review on "The effectiveness of electroconvulsive therapy" (ECT). It examined placebo-controlled studies and concluded ECT had minimal benefits for people with depression and schizophrenia. [10] The authors said "given the strong evidence of persistent and, for some, permanent brain dysfunction, primarily evidenced in the form of retrograde and anterograde amnesia, and the evidence of a slight but significant increased risk of death, the cost-benefit analysis for ECT is so poor that its use cannot be scientifically justified". [11] Psychiatrists, however, sharply criticized this paper in passing by calling it an "evidence-poor paper with an anti-ECT agenda". [12]

In 2012, Bentall and collaborators in Maastricht published a meta-analysis of the research literature on childhood trauma and psychosis, considering epidemiological, case-control, and prospective studies. [13] This study found that the evidence that childhood trauma confers a risk of adult psychosis is highly consistent, with children who have experienced trauma (sexual abuse, physical abuse, loss of a parent or bullying) being approximately three times more likely to become psychotic than non-traumatised children there was a dose-response effect (the most severely traumatised children were even more likely to become psychotic) suggesting that the effect is causal. This finding, and other findings suggesting that there are many social risk factors for severe mental illness, has led to Bentall's current interest in public mental health.

Misconduct and unethical behaviour

It’s worth noting how serious the Journal of Psychosomatic Research considered the misconduct they identified by Relton and others. From the Results section of the paper:

We found the presentation by Dr. Relton disturbing on multiple grounds. This admission of unethical behavior calls her scientific integrity into question. The premise for her actions rests on an errant assumption widespread among clinicians, based on anecdotal experience, that one possesses an ultimate knowledge of what works and doesn’t work without the need for rigorous study. The history of medicine, unfortunately, has been littered by countless treatments that practitioners believed in and dispensed, only to be later found not beneficial or even harmful [4]. This underscores the importance of rigorous study for treatments where equipoise exists in the scientific community, as it arguably did for the use of homeopathy for chronic fatigue syndrome. Dr. Relton likely did not hold that equipoise herself, but if she had ethical concerns about the study, the appropriate action would have been to not participate in it. Instead, she purports to have enlisted colleagues to deliberately and systematically undermine the study.

In watching the presentation, the purpose of this admission seemed to be to discount the results of a rigorous but essentially negative study in the context of promoting her own ideas related to trial design. While we cannot know for certain that her motivation was to discount the results of this study, what she said clearly seeks to undermine the credibility of a trial whose results challenged her firmly held but untested beliefs about the benefit of a treatment that she had high allegiance to. Regardless of her intent or what actually happened during the trial, Dr. Relton’s presentation is ipso facto evidence of either an admitted prior ethical breach or is itself an ethical breach for the following reasons. Either she indeed undermined an ambitious effort to study of the efficacy of homeopathy for chronic fatigue syndrome, negating the work of all other investigators, study staff, and participants involved in the study as well as the investment of the public, or she is conducting a late and inappropriate attack on the study’s credibility. Her presentation certainly warrants formal censure from the scientific community, and this paper may contribute to that. Despite this clear indictment, after discussing and considering the complaint of Mr. Henness for several months, we ultimately decided not to retract the paper.

They decided not to retract the paper but instead use it for ethical reflection. However, they concluded I had highlighted “undisputable evidence of scientific misconduct” by the homeopaths concerned:

When is lack of scientific integrity a reason for retracting a paper? A case study

Objective: The journal received a request to retract a paper reporting the results of a triple-blind randomized placebo-controlled trial. The present and immediate past editors expand on the journal’s decision not to retract this paper in spite of undisputable evidence of scientific misconduct on behalf of one of the investigators.

Methods: The editors present an ethical reflection on the request to retract this randomized clinical trial with consideration of relevant guidelines from the committee on Publication Ethics (COPE) and the International Committee of Medical Journal Editors (ICMJE) applied to the unique contextual issues of this case.

Results: In this case, scientific misconduct by a blinded provider of a homeopathy intervention attempted to undermine the study blind. As part of the study, the integrity of the study blind was assessed. Neither participants nor homeopaths were able to identify whether the participant was assigned to homeopathic medicine or placebo. Central to the decision not to retract the paper was the fact that the rigorous scientific design provided evidence that the outcome of the study was not affected by the misconduct. The misconduct itself was thought to be insufficient reason to retract the paper.

Conclusion: Retracting a paper of which the outcome is still valid was in itself considered unethical, as it takes away the opportunity to benefit from its results, rendering the whole study useless. In such cases, scientific misconduct is better handled through other professional channels.


The Ig superfamily adhesion molecule Sdk localises specifically at cell vertices at the level of AJs in Drosophila epithelia

We identified Sdk as a marker of tAJs in early embryos in a screen of the Cambridge Protein Trap Insertion (CPTI) collection of yellow fluorescent protein (YFP) traps [19]. The three independently generated CPTI protein traps in sdk, all located at the N-terminus of the protein (S1A and S1B Fig) [44], showed the same vertex-specific localisation (S1C Fig), so we used one of them, sdk-YFP CPTI000337 , for further characterisation (shortened as Sdk-YFP below). We surveyed other epithelia to ask whether Sdk also localised at apical vertices there. In the large majority of epithelia, Sdk-YFP localises at vertices at the level of AJs as in the early embryo [19] (Fig 1B and 1C and S1 Table and S2 Fig). For example, Sdk is at tAJs in the amnioserosa, a squamous epithelium in embryos in the larval wing disc, a pseudostratified epithelium and in the early adult follicular cells, a cuboidal epithelium. Vertex localisation of Sdk is found both in immature epithelia without septate junctions (early embryo, amnioserosa, and follicular layer) and in mature epithelia with septate junctions (S1 Table). This suggests that the localisation of Sdk at tAJs is independent of the presence of tricellular septate junctions (tSJs). There are a few notable exceptions to the tricellular localisation of Sdk in epithelia: in third instar salivary glands and in the follicular epithelium after stage 7, Sdk is all around the membrane (S1 Table and S2 Fig), whereas during GBE, Sdk-YFP appears planar-polarised at bicellular contacts in addition to tricellular localisation (see below and Fig 1B and S1F Fig). Also, in the adult midgut, Sdk is not detected, consistent with the midgut’s atypical apicobasal polarity [45] (S2F Fig and S1 Table). From this survey of many epithelia, we conclude that Sdk is a resident protein of tAJs in Drosophila epithelia, the first of its kind.

