Trichomonas vaginalis, the etiologic agent of the most common non-viral sexually transmitted infection worldwide. With an estimated annual prevalence of 276 million new cases, mixed infections with different parasite strains are expected. Although it is known that parasites interact with their host to enhance their own survival and transmission, evidence of mixed infections call into question the extent to which unicellular parasites communicate with each other. Here, we demonstrated that different T. vaginalis strains can communicate through the formation of cytoneme-like membranous cell connections. We showed that cytonemes formation of an adherent parasite strain (CDC1132) is affected in the presence of a different strain (G3 or B7RC2). Our findings provide evidence that this effect is contact-independent and that extracellular vesicles (EVs) are responsible, at least in part, of the communication among strains. We found that EVs isolated from G3, B7RC2, and CDC1132 strains contain a highly distinct repertoire of proteins, some of them involved in signaling and communication, among other functions. Finally, we showed that parasite adherence to host cells is affected by communication between strains as binding of adherent T. vaginalis CDC1132 strain to prostate cells is significantly higher in the presence of G3 or B7RC2 strains. We also observed that a poorly adherent parasite strain (G3) adheres more strongly to prostate cells in the presence of an adherent strain. The study of signaling, sensing, and cell communication in parasitic organisms will enhance our understanding of the basic biological characteristics of parasites, which may have important consequences in pathogenesis. Trichomonas vaginalis is the causative agent of trichomoniasis, the most common non-viral sexually transmitted infection worldwide, with an estimated 276 million new cases annually , soluble secreted factors, membrane protrusions, and direct contact between cells are all different forms of cell communication . Plasma T. vaginalis, the analysis of EVs, both exosomes and MVs, has become a very exciting field in the study of parasite: host interaction, as it has been shown that the formation of EVs increase in the presence of host cells and modulate parasite adherence  is affected in the presence of a different strain (G3 or B7RC2). Furthermore, we have shown that this effect on cytoneme formation is contact-independent and that EVs are responsible, at least in part, of the communication between strains. To explain the differential response in cytoneme formation due to the presence of EVs isolated from different strains, we analyzed the EVs’ protein content using mass spectrometry and demonstrated that highly specific protein cargo was detected in EVs isolated from different strains. Finally, we showed that parasite adherence to host cells is affected by this communication as binding of adherent T. vaginalis CDC1132 strain to prostate cells is significantly higher in the presence of G3 or B7RC2 strains. Importantly, we observed that in the presence of an adherent strain, a poorly adherent parasite strain (G3) adheres more strongly to prostate cells, suggesting that interaction between isolates with distinct phenotypic characteristics may have significant clinical repercussions. The study of signaling, sensing, and cell communication in parasitic organisms will surely enhance our understanding of the basic biological characteristics of parasites and reveal new potential clinical outcomes.Here, we demonstrated that different T. vaginalis by fluorescent and live-cell microscopy revealed the presence of filamentous structures extending from the surface of some cells  and co-incubated with varying amount of unstained G3, B7RC2, and CDC1132 strains . Then, co-incubated parasites were stained with WGA, and the formation of filopodia and cytonemes in CDC1132 strain was evaluated by fluorescent microscopy . Although we could not observe a clear increase in filopodia formation in CDC1132 strain upon exposure to G3 or B7RC2, the formation of cytonemes in CDC1132 is clearly affected by the presence of a different strain compared to the exposure to CDC1132 itself  strains using a 1 µm porous membrane that prevent direct contact between parasites. This system allows for secreted factors and small EVs to pass between the two cell populations but keeps the parasites (10–15 µm in diameter) loaded in the inserts from contacting the parasites inoculated in the bottom. To measure paracrine signal activation, the number of CDC1132 parasites in the bottom of the well containing cytonemes and filopodia was measured by WGA staining after 1 hr incubation . As can T. vaginalis  and a poorly adherent strain (G3) could affect the attachment of G3 to BPH1 cells. Importantly, co-incubation of CDC1132 strain with G3 parasites resulted in a twofold increase in G3 attachment to BPH1 . As can be observed in Mycoplasma was not detected in the supernatant of any of the analyzed strains. The findings indicate that EVs released by isolates with distinct phenotypic characteristics can affect the behavior of recipient parasite, which could have significant clinical implications in the context of mixed infections.To test whether the parasite: parasite communication could affect the behavior of a poorly adherent strain, we expanded our attachment assay and examined whether communication between a highly adherent  to BPH1 . In conc to BPH1 , these d species  and that species . To ruleTrypanosoma brucei parasites modulates the quorum sensing-mediated differentiation of into the tsetse fly transmissible short-stumpy developmental form in the mammalian bloodstream , B7RC2 , and CDC1132   with 10 U/ml penicillin and cultured at 37°C/5% CO2.SA 2008)  were culSA 2008) . G418 (1SA 2008) . ParasitSA 2008) , tested Mycoplasma in the supernatant of G3, B7RC2, and CDC1132 cultured parasites, a PCR reaction using TransDetect PCR Mycoplasma Detection Kit was performed. To this end, 10 ml of parasite culture with a density of 1.5×106 parasites/ml was centrifuged at 2000× g for 5 min. Then, 40 µl of the cell supernatant was transferred to a PCR tube, incubated at 95°C for 10 min in a thermocycler, and 2 µl was used to proceed with the PCR. Control template provided in the kit and ultrapure water were used as positive and negative control for the reaction, respectively.To evaluate the presence of Triticum vulgaris conjugated with FITC(fluorescein isothiocyanate) (Sigma). To this end, parasites were incubated 1:100 WGA/PBS dilution at 37°C during 1 hr, washed three times with PBS solution and mounted using Fluoromont Aqueous Mounting Medium (Sigma). Fluorescent parasites were visualized using a Zeiss Axio Observer 7 (Zeiss) microscope.Parasites were incubated at 37°C for 1 hr on glass coverslips as previously described . Parasit4, dehydrated in ethanol, and critical point-dried with liquid CO2. The dried cells were coated with gold–palladium to a thickness of 15 nm and then observed with a Jeol JSM-5600 SEM, operating at 15 kV. Some images were colored using Adobe Photoshop software (Adobe USA), version 24.0.1.Cells were washed with PBS solution and fixed in 2.5% glutaraldehyde in 0.1 M cacodylate buffer, pH 7.2. The cells were then post-fixed for 15 min in 1% OsOClumps formation was analyzed in parasites grown in regular TYM media under anaerobic conditions at different concentration. A clump was defined as the size corresponding to an aggrupation of at least five parasites. Quantification of clumps in thirty 20× magnification fields was performed using a Nikon TSM (Nikon) microscope. Three independent experiments were performed.CDC1132 parasites were labeled using CellTracker Red CMTPX Dye (ThermoFisher). Then, labeled CDC1132 parasites were co-incubated for 1 hr at 37°C with unlabeled G3, B7RC2, and CDC1132 parasites at different cell ratios . Parasites attached to coverslips were fixed and labeled with WGA as described previously. The number of CDC1132 parasites containing filopodia and cytonemes, visualized as the ones with double labeling (red and green), was analyzed using a Zeiss Axio Observer 7 (Zeiss) microscope. Three independent experiments, each of them in duplicates, were performed.Wild-type parasites from G3, B7RC2, and CDC1132 strains as well as G3 parasites transfected with VPS32 or EpNEO were co-cultured using polyester transwell-24 inserts . CDC1132 parasites were loaded at the bottom of each well and exposed to wild-type G3, B7RC2, CDC1132 parasites, or G3 parasites transfected with VPS32 and EpNEO placed into the transwell inserts (parasite ratio 1:2 bottom: transwell) for 1 hr at 37°C. Then, CDC1132 parasites attached to coverslips were labeled with WGA, and the formation of filopodia and cytonemes was analyzed using a Zeiss Axio Observer 7 (Zeiss) microscope.6 cells/ml) from G3, B7RC2, and CDC1132 strains by incubating the parasites for 4 hr at 37°C in TYM medium without serum. Then, conditioned medium was harvested and centrifuged at 3000 rpm for 10 min to remove cell debris. The media was filtered through a 0.8 μm filter, and the sample was pelleted by centrifugation at 100,000× g for 90 min to obtain an EVs enriched fraction (a mixture of MVs and exosomes). The pellet was resuspended in 200 μl cold PBS + 1× cOmplete ULTRA Tablets, Mini, EASYpack Protease Inhibitor Cocktail (Sigma). EVs isolation from G3, B7RC2, and CDC1132 was performed in parallel. Three independent experiments were performed.As recommended, the term is referred to all sub-populations of EVs including exosome and MVs . As prevTotal protein concentration was determined colorimetrically . The standard curve was prepared using Bovine Serum Albumin (Promega). Absorbance was measured at 595 nm with a spectrophotometer.To carry out the incubation step, 10 and 20 μg of EVs or the same volume of the PBS solution (control) were incubated for 1 hr at 37°C with CDC1132 parasites in a 24-well plate. As control, EVs (10 μg) were inactivated by autoclaving following a previously described protocol . After iEVs enriched samples were resuspended in a minimal volume of digestion buffer . Resuspended proteins were reduced and alkylated by the sequential addition of 5 mM tris(2-carboxyethyl) phosphine and 10 mM iodoacetamide as