Next, we used Structured Illumination Microscopy (SIM) to examine Sdk localisation in fixed early embryos at a resolution higher than conventional confocal microscopy (see Materials and Methods). With this higher resolution (about 100 nm in XY and 125 nm in Z), the Sdk-YFP signal resolves as a string-like object at vertices (Fig 1D–1G). The Sdk-YFP strings are seen at all stages and in all regions of the early embryo, in epithelia that are remodelling such as the ventrolateral ectoderm (Fig 1D, 1E and 1G), and in more inactive epithelia such as the head ectoderm (Fig 1F). This suggests that the localisation of Sdk-YFP in strings or plaques is a general feature of the epithelium. The strings are continuous at vertices, and co-staining with E-Cadherin (Fig 1D and 1E) or Atypical protein kinase C (aPKC), a marker of the apical domain (S1D Fig), shows that Sdk strings extend a little beyond the E-Cadherin belt both apically and basally (S1E Fig). Sdk has a very long extracellular domain (>2,000 aa) and is tagged with YFP at the end of this domain (S1B Fig). The length of the strings, around 2 μm, suggests the formation of an assembly of Sdk proteins containing YFP in the extracellular space at apical vertices. Because the YFP tag is at the end of the extracellular domain of Sdk, a terminal fragment could be forming the strings alone. To rule this out, we co-stained Sdk-YFP–expressing embryos with an antibody raised against the intracellular domain of Sdk (S1B Fig)[26]. The same string-like localisation was observed, showing that the strings are likely made of the whole of the Sdk protein (S1F and S1G Fig).

The presence of string-like structures suggests that Sdk proteins form specialised assemblies at vertices. Because vertebrate Sdk proteins are known to bind homophilically, we asked how many cells expressing Sdk are required for Sdk vertex localisation. Because of the lack of cell divisions in the ectoderm at gastrulation, mosaic analysis cannot be performed using the yeast site-specific recombination system FRT/FLP [46], so we generated mosaics in the follicular cell layer of female ovaries. X chromosomes bearing either FRT (recombinase recognition site), nuclear localisation signal-red fluorescent protein (nls-RFP), and sdk-YFP or FRT and the mutation sdk Δ15 [26] were constructed and mosaics produced using heat shock (hs)-Flp (recombinase under hs control) (see Materials and Methods). Heat shocks were timed to generate clones in the follicular epithelium, and tricellular vertices with one, two, or three mutant cells were examined for Sdk-YFP fluorescence (Fig 1H and 1I). We find that tricellular vertices with three mutant sdk cells do not have Sdk-YFP signal at vertices, showing that there is no perdurance of the protein in the mutant clones. Whereas vertices contributed by one mutant and two wild-type cells are positive for Sdk-YFP, we find that vertices contributed by two mutant cells and one wild-type are not (Fig 1H and 1I). This indicates that two cells contributing Sdk are sufficient for localising Sdk at tricellular vertices.

Sdk localisation changes when junctions are remodelled during axis extension

As mentioned above, Sdk-YFP appears planar-polarised during convergence and extension of the Drosophila germband at gastrulation (Fig 1B and S1F Fig). During this morphogenetic movement, vertices are remodelled during polarised cell intercalation, opening the possibility that the planar polarisation of Sdk was linked to this remodelling. To test this, we made movies of embryos labelled with Sdk-YFP and E-Cadherin-mCherry (to label the AJs). We consistently observed a different behaviour of Sdk at shortening versus elongating junctions during T1 transitions (Fig 2A and 2B). At some point during junction shortening, Sdk-YFP loses its sharp punctate localisation at vertices and apparently becomes distributed all along the shortening junction (time points 40 to 120 seconds in example shown in Fig 2A) until it sharpens again into a single punctum at the four-cell intermediate (time points 140 to 160 seconds, Fig 2A). In contrast, when the new junction begins to grow, the single Sdk-YFP punctum appears to immediately split into two sharp puncta flanking the elongating junction (time points 160 to 180 seconds, Fig 2A).

(A,B) Sdk localisation during a T1 transition imaged over 20 minutes in live embryos labelled with Sdk-YFP and DE-Cad-mCherry KI . Time is indicated in seconds since the start of the intercalation event. Each image is a maximum intensity projection over 3 z-slices spanning 1.5 μm. This movie is representative of behaviour found in all of n = 9 complete intercalation events, n = 8 junction shrinkages, and n = 6 junction growths. (B) Cartoon illustrating the behaviour of Sdk-YFP shown in A. (C–H) Analysis of Sdk-YFP string localisation at shortening and elongating junctions by super-resolution SIM. Embryos are fixed and stained for GFP and E-Cad. Scale bars = 1 μm. (C–E) Representative SIM super-resolution images of DV-oriented junctions at late stage 6 (C) and at stage 7 (D) and of an AP-oriented junction at stage 8 (E). Orientation is within 20 o of AP or DV axis. For each example, the string classification used in F is shown. (F) Quantification of string morphologies based on 3D reconstructions in stage 7 and stage 8 embryos. Morphologies where divided into three classes: vertical, planar, and step-like (stage 7: total n = 40, step = 19, planar = 13, vertical = 8 stage 8: total n = 47, step = 15, planar = 11, vertical = 21). Statistical significance calculated by chi-squared test. (G) Quantification of string lengths at shrinking versus growing junctions (defined by their orientation within 20 o of AP or DV embryonic axis, respectively shrinking: n = 44, growing: n = 21). Statistical significance calculated by Mann–Whitney test. (H) Quantification of string lengths at stage 7 versus stage 8 (stage 7: n = 42, stage 8: n = 49). Statistical significance calculated by Mann–Whitney test. Data for graphs F–H can be found at AP, anteroposterior DE-Cad, DE-Cadherin DV, dorsoventral GFP, green fluorescent protein Sdk, Sidekick SIM, Structured Illumination Microscopy YFP, yellow fluorescent protein.

We examined further this differential localisation of Sdk in our super-resolution data. In non-intercalating cells, Sdk-YFP strings tend to be aligned parallel to the apicobasal axis, adopting a ‘vertical’ configuration (see, for example, in stage 9 embryos, Fig 1E and 1F). In contrast, in intercalating cells, Sdk-YFP strings tend to be more planar: some strings are completely planar (‘planar’ configuration), whereas others are vertical, then planar (‘step’ configuration) (see examples in Fig 2C–2E). Using 3D reconstruction of the super-resolution data (see Materials and Methods), we systematically classified the configurations of strings in stage 7 and stage 8 embryos. At stage 7, when cells are intercalating actively, 80% of the strings have a ‘planar or ‘step’ configuration, this proportion decreasing to about 50% at stage 8, when cell intercalation starts to decrease (Fig 2F). 3D reconstruction allows one to measure the length of Sdk-YFP strings accurately, and we find that strings are longer at dorsoventral (DV)-oriented junctions compared to AP-oriented junctions (Fig 2G). Strings are also longer at stage 7 compared to stage 8 (Fig 2H). Together, these quantifications support the notion that Sdk-YFP strings become longer and more planar during shrinkage of DV-oriented junctions, whereas the strings are shorter and more vertical when AP-oriented junctions are growing. We also infer that the planar string patterns of DV-oriented junctions in our super-resolution data are likely to correspond to the continuous distribution of Sdk-YFP observed at shortening junctions in the live data at lower resolution (Fig 2A and 2B).