described previously. The samples were then digested by Lys-C (Princeton Separations) and trypsin proteases Promega; . First, T. vaginalis to host cell monolayers was carried out (2. Parasites (CDC1132 or G3 strain) were labeled with Cell Tracker Blue CMAC , added to confluent BPH-1 cells (1:3 parasite: host cell ratio), and exposed to different parasites strains loaded in the inserts in a 1:2 bottom: transwell ratio. Plates were then incubated together at 37°C and 5% CO2 for 60 min. Coverslips were subsequently washed in PBS solution, fixed with 4% paraformaldehyde and mounted on slides with Fluoromont Aqueous Mounting Medium (Sigma). Quantification of fluorescent parasites attached to host cells was measured using a Zeiss Axio Observer 7 microscope. Thirty 10× magnification fields were analyzed per coverslip. All experiments were performed three independent times in duplicates.A modified version of an in vitro assay to quantify the binding of ried out . BrieflySpecific statistical considerations and the tests used are described separately for each subsection. GraphPad Prism for Windows version 8.00 was used for graphics. Data are shown as mean ± SD. Statistical significance was established at p<0.05, and for statistical analyses, the InfoStat software  version  This study presents novel information and describes fundamental findings in various fields of biology. It is of interest to a wide audience, including parasitology, cell-to-cell communication, and the roles of extracellular vesicles. The manuscript presents compelling evidence that will contribute to the advancement of our understanding of how parasitic extracellular vesicles (and cytoneme structures) are formed and participate in intra-species communication. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Dominique Soldati-Favre as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: PierLuigi Fiori (Reviewer #2).Thank you for submitting your article "Role of cytoneme-like structures and extracellular vesicles in Trichomonas vaginalis parasite: parasite communication" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Please evaluate the feasibility of the suggestions listed by reviewer 1, since they require substantial experimentation. If there are difficulties to follow these suggestions, please provide an explanation of the reasons why your conclusions can be supported without these new assays;2) Please explore, on the basis of the current literature, how EV and cytoneme formation are related;3) Please modify the text in accordance with the comments by the three reviewers.Reviewer #1 (Recommendations for the authors):The most important points and of greatest concern is in relation to experiments with cellular communication.Figure 4 is very interesting and has strong evidence of cell communication being a specific strain. depending on extracellular vesicles. Experiments A and B are related but may have similar ways of presenting the results to have better deductions. The effect of cellular communication through Transwell is represented by the percentage of parasites that acquire filopodia or cytonemes. In B the experiment is performed by adding extracellular vesicles purified previously and the formation of filopodias and cytonemas is represented by the increased presence by times x. This does not allow us to easily deduce the direct participation of extracellular vesicles in the effect on A or soluble factors. The experiments should be related to each other, where in B The authors could add the supernatant of the interactions between parasites removed from extracellular vesicles to see if there is a synergistic effect on the formation of filopodios or cytonemas or just the effect is by extracellular vesicles. Another control that could be used is the use of inactivated extracellular vesicles. Another important curiosity is because the effect is not dose-dependent, Was there saturation of the effect? In this way, it would be important for the authors to investigate the minimum concentration capable of inducing a significant formation of filopodias or cytonemas and if it responds to a dose dependence or is just a sign of activation by the recognition of specific components of the vesicles involved.Another suggestion in the experiment in B is to mark the vesicles with fluorochromes and see what happens to them in the parasite. Are they endocited? Are part of the filopodias or cytonemes? The authors could follow the course of the EVS during the parasite-parasite The authors could follow the course of the EVS during the parasite-parasite communication.The experiments in Figure 6 on the phenotypic effects of the combination of strains are very interesting. The authors could make a set of experiments with a total conditioned medium, with extracellular vesicles of the interaction, and with conditioned medium removed from extracellular vesicles to determine if the effects are due to extracellular vesicles or soluble factors released.Reviewer #2 (Recommendations for the authors):The manuscript describes the fundamental role of protozoan-to-protozoan and protozoan-to-host cell communication in modulating the pathogenicity of Trichomonas vaginalis.In fact, the manuscript has many strengths and only a few weaknesses I would ask the authors to clarify just a few points (see below)1. Many clinical isolates of T.vaginalis establish symbiosis with some Mycoplasma species particularly (M.hominis and M.girerdii). Have the T.vaginalis strains used for the experiments been screened for the presence of the bacteria? It is known that the reference strain G3 is mycoplasma-free, but it is necessary to know whether the other isolates possess bacterial endosymbionts.2. The presence of endosymbionts influences the pathophysiology of T.vaginalis. In particular, both the adhesiveness and cytopathogenicity of the protozoan are modulated. These aspects should be presented in the discussion, especially the adhesive capabilities of human cells3. The manuscript does not report a study on the expression of genes involved with adhesiveness or modulation of cytoskeleton architecture in different experimental conditions. This can be considered a limitation of the work and should be reported in the discussion.Reviewer #3 (Recommendations for the authors):(1) Page-4 (Introduction): It appears that the literature cited about parasite EVs is not updated. Authors might consider revisiting and citing those EV-related papers that are more relevant to the current study.(2) Figure 5. Efforts should be undertaken to connect the data from the proteomic analysis with biological processes such as cytoneme formation, EV secretion, and pathogenicity caused by Trichomonas.(3) The authors have carried out solid experiments that produced very interesting results. Unfortunately, they were not discussed in detail in the Results section. The authors might consider re-writing some sections of the results.(4) How EV secretion and cytoneme formation are related? Is it possible that exosomal vesicles released by T. vaginalis change the membrane structures that lead to cytoneme formation?(5) Figure 2. The clumps formations by CDC 1132 are shown. It is necessary to show the clumps formation by poorly adherent cells just for comparison. Also, fold changes of what (panel C)?(6) Some Figure legends  are short. They should be elaborated accordingly.(7) In several places in the text, there are some grammatical mistakes, which require the authors' attention. Reviewer #1 (Recommendations for the authors):The most important points and of greatest concern is in relation to experiments with cellular communication.Figure 4 is very interesting and has strong evidence of cell communication being a specific strain. depending on extracellular vesicles. Experiments A and B are related but may have similar ways of presenting the results to have better deductions. The effect of cellular communication through Transwell is represented by the percentage of parasites that acquire filopodia or cytonemes. In B the experiment is performed by adding extracellular vesicles purified previously and the formation of filopodias and cytonemas is represented by the increased presence by times x. This does not allow us to easily deduce the direct participation of extracellular vesicles in the effect on A or soluble factors. The experiments should be related to each other, where in B The authors could add the supernatant of the interactions between parasites removed from extracellular vesicles to see if there is a synergistic effect on the formation of filopodios or cytonemas or just the effect is by extracellular vesicles.T. vaginalis is highly inefficient, requiring 250-500 ml of conditioned media for isolation. Furthermore, the standard protocol calls for the use of 10 µg of isolated EVs for experiments, yet the amount of EVs in the transwell assay remains unknown. These differences in the isolation protocol render it impossible to accurately assess the role of EVs in the transwell experiment when using a 24-well plate . We propose here that EVs play a crucial role in the communication between parasites, although we concur with the reviewer that there may be other secreted factors involved in this process.We appreciate the reviewer's comment and share his/her opinion. However, it is not feasible to compare experiment 4A with former experiment 4B to fully comprehend the involvement of EVs in the observed effect due to several methodological limitations that prevent direct comparison. The isolation of EVs in To establish the involvement of EVs in parasite: parasite communication within a comparable experimental setup, we have conducted a transwell assay using parasites that overexpress the VPS32 protein. In a previous publication , we have demonstrated that parasites with an overexpression of VPS32, a component of the ESCRT III complex, release a larger quantity of EVs compared to the EpNEO control (parasites transfected with an empty plasmid). As shown in the new Figure 4B, the number of CDC1132 parasites located at the bottom of the well containing cytonemes and filopodia is significantly higher when VPS32 overexpressing parasites (G3 strain) are loaded into the inserts compared to the incubation with G3 parasites transfected with EpNEO or G3 wild