Based on the above live and fixed data, we propose that Sdk localisation during a T1 transition follows the sequence illustrated in Fig 2C–2E. Two possible explanations are possible for this change in Sdk localisation: Sdk either moves to bicellular contacts at shortening junctions or, alternatively, remains at tricellular contacts, but cells form protrusions extending towards the shortening junctions (S1H and S1I Fig, and see Discussion). Because the increase in resolution with SIM is moderate, we were unable to distinguish between these two possibilities. We conclude that the localisation of Sdk is different between shortening and growing junctions, suggesting that Sdk may play a role in polarised cell intercalation.

In intercalating cells, rosette centres contain separable tricellular vertices marked by Sdk

Next, we examined the localisation of Sdk during rosette formation (Fig 3). Rosettes are observed in the germband when several contiguous DV-oriented junctions shorten together, merging into an apparently single vertex [35]. It is not known whether each rosette centre really represents a single junctional vertex structure or not. To address this, we made movies of Sdk-YFP embryos also labelled with growth-associated protein 43 (Gap43)-Cherry to label all cell membranes. Live imaging suggests that rosette centres are in fact made of several puncta of Sdk-YFP, which move relative to each other in a dynamic fashion during rosette cell rearrangements (Fig 3A). The sequence of shortening and elongation of very short junctions between Sdk-YFP puncta suggested that tricellular vertices marked by Sdk might remain separated in rosette centres. This has implications for how we understand polarised cell intercalation because this suggests that rosettes might resolve through successive T1 transitions.

(A) Sdk localisation during rosette formation imaged over 15 minutes in live embryos labelled with Sdk-YFP and Gap43-mCherry. Time is indicated in seconds since start of the intercalation rosette. Each image is a maximum intensity projection over 3 z-slices spanning 1.5 μm. Movie is representative of behaviour found in all of n = 6 full rosette-like intercalation events. Scale bars = 5 μm. Close-up images of the rosette centre are shown in yellow boxes for the Sdk-YFP channel. Cartoon below illustrates the dynamics of the Sdk-YFP puncta seen in the movie. (B) Sdk-YFP string localisation at a rosette centre involving six cells, imaged by super-resolution SIM. The image is from a stage 8 embryo fixed and stained for GFP and DE-Cad. Maximum projection over 12 slices = 1.5 μm. Close-ups of the rosette centre with different projections are shown in yellow boxes to demonstrate that three distinct strings can be resolved. Cartoon shown to interpret images. Scale bars for the main SIM panels are 5 μm and for the close-ups 1 μm. DE-Cad, DE-Cadherin Gap43, growth-associated protein 43 GFP, green fluorescent protein Sdk, Sidekick SIM, Structured Illumination Microscopy tAJ, tricellular adherens junction YFP, yellow fluorescent protein.

To test this, we examined rosette centres in our super-resolution data (Fig 3B). Maximal projections (for example, see the XY projection in Fig 3B) are unable to reveal whether Sdk-YFP strings are continuous or separate. Thus, as above, we used 3D reconstruction to follow the path of the strings at rosette centres (Materials and Methods). This analysis revealed that several strings are always observed in the middle of rosettes and they are not in contact with each other (see reconstruction in Fig 3B). This indicates that rosette centres are composed of separable tricellular apical vertices marked by Sdk. We also examined the configuration of rosette centres below the AJs, marking the whole membrane (for example, using concavalin A, S3 Fig). As observed by others [47], we find that the cell connectivity can change significantly within the apical-most 3 μm. In the case shown in S3 Fig, whereas three distinct Sdk strings are present in the apical-most portion of the rosette centre, a single punctum of Sdk is found 2 μm below, where the connectivity of the cells differs. We conclude that Sdk strings corresponds to the apical-most junctional conformation and that during junctional exchange at the level of AJs, single junctional vertex intermediates are not usually formed between more than four cells. This suggests that intercalation events forming rosettes occur through separable T1-like events.

Loss of sdk causes abnormal cell shapes during GBE

To investigate a possible role of Sdk in GBE, we made movies of embryos homozygous for the sdk MB5054 null mutant [26] and carrying E-cadherin-GFP [48] to label apical cell contours. Because sdk loss-of-function mutations are viable [23], these embryos are devoid of both maternal and zygotic contributions for Sdk. Consistent with a role in GBE, we observe an abnormal cell shape phenotype in sdk null mutants during GBE, with distinct differences in the geometry and topology of the apical planar cellular network compared to the wild type in particular, many cells have a less regular and more elongated polygonal shape (Fig 4A and 4B).

(A,B) Movie frame of ventral ectoderm at 30 mins into GBE from representative WT (A) and sdk (B) movies, labelled with E-cadherin-GFP. (C,D) Measurement of cell shape anisotropy and orientation (see also S5A–S5C Fig). Cell shape anisotropy is calculated as the log ratio of the principal axes of best-fit ellipses to tracked cell contours. An isotropic cell shape (a circle) will have a log-ratio value of 0 and a very elongated cell a value of over 1. Cell orientation is given by the cosine of the angular difference between the ellipse’s major axis and the DV embryonic axis. Negative values indicate cells that are elongated in the AP axis, positive values in the DV axis. Cell shape anisotropy and orientation measures are then multiplied together to give a composite measure (termed ‘axial shape elongation’) of how elongated cells are in the orientation of the embryonic axes (Materials and Methods). (D) Axial shape elongation measure (y-axis) for the first 30 mins of GBE (x-axis) for WT and sdk embryos. In this graph and hereafter, the ribbon’s width indicates the within-embryo confidence interval, and the dark grey shading indicates a difference (p < 0.05) (Materials and Methods). (E–F) Measures of AP and DV cell lengths in WT and sdk embryos (see also S5D and S5E Fig). Cell shape ellipses are projected onto AP and DV axes to derive a measure of cell length in each axis. (G,H) Evolution of AP-oriented and DV-oriented cell–cell interface lengths (y-axis) as a function of time in GBE (x-axis) (see also S5F and S5I Fig). Tracked cell–cell interfaces are classified as AP- or DV-oriented according to their orientation relative to the embryo axes. Data for graphs D–H can be found at AP, anteroposterior DV, dorsoventral GBE, germband extension GFP, green fluorescent protein Sdk, Sidekick WT, wild type.

To describe these phenotypes quantitatively, we acquired five wild-type and five sdk movies of the ventral side of embryos over the course of GBE. We then segmented the cell contours, tracked cell trajectories through time, and synchronised movies within and between each genotype group, as previously [41, 43, 49] (Materials and Methods). To allow comparisons between wild-type and sdk embryos, we defined the beginning of GBE (time 0) using a given threshold in the rate of tissue extension (see Materials and Methods, S4A and S4B Fig and S1 and S2 Movies). The total number of ventral ectoderm cells in view and analysed increased from start to end of GBE, from about 500 cells to above 2,000 cells for both wild-type and sdk embryos (S4C and S4D Fig).

We first analysed the anisotropy in cell shapes and their orientation in the course of GBE (Fig 4C). The eccentricity of ellipses fitted to the apical cell shapes is used as a measure of cell shape anisotropy (see Materials and Methods). The orientation of the ellipse’s major axis relative to the embryonic axes gives the cell orientation. At the beginning of GBE, ectodermal cells are elongated in DV because the tissue is being pulled ventrally by mesoderm invagination [41, 43] (Fig 4D and S5A–S5C Fig). In wild-type embryos, the cells then become progressively isotropic as the ectoderm extends, as we showed previously [41]. In contrast, in sdk embryos, the cell shapes become briefly isotropic and then become anisotropic again, this time along the AP axis (Fig 4D and S5B and S5C Fig). This anisotropy in the AP direction could be due to cells being longer in AP, thinner in DV, or both. To distinguish between these possibilities, we measured the cell lengths along AP or DV (Fig 4E and 4F and S5D and S5E Fig). We found that both cell lengths are significantly different in sdk mutants compared to the wild type, with cells being shorter in DV and also, but more moderately, longer in AP.

Because the AP and DV cell lengths described above are a projection of ellipses fitted to the cell shapes, we also looked directly at the length of the cell–cell interfaces in the course of GBE (Fig 4G and 4H and S5F–S5I Fig). We classified cell interfaces as being AP- or DV-oriented based on their angles with the embryonic axes (see Materials and Methods). Mirroring the cell length results, we find AP-oriented interfaces get a little longer and the DV-oriented interfaces shorter in sdk compared to wild-type embryos in the course of GBE.

Together, our cell shape quantifications demonstrate that overall, sdk cells become shorter in DV and, to a lesser extent, longer in AP in the course of GBE, consistent with our initial qualitative observation of many more elongated cells in sdk mutants. We hypothesised that this cell shape phenotype could be a consequence of a defect in polarised cell intercalation, which would, in turn, modify cell topologies.

Tears in the apical cortex persist in sdk mutants during polarised cell intercalation

A possibility is that Sdk is required for normal polarised cell intercalation through mediating homophilic adhesion or anchoring the actomyosin cytoskeleton when cells rearrange. Supporting this notion, we noticed apical discontinuities in the converging and extending epithelium in sdk mutants labelled with E-Cadherin-GFP KI and Myosin II-Cherry (Fig 5 and S6 Fig). These apical tears or gaps are lined by the actomyosin cortex and usually associated with a depletion in E-Cadherin (Fig 5A and 5C). In an example in which E-Cadherin was still present around a small circular gap, following the signal more basally showed that the gap had closed already 1 μm below the AJs (Fig 5B). These apical tears seemed particularly prevalent and larger at the centres of rosettes, forming oval structures bordered by Myosin II, as shown in super-resolution images in Fig 5C.

(A) A single z-frame at the level of AJs showing a gap or tear in the cortex at a presumed rosette centre in an sdk mutant embryo. Left panel shows merge between DE-Cad-GFP and Sqh-mCherry (shortened as MyoII-mCherry) signals, right panel, DE-Cad-GFP channel only. In the bottom panel, the different cells have been coloured to highlight the apical gap in the middle. (B) Single z-frames at different positions along the apicobasal axis of an apical gap in an sdk mutant embryo (from movie shown in S6A Fig) at the level of AJs (0 μm) and 1 and 2 μm below. The gap present at the level of AJs is closed in the planes basal to the AJs. Bottom panels show colourised cells, highlighting the apical gap in the apical-most z-slice. (C) Fixed and stained sdk mutant embryo against DE-Cad and GFP (to reveal MyoII-GFP), imaged by SIM super-resolution microscopy at stage 7. Each image is a maximum intensity projection over 3 μm at the level of AJs in the ventral ectoderm. Regions bounded by yellow and blue lines show discontinuities in E-Cad signal, indicating holes in apical adhesion, and are shown below as close-ups. (D–F) Apical gaps quantifications in WT and sdk mutant movies as shown in A, B. (D) Quantification of the number of gaps found at the level of AJs, normalised to a given area (2,500 μm 2 ) of the ectoderm. One to two regions (embryo sides) were quantified per movie: WT, n = 7, from seven embryos sdk mutant n = 10, from eight embryos Mann–Whitney, p-value = 0.0018. (E) Quantification of how long apical gaps persist in the tissue. (F) Quantification of how long apical gaps persist as a function of the number of cells present at the gap’s border. We detected gaps where four to seven cells and more meet. For both E and F, the number of gaps quantified was n = 92 for WT and n = 115 for sdk mutant. In D, Mann–Whitney, p-value < 0.0001. Data for graphs D–F can be found at AJ, adherens junction DE-Cad, DE-Cadherin GFP, green fluorescent protein MyoII, Myosin II Sdk, Sidekick SIM, Structured Illumination Microscopy Sqh, spaghetti-squash WT, wild type.

Next, we systematically looked for these cortical discontinuities, comparing movies of sdk and wild-type embryos labelled with E-Cadherin-GFP KI and Myosin II-Cherry. Unexpectedly, we also found apical gaps in wild-type embryos in the course of GBE, which to our knowledge has never been reported. However, compared to the wild type, apical gaps are more numerous in sdk mutants and also persist in the epithelium for much longer (Fig 5D and 5E and S6A Fig). In both the wild type and sdk mutants, the apical gaps appear associated with groups of cells undergoing polarised rearrangements. Gaps forming where four cells meet are likely to represent single T1 transitions and are the most transient (Fig 5F). Apical holes forming where five cells or more meet are likely to correspond to rosette centres. We find that the more cells that are present, increasing from four to seven cells and above, the more persistent the apical gaps are in sdk mutants (Fig 5F). Whereas in the wild type, apical gaps are more transient and rapidly resolved, in sdk mutants, the apical gaps persist, sometimes for the whole duration of GBE. In the latter case, we find that these then resolve when cell division starts in the epithelium at the end of GBE (S6B Fig). Based on these results, we conclude that the presence of Sdk facilitates the resolution of cortical discontinuities at the level of AJs during cell rearrangements in an extending tissue, this requirement being more acute when cells are rearranging as rosettes (involving more than four cells).

Sdk is required for normal polarised cell intercalation during axis extension

We have shown previously that the tissue deformation of GBE is caused by a combination of cell intercalation and cell shape changes and that cell shape changes can compensate for cell intercalation defects [41, 43, 50]. We measure the relative contributions of these two cell behaviours by considering each cell and a corona of neighbours to calculate the different strain rates [50] (see Materials and Methods) (Fig 6A). Briefly, the relative movement of cell centroids in small patches of tissue is used to calculate the tissue strain rates within each patch, individual cell shapes are approximated to ellipses to measure the cell shape strain rate finally, the difference between tissue strain rates and cell shape strain rates gives a continuous measure of the strain rate due to cell intercalation (Fig 6A). Strain rates are then projected along the AP embryonic axis to calculate the rate of deformation in the direction of tissue extension. We find that the rate of tissue extension along AP is decreased in sdk mutants (Fig 6B and S7A and S7D Fig). Moreover, we find a decrease in cell intercalation contributing to extension (Fig 6D and S7C and S7F Fig), which is compensated to some extent by cell shape changes (Fig 6C and S7B and S7E Fig). This suggests that the relative contributions of cell intercalation and cell shape change to total tissue extension are altered in sdk mutants. Supporting this, we find that the proportion of the cell intercalation strain rate contributing to AP extension is indeed lower in sdk compared to the wild type (S8A Fig).

(A) Graphical illustration of our measures of tissue and cell shape SRs (Materials and Methods). The cell intercalation SR is derived from these two measures. (B–D) Average SRs in the direction of extension (along AP) for five WT (blue) and five sdk mutant (red) embryos for the first 30 minutes of GBE. Total tissue SR (B), cell shape SR (C), and cell intercalation SR (D). Units are in pp per minute. (E) Diagram of a T1 transition leading to a loss of neighbours 1 and 3 along AP and a gain of neighbours 2 and 4 along DV. (F,G) Analysis of the number and orientation of T1 transitions averaged for five WT (blue) and five sdk mutant (red) embryos for the first 30 minutes of GBE (see also S8D and S8E Fig). (F) Orientation of all T1 transitions relative to the AP embryonic axis. Orientation is given by the angle of cell interfaces relative to AP, 5 minutes before a T1 swap (Kolmogorov–Smirnov test, N = 1,786 for WT and 1,890 for sdk mutant, D = 0.1115, p < 0.0001). (G) Cumulative proportion of T1 swaps contributing to axis extension in AP (called productive T1 swaps see Materials and Methods) for the first 30 minutes of GBE and expressed as a pp of DV-oriented interfaces tracked at each time point. Data for graphs B–G can be found at AP, anteroposterior DV, dorsoventral GBE, germband extension pp, proportion Sdk, Sidekick SR, strain rate WT, wild type.

The above measure of cell intercalation is a measure of the continuous movement of cells relative to each other. We wanted to confirm the cell intercalation defect using a discrete measure. For this, we detected the number of neighbour exchanges, called T1 swaps, occurring for any group of four cells in the tissue. In this method, a T1 swap is defined by a loss of neighbour caused by cell–cell contact shortening, followed by the growth of a new cell–cell contact and a gain of neighbour (Fig 6E and S8B and S8C Fig Materials and Methods) [49, 51]. While the total number of T1 swaps is only moderately decreased in sdk mutants compared to the wild type (S8D and S8E Fig), their orientation is abnormal. First, we find that in sdk mutants, the T1 swaps are not as well-oriented relative to the embryonic axes compared to the wild type (Fig 6F and S8H Fig) (note, however, that the orientation of the shortening junctions relative to the growing junctions is unchanged in sdk mutants compared to the wild type S8F and S8G Fig). Second, we can quantify the contribution of T1 swaps to AP extension (defined as ‘productive’ T1 swaps see Materials and Methods), and those are robustly decreased in sdk versus the wild type (Fig 6G and S8I Fig). We also looked at the geometric arrangement of cells during junctional shortening. We find that the angle between the shortening junctions and the centroid–centroid line between intercalating cells is larger in sdk mutants compared to the wild type (S8J Fig), suggesting that cell intercalation patterns are less regular. In conclusion, both the continuous and discrete methods we employed above indicate that the polarised cell intercalation contributing to AP tissue extension is decreased in sdk mutants.

Modelling the sdk mutant phenotype

One hypothesis to explain a defect in polarised cell intercalation in sdk mutants is that the Sdk homophilic adhesion molecule facilitates the transition between shortening and elongating junctions at apical tricellular vertices. The Sdk adhesion molecule might provide a specialised adhesion system at vertices (perhaps bridging the intercellular vertex gap more effectively than the shorter E-Cadherin) or a specialised anchorage of the actomyosin cytoskeleton. To test this hypothesis, we extended our previously published vertex model of an intercalating tissue [49]. Vertex models traditionally implement cell rearrangement by imposing an instantaneous T1 swap on all small edges (below a threshold length) (Fig 7A). In order to model a putative phenotype in cell rearrangement, we developed a new framework in which the vertices of a shrinking edge temporarily merge to form higher-order vertices, which may resolve with some probability per unit time (Fig 7B and 7C and S1 Text). Vertices can be of rank 4 (four cells around a vertex Fig 7B), to model a single T1 transition, or of rank 5 and above (five cells or more around a vertex Fig 7C), to model rosettes. In addition to this change, we imposed periodic boundary conditions on the tissue, reducing artefacts that arise with a free boundary. Finally, we added a posterior pulling force to simulate the effects of the invaginating midgut [42, 43] (S9A Fig and S1 Text).

(A) Cell rearrangement (T1 transition) is usually implemented in vertex models as follows: edges with length below a threshold are removed, and a new edge is created between previously non-neighbouring cells. (B–C) Alternative implementation of cell rearrangement used in this paper. (B) Shortening junctions merge to form a four-way vertex and a protorosette, which has a probability, p4, of resolving at every time step. (C) Formation of rosettes around higher-order vertices (formed of five cells, as shown here, or more) due to the shortening of junctions connected to four-way vertices. Edges connected to the shortening junction are merged into the existing vertex, which now has a probability, p5+, of resolving at every time step. (D) Initial configuration for each simulation. The tissue is a tiling of 14 × 20 regular hexagons with periodic boundary conditions. All cells are bestowed one of four stripe identities, <S1, S2, S2, S4>, representing identities within parasegments, as in [49] (see S9A Fig for an illustration). (E) Wild-type simulation of Drosophila GBE in the presence of a posterior pulling force, implementing cell rearrangements as outlined in B and C with and . Model parameters used were (Λ, Γ) = (0.05, 0.04). (F) Tissue strain rate in the A–P (extension) direction for wild-type tissues with parameters used in E. Solid line and shading represent mean and 95% confidence intervals from five independent simulations. (G) Simulation of GBE in a tissue in which T1 swaps are less likely to resolve, with and . All other parameters are kept equivalent to wild-type simulation. (H) Tissue strain rate in the A–P (extension) direction for tissues with parameters used in G compared to strain rate of wild-type tissue in E. Solid line and shading represent mean and 95% confidence intervals from five independent simulations. (I) Simulation of GBE in a tissue in which T1 swaps are less likely to resolve, with and , as in G, and additionally in which the shear modulus of the tissue (in the absence of actomyosin cables) has been reduced by setting Γ = 0.01. All other parameters are kept equivalent to wild-type simulation. (J) Tissue strain rate in the A–P (extension) direction for tissues with parameters used in I compared to strain rate of wild-type tissue in E. Solid line and shading represent mean and 95% confidence intervals from five independent simulations. As shown in key, for B, C, E, G, and I, cell colouring indicates the vertex rank of a cell, defined as the maximum number of cells sharing one of its vertices (note that darker blue is for rosettes of rank 5 and above). Further details about models and simulations can be found in S1 Text. Data for graphs F, H, and J can be found at A–P, anterior–posterior GBE, germband extension Sdk, Sidekick.

We used this new mathematical framework to model the cell intercalation defect we report for sdk mutants. First, we took into account the striking relationship between the number of cells involved in an intercalation event and the persistence of apical gaps or tears in sdk mutants (Fig 5F). Gaps forming between four cells, presumably as a consequence of a single T1 swap, take longer to close in sdk mutants compared to the wild type, but they eventually resolve. In contrast, gaps present at the centres of rosettes involving five cells or more often persist until the end of imaging (Fig 5F and S6B Fig). Second, our evidence indicates that a rosette centre is in fact made up of several separable Sdk string-like structures (Fig 3). Together, these results suggest that i) single T1 swaps might be delayed in sdk mutants and ii) this delay might increase when cells intercalate as rosettes because it requires the resolution of several T1 swaps in short succession. Our data in Fig 5F support the idea that rosettes accumulate and get stuck in sdk mutants (see also S1 and S2 Movies). To test whether intercalation might also be delayed in single T1s, we measured the resolution phase of T1 swaps in sdk mutants versus the wild type (S9B Fig). Note that in this analysis, only the successful T1 swaps are quantified, so this automatically excludes any stuck rosettes. We find a 1-minute delay between the wild type and sdk mutants (S9C Fig), supporting the assumptions of the model.

To model such a delay in sdk mutants, we imposed a lower probability of successful resolution of T1 swaps per unit time than in the wild type (Fig 7B and 7C). We distinguished isolated T1 swaps involving only four cells (Fig 7B) from linked T1 swaps in rosettes involving five or more cells by lowering the resolution probability further for rosettes (vertices connected to five or more cells) (Fig 7C). Rosettes appear in both sdk mutant and WT simulations, but as expected from the probabilities imposed, they get stuck in the sdk simulation, while they are resolved quickly in the wild-type simulation (Fig 7D, 7E and 7G) (S3 and S4 Movies). This leads to the topology of the cellular network being different in the sdk simulation in ways reminiscent of the sdk phenotype (see Fig 4A and 4B). We next compared the tissue strain rates in these simulations (Fig 7F and 7H). The strain rates are initially very similar, suggesting that the posterior pulling force is the main contribution to the initial AP strain rate. Then, the imposed delay in rearrangements leads to a reduced strain rate as the strength of the pull declines and DV-oriented junctions have shortened to the point of rearrangement (Fig 7H). When compared with the biological data, however, these strain rate patterns did not mimic the initial decrease in tissue strain rate in sdk mutants compared to the wild type (Fig 6B).

To capture the difference in the initial tissue-level strain rate, we made one additional modification to the model. We reasoned that the loss of Sdk may lower intercellular adhesion globally in the tissue since vertebrate Sdk homologues are known homophilic adhesion molecules [27, 28]. To model this, we perturbed the mechanical properties of the sdk tissue (in a manner equivalent to a decrease in the shear modulus of a mechanically homogenous tissue see S1 Text). The posterior pull and the properties of the neighbouring tissues were left unchanged. In the new sdk simulation, the initial extension strain rate is now lower compared to the wild type, more accurately reproducing the biological data (Fig 7J compare with Fig 6B). Interestingly, more rosettes form in this simulation (Fig 7I and S5 Movie). Because of the probability imposed for rosette resolution, this results in more stuck rosettes in this simulation, which, in turn, has an increased impact on the topology of the cellular network (Fig 7I). In conclusion, our mathematical modelling supports the notion that together, a change in the elastic mechanical properties of the cells and a delay in cell rearrangement could explain the sdk mutant phenotypes we observe in vivo.



To determine whether global reduction of CD68 (cluster of differentiation) macrophages impacts the development of experimental pulmonary arterial hypertension (PAH) and whether this reduction affects the balance of pro- and anti-inflammatory macrophages within the lung. Additionally, to determine whether there is evidence of an altered macrophage polarization in patients with PAH.

Approach and Results:

Macrophage reduction was induced in mice via doxycycline-induced CD68-driven cytotoxic diphtheria toxin A chain expression (macrophage low [MacLow] mice). Chimeric mice were generated using bone marrow transplant. Mice were phenotyped for PAH by echocardiography and closed chest cardiac catheterization. Murine macrophage phenotyping was performed on lungs, bone marrow–derived macrophages, and alveolar macrophages using immunohistochemical and flow cytometry. Monocyte-derived macrophages were isolated from PAH patients and healthy volunteers and polarization capacity assessed morphologically and by flow cytometry. After 6 weeks of macrophage depletion, male but not female MacLow mice developed PAH. Chimeric mice demonstrated a requirement for both MacLow bone marrow and MacLow recipient mice to cause PAH. Immunohistochemical analysis of lung sections demonstrated imbalance in M1/M2 ratio in male MacLow mice only, suggesting that this imbalance may drive the PAH phenotype. M1/M2 imbalance was also seen in male MacLow bone marrow–derived macrophages and PAH patient monocyte-derived macrophages following stimulation with doxycycline and IL (interleukin)-4, respectively. Furthermore, MacLow-derived alveolar macrophages showed characteristic differences in terms of their polarization and expression of diphtheria toxin A chain following stimulation with doxycycline.


These data further highlight a sex imbalance in PAH and further implicate immune cells into this paradigm. Targeting imbalance of macrophage population may offer a future therapeutic option.

This course can help you spot aliens

MPhys Physics and Astrophysics is a full degree course where Physics is taught in the context of Astronomy. At University of Sheffield , UK, the degree entails learning advanced topics in Maths and Physics as well as learning analytic problem-solving skills, numerical and computing skills and data analysis, writing and communication, research skills and practical instrumentation, says Simon Goodwin , professor of Theoretical Astrophysics, Department of Physics and Astronomy at the university.

“Under this course, we use quantum mechanics , nuclear and thermal physics to understand how stars form and evolve. Determine the properties of stars and galaxies and even look for aliens using spectroscopy, and how general relativity explains the structure of the universe,” says Goodwin.

“Along with traditional subject knowledge in advanced Maths and Physics, students are imparted knowledge about computational models and big data skills that are needed to understand what is happening and communicating these findings in talks and reports need an understanding of communication, which is also taught to the students,” he adds.

In order to offer industry exposure, students work on a project with industries where they are expected to provide solutions to real problems. The students can also take part in at least one industry-led project, and can complete a course on 'Physics in an Enterprise Culture'.

Industry exposure

“The university has been collaborating with new industrial partners, recent development includes designing particle sensors to use for nuclear security, calibrating medical fluid flow sensors, and modelling the local public transport network,” adds the professor.

The students have assisted in building new instruments and telescopes to look for short-lived events in space a group of students recently developed a new statistical analysis tool to trace the binary stars while two students recently discovered the effectiveness ofyoung stars in disrupting young planetary systems.

Students can take part in industrial projects designing control systems, write software to drive equipment, or model and analyse data for a variety of different applications

Real-life applications

According to Goodwin, MPhys Physics and Astrophysics is a useful degree with a wide variety of applications. Many students choose to pursue PhDs in astronomy/astrophysics as well as in various engineering. There are many non-research careers open to students with the data analysis/numerical/problem solving skills that come from the degree.

“We have had students recently follow careers in engineering, patent law, data analysis (both public and private sector), computing, finance, accountancy any job that requires analytic and numerical expertise is open to a Physics and Astrophysics graduate,” he says.

What would classify those findings of the University of Sheffield experiment as aliens? - Biology

Purpose This study aims to propose guidelines for the joint use of partial least squares structur. more Purpose
This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field.

This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct.

This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions.

The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.

6. Climate change and creative opportunities associated with planting design

Urban planners, urban designers, architects, landscape architecture and professionals are interested in sustainability by climate change challenges. However, climate change also helps to free up conventional thinking by making people come to terms with the idea that the future will not be the same as the past.

The climate change literature suggests that, in the years to come, a number of the species incorporated into public planting programs will no longer be sustainable. In the specific context of North America, for example, there has been a wide implementation of naturalistic design with the use of predominantly native species, whereas, in the context of Europe, both non-native and native species have been used, depending on various cultural and ecological factors (Hitchmough and Dunnett, 2004a, 2004b). Importantly, some species utilized in the planting design initiatives of the UK, at present, are not well aligned to their current locations from a climatic point of view. To ensure that sustainable urban landscapes can be achieved, it is likely to be essential to incorporate a broader range of native and non-native species that are increasingly well fitted to the changing climate. In Europe, there is a diversity of views surrounding the incorporation of exotic plant species in urban-designed landscapes, although the debate is less skewed to a natives-only policy than is the case in the USA (Hitchmough, 2011). These arguments are based on concerns about potential invasiveness and the advantages of using native rather than non-native species, to support animal biodiversity to the highest possible degree. Nativeness is a concept first outlined by John Henslow in 1783. Henslow was a botanist who had considered the idea in line with the terms “native” and “alien,” as applied in the common law in the late 1840s, to define plants that were British rather than artefacts from elsewhere. His interests were mainly practical rather than philosophical. In the 100 years that followed, some different professionals, including zoologists and botanists, have detailed and examined the various species introduced with and without awareness. British ecologist Charles Elton wrote the Ecology of Invasions by Animals and Plants in 1958 at a time when there was a general lack of agreement about the overall appropriateness of intervention upon the introduction of alien species. It was sometime later – notably in the 1990s – that the concept of invasion biology became recognized as its distinctive discipline and non-native species began to be seen as doing harm. In more recent years, there have been signs of more thoughtful standpoints on non-native species starting to emerge in the ecological research literature, although not in the USA, as the evidence begins to accrue for non-native species also playing valuable roles regarding delivering ecosystem services. In many countries, exotic species and their introduction have notably increased the number of species in a region, both those that are now established parts of the biota and the much more extensive range or species which are transient and are on the brink of extinction. This is not to say that there are not some evil aliens, but preferably that the balance sheet is more complicated than initially thought.

In this vein, it is clear that utilizing a combination of both non-native and native species in urban public environments enables a more significant impact regarding color (Hitchmough and Woudstra, 1999). The native species tend to be accepted as the most suitable plants for use when aiming at achieving the most sustainable planting, as they are often highly pre-adapted to local climates with the assumption that they have been sourced from comparable biomass as in urban settings. They are particularly useful because of their high capacity in many cases for self-reproduction, without this being regarded (as it is with non-native species) as a biological invasion. Nonetheless, exotic plants have also been recognized as part of many civilizations and designed landscapes for long period of time, particularly in Europe and Asia, meaning it is essential to consider the views of people regarding what they perceive to be suitable (Kendle and Rose, 2000). Accordingly, there are some valuable opportunities centered on improving the aesthetic character of urban landscapes through ensuring the careful selection of non-native plant communities. It is typical for plant fitness, in an urban setting, to be significantly influenced by the habitat in which the species have evolved, where the species will be seen to be a better fit when the habitat is well aligned with the environmental conditions apparent at the location of cultivation. Such plants will be the most sustainable (Hitchmough, 2011). With this in mind, the view might be taken that local native species typically will be more fitted to many planting sites than species from further afield (Schmitz and Simberloff, 1997 Gilbert and Anderson, 1998 Parker et al., 1999). Although the view seems to be relatively accurate when taking into account climatic factors (Davis, 1989 Hitchmough, 2011), some species prove to be well fitted even when those environments are comparatively different to their present habitats. Sometimes, this is because of their past biogeographical distribution and history. In other cases, species that are well fitted are those that occur in habitats for a long time, which because of local factors such as altitude and soil moisture, etc., closely resemble the conditions at the planting site. Importantly, although fitness is acknowledged to be an important factor, it remains that there has been little attention directed toward herbaceous species.


Temperature limitation of photosynthesis and growth

Photosynthesis under light-limited and light-saturated conditions and the rate of leaf growth were all greater in the C4 than the C3 subspecies at high temperatures ( Figs 1, 3), supporting our first hypothesis. However, the C4 advantage was maintained at significantly lower temperatures than anticipated, with a crossover temperature range beginning at ≤17 °C for φ CO 2 ⁠ , and ≤15 °C for Asat and LER, and no evidence of higher values in the C3 plants at any temperature ( Figs 1, 3). On the basis of these data, a photosynthetic advantage for the C3 over the C4 subspecies of A. semialata between 10 °C and 15 °C cannot be excluded. However, given the occurrence of photoinhibition in this temperature range, it seems likely that the distribution of the C3 subspecies to higher latitudes and altitudes than its C4 sister is driven by alternative mechanisms.

Values of φ CO 2 at 30 °C were 0.070–0.078 mol mol −1 for the C4 and 0.040–0.048 mol mol −1 for the C3 subspecies (95% confidence intervals). The C3 values are slightly lower, and the C4 marginally higher than ranges previously measured for C3 and C4 grasses (C3 range 0.052–0.056 mol mol −1 , C4 range 0.060–0.069 mol mol −1 Ehleringer and Pearcy, 1983). Therefore, although the temperature dependency of quantum yield in each subspecies is typical of C3 and C4 photosynthesis, a difference in absolute values results in a lower crossover temperature than expected ( Ehleringer et al., 1997). Errors in φ CO 2 of ±10% may have been introduced by the inaccurate estimation of leaf area. However, there is no reason to expect this factor to introduce a major systematic bias, and it is therefore suggested that the low crossover temperature may be a real effect rather than an artefact.

In theory, the C4 photosynthetic pathway has the potential to achieve higher light-saturated CO2-fixation rates than the C3 type at any temperature, given equal investment in Rubisco and insensitivity of the photosynthetic apparatus to chilling injury ( Long, 1999). In practice though, C4 plants typically accumulate significantly less Rubisco than C3 species ( Long, 1999), and the calculated values of Amax (for the C4) and Vc,max (for the C3) at 25 °C in our experiment ( Table 1) are consistent with a 3-fold lower capacity of the enzyme in the C4 than C3 subspecies. In the absence of other limitations, the amount of Rubisco in C4 leaves can become a major constraint on CO2-fixation at low temperatures ( Pittermann and Sage, 2000). However, the observed decreases in Vp,max and Amax for the C4 plants between 25 °C and 15 °C ( Table 1) are also consistent with a lower capacity for PEP regeneration and a decline in PEP carboxylase (PEPC) activity at sub-optimal temperatures. Both in vitro ( Chinthapalli et al., 2003) and in vivo ( Chen et al., 1994) studies of PEPC show temperature-dependency of enzyme activity at 15 °C (reviewed by Sage and Kubien, 2007), but cold-lability of enzymes in the PEP regeneration pathway typically occurs only at temperatures <10 °C (reviewed by Long, 1999).

The limitation of Asat by sub-optimal temperatures was significantly less pronounced in the C3 than C4 subspecies ( Fig. 1), and is consistent with the opposing responses of Rubisco specificity and photosynthetic capacity (catalytic rate of Rubisco and RubP regeneration rate) to temperature ( Long, 1991 Bernacchi et al., 2001). The ≤15 °C crossover temperature range for Asat may therefore be explained in terms of four interacting factors: (i) the lower activity of Rubisco in C4 than C3 leaves (ii) the decreased activity of C4 cycle enzymes at 15 °C relative to 25 °C (iii) elimination of photorespiration in the C4 leaves, which makes photosynthesis directly proportional to photosynthetic capacity and (iv) the increase in apparent (in vivo) Rubisco specificity, which offsets decreases in its catalytic rate and the RubP regeneration rate of C3 leaves at low temperatures.

Light- and chilling-mediated photodamage

An 8 h exposure of leaves to a temperature of 15 °C under high PPFD led to inhibition of photosynthetic CO2-assimilation and slow-relaxing (>15 min) quenching of chlorophyll fluorescence in both subspecies ( Fig. 4), suggesting damage to photosystem 2 reaction centres. This photodamage was exacerbated by acute exposure to freezing and high PPFD over a period of hours ( Fig. 4) or chronic exposure to chilling in the range 5–15 °C and lower PPFD over a period of days ( Fig. 6). In the latter case, major decreases in qP and Fv/Fm were associated with declining NPQ ( Fig. 6), implying uncoupling of photochemistry from the thylakoid proton gradient and/or temperature-mediated decreases in xanthophyll cycling (Bilger and Björkman, 1991).

The observation of chilling-induced photodamage in both C3 and C4 leaves conflicts with our a priori expectation of higher thresholds for chilling injury in the C4 subspecies. Rather, it supports the alternative hypothesis that susceptibility to chilling injury in A. semialata is related to its tropical ancestry. A tropical origin for A. semialata is suggested by the overlap in Equatorial Africa of the distributions for all five species in the Alloteropsis genus ( Ellis, 1981). This idea that chilling injury in C4 plants is a consequence of their ancestry, rather than an inherent weakness in their photosynthetic pathway, is supported by observations of chilling tolerance in C4 species which originate from high latitude and altitude habitats (e.g. Miscanthus, Beale et al., 1996 Bouteloua, Pittermann and Sage, 2000 and Muhlenbergia, Kubien and Sage, 2004).

Freezing injury and cold acclimation

Freezing caused leaf injury in unhardened plants of both subspecies under illuminated conditions, but a 2-week chilling pretreatment allowed cold acclimation of the C3 subspecies ( Fig. 5). The C4 plants failed to develop the same protection, and suffered the same levels of injury as unhardened plants ( Fig. 5). This result demonstrates that, although the C4 subspecies is not more vulnerable to chilling injury, it is significantly more sensitive to freezing. Since both C3 and C4 subspecies have diverged recently from a (sub)tropical common ancestor, differential freezing sensitivity cannot be attributed to the different climatic histories of C4 and C3 evolutionary lineages. Instead two alternative, but not mutually exclusive, interpretations of these results are offered.

First, they could represent ecotypic differentiation between the subspecies that is not directly related to their photosynthetic pathway, but correlated to their differing geographical distributions. The C4 subspecies of A. semialata occupies a range stretching from tropical Australia, through the Asian and African wet tropics, and into seasonally arid subtropical regions of southern Africa. The C3 subspecies co-occurs with its C4 sister in subtropical South Africa, but extends to higher altitudes and further polewards ( Ellis, 1981). Although these contrasting distributions are likely to be linked to the difference in photosynthetic pathway, they may also generate ecotypic differences between the subspecies that are not. In the regions of southern Africa where A. semialata is found, winter is characterized by freezing events, but is typically dry (New et al., 1999), and early spring is therefore the main fire season in this region ( Carmona-Moreno et al., 2005). A failure to protect leaves from frost in the C4 subspecies could therefore represent an adaptive response to other features of the environment, such as high soil water deficit or fire risk ( Ripley et al., 2008). In contrast, the geographic range of the C3 subspecies is characterized by a lower fire risk ( Giglio et al., 2006), and an increased probability of winter rainfall ( Vogel et al., 1978), so that leaf retention during winter could bring important fitness benefits.

Secondly, cold acclimation mechanisms could be less effective in C4 than C3 plants, or more costly in metabolic terms, perhaps reflecting a fundamentally lower phenotypic plasticity in C4 species ( Sage and McKown, 2006). However, field observations during the growing season and manipulation experiments have demonstrated that the leaves of some C4 grass species can resist freezing injury at temperatures of < –10 °C ( Sage and Sage, 2002 Márquez et al., 2006 Sage and Kubien, 2007), and may develop frost protection during exposure to chilling ( Rowley et al., 1975 Rowley, 1976 Stair et al., 1998). Vulnerability to freezing is therefore not inevitable in C4 grasses however, most fail to develop significant cold acclimation, and are highly sensitive to freezing ( Rowley et al., 1975 Rowley, 1976 Ivory and Whiteman, 1978). These data suggest that this mechanism may be ecologically relevant, and should be considered when explaining C4 grass distributions in relation to temperature.

Two of our experiments suggested that freezing injury in the C4 subspecies of A. semialata may be mediated by plant hydraulics. Preliminary data from experiment 3 indicated that a frozen soil exacerbated damage to C4 photosynthesis, suggesting that ice in the root system interrupted the water supply to thawing leaves. Experiment 5 demonstrated a radically different pattern of moisture release in C3 and C4 leaves with the potential for significant cellular desiccation by extracellular ice in the C4 that is avoided by the C3, and a lower ΨL,sat in the C4 leaves ( Table 2). In combination, these data suggest that freezing injury may be caused by ‘frost drought’, and are consistent with scanning electron microscopy (SEM) observations in other freezing-sensitive or unhardened species ( Ashworth and Pearce, 2002 Ball et al., 2004). However, the mechanism that differentiates C3 and C4 species, and its links (if any) to the photosynthetic pathway, remains unknown. One possibility is that suberization of the bundle sheath causes hydraulic isolation from the mesophyll, and makes bundle sheath cells particularly vulnerable to ice damage ( Ashworth and Pearce, 2002), but this idea requires further investigation.

What would classify those findings of the University of Sheffield experiment as aliens? - Biology

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