type parasites . Given that VPS32-overexpressing parasites release more EVs than EpNEO parasites, these findings provide compelling evidence that the released EVs play a key role in the formation of membrane protrusions in recipient parasites. Notably, this experimental setup is linked to Figure 4A, enabling the contribution of EVs to be evaluated.Another control that could be used is the use of inactivated extracellular vesicles.We thank the reviewer for this excellent suggestion. We have followed his/her suggestion and analyzed the effect using inactivated EVs as control. These results have now been included as Figure 4c.Another important curiosity is because the effect is not dose-dependent, Was there saturation of the effect? In this way, it would be important for the authors to investigate the minimum concentration capable of inducing a significant formation of filopodias or cytonemas and if it responds to a dose dependence or is just a sign of activation by the recognition of specific components of the vesicles involved.As previously documented in our publications, the optimal effect of EVs on T. vaginalis adherence was found to be at a dose of 9-10 μg . In agreement with this observation, no further enhancement in the formation of cytonemes and filopodia was observed when the concentration of EVs was increased to 20 μg, as illustrated in Supplementary Figure S3. Consequently, all experiments were conducted using 10 μg of EVs.Another suggestion in the experiment in B is to mark the vesicles with fluorochromes and see what happens to them in the parasite. Are they endocited? Are part of the filopodias or cytonemes? The authors could follow the course of the EVS during the parasite-parasite The authors could follow the course of the EVS during the parasite-parasite communication.Regrettably, we have not yet delved into the mechanism by which EVs impact cytoneme formation in recipient parasites. We acknowledge that this is a fascinating avenue of analysis, and future research will be dedicated to elucidating this mechanism. Nevertheless, we recognize that this is beyond the scope of the present study.The experiments in Figure 6 on the phenotypic effects of the combination of strains are very interesting. The authors could make a set of experiments with a total conditioned medium, with extracellular vesicles of the interaction, and with conditioned medium removed from extracellular vesicles to determine if the effects are due to extracellular vesicles or soluble factors released.This would be a very interesting experiment. However, as previously stated, there are methodological limitations that make this experiment impossible.Reviewer #2 (Recommendations for the authors):The manuscript describes the fundamental role of protozoan-to-protozoan and protozoan-to-host cell communication in modulating the pathogenicity of Trichomonas vaginalis.In fact, the manuscript has many strengths and only a few weaknesses I would ask the authors to clarify just a few points (see below)1. Many clinical isolates of T.vaginalis establish symbiosis with some Mycoplasma species particularly (M.hominis and M.girerdii). Have the T.vaginalis strains used for the experiments been screened for the presence of the bacteria? It is known that the reference strain G3 is mycoplasma-free, but it is necessary to know whether the other isolates possess bacterial endosymbionts.We thank the reviewer for this suggestion. To rule out the possibility that any observed effects were caused by the presence of Mycoplasma in the conditioned media rather than by the EVs, the supernatants of all three strains was PCR amplified using the TransDetect PCR Mycoplasma Detection Kit (TransGen). These results have now been included as Sup.Figure 42. The presence of endosymbionts influences the pathophysiology of T.vaginalis. In particular, both the adhesiveness and cytopathogenicity of the protozoan are modulated. These aspects should be presented in the discussion, especially the adhesive capabilities of human cellsDone.3. The manuscript does not report a study on the expression of genes involved with adhesiveness or modulation of cytoskeleton architecture in different experimental conditions. This can be considered a limitation of the work and should be reported in the discussion.Done.Reviewer #3 (Recommendations for the authors):(1) Page-4 (Introduction): It appears that the literature cited about parasite EVs is not updated. Authors might consider revisiting and citing those EV-related papers that are more relevant to the current study.New references have been included.(2) Figure 5. Efforts should be undertaken to connect the data from the proteomic analysis with biological processes such as cytoneme formation, EV secretion, and pathogenicity caused by Trichomonas.Done.(3) The authors have carried out solid experiments that produced very interesting results. Unfortunately, they were not discussed in detail in the Results section. The authors might consider re-writing some sections of the results.Done.(4) How EV secretion and cytoneme formation are related? Is it possible that exosomal vesicles released by T. vaginalis change the membrane structures that lead to cytoneme formation?Regrettably, we have not yet delved into the mechanism by which EVs impact cytoneme formation in recipient parasites. We acknowledge that this is a fascinating avenue of analysis, and future research will be dedicated to elucidating this mechanism. Nevertheless, we recognize that this is beyond the scope of the present study.(5) Figure 2. The clumps formations by CDC 1132 are shown. It is necessary to show the clumps formation by poorly adherent cells just for comparison. Also, fold changes of what (panel C)?Poorly adherent strains usually do not form clumps, they grow separately. Panel C legend was clarified.(6) Some Figure legends  are short. They should be elaborated accordingly.Figure legends have been modified accordingly(7) In several places in the text, there are some grammatical mistakes, which require the authors' attention.Done.


Routine examination after cataract surgery, including a refraction test 4 to 6 weeks after surgery, is mandatory in most hospitals. In recent years, there has been growing interest in exploring alternative approaches to postoperative follow-up in cataract surgery patients due to the increasing number of cataract surgeries being performed, the limited availability of health care resources, and the need to optimize the use of health care services.We aim to compare postoperative visual outcomes after a day 0 examination in patients with 2 follow‑ups, one on day 7 and other on day 30, and patients with a single ophthalmic follow‑up between days 25 to 30.A prospective, quantitative, experimental control study will be carried out in Reiyukai Eiko Masunaga Eye Hospital, located in Banepa, Kavrepalanchok, Nepal. All patients undergoing cataract surgery meeting the inclusion and exclusion criteria irrespective of the type of surgery  will be included in the study. The patients will be randomly assigned to 1 of 2 groups. Patients in group 1 will be examined on day 1, day 7, and day 30, whereas patients in group 2 will be examined on day 1 and once between days 25 to 30. The minimum clinically important difference (MCID) in our study will be set according to the improvement in the Snellen visual acuity chart.The study is expected to be completed within 6 to 8 months from the start of the project. Data analysis and report writing will be carried out in a 2-month period. Best-corrected visual acuity will be compared between the 2 groups to determine if the MCID is achieved. The cost-effectiveness of the new approach will also be analyzed.We aim to conclude that we can safely defer the 1-week postoperative follow-up visit in patients undergoing uncomplicated cataract surgery and that, moreover, we can reduce the patient load at the hospital and decrease patient expenses by decreasing the frequency of hospital visits.PRR1-10.2196/48616 Cataract is the leading cause of blindness and visual impairment in lower- and middle-income countries ,2. SurgeLimburg et al  investigThe general objective is to reduce follow-up visits in cataract surgery patients.The specific objectives are (1) to compare postoperative visual outcomes after a day 0 examination in patients with either 2 follow‑up visits, one on day 7 and the other on day 30, or 1 ophthalmic follow‑up visit between day 25 and day 30; (2) to assess the safety of deferring the first follow‑up visit at 1 week; and (3) to perform a cost-benefit analysis of the intervention.This will be a prospective, quantitative, experimental, noninferiority control study of health services use. Selected patients undergoing cataract surgery at Reiyukai Eiko Masunaga Eye Hospital irrespective of the type of surgery (SICS or phacoemulsification) will be included in the study.All enrolled patients will undergo an examination on the first postoperative day conducted by an ophthalmologist who is unaware of the patient’s group assignment. Subsequently, the patient will proceed to the counselor’s office, where a unique identification number will be generated and the patient will be randomized into either group 1 or group 2.In group 1, patients will be instructed to attend follow-up visits at 1 week and 1 month postoperatively. In group 2, patients will be instructed to attend a follow-up visit at 1 month postoperatively only. During the visit, the patients will receive detailed guidance on the appropriate use of medications and be educated about the signs and symptoms they should be vigilant for. The specific date of the postoperative visit will also be communicated.At each follow-up visit, the patient will directly visit the counselor’s office. Upon arrival, they will be provided with a blank sheet of paper containing the unique identification number assigned to them on the first postoperative day. Visual acuity testing will be conducted by a blinded optometrist, while slit lamp biomicroscopy will be performed by a blinded ophthalmologist. Before leaving the hospital, the patient will return the paper to the counselor, who will then enter the necessary information into the patient’s file. Patients undergoing uncomplicated cataract surgery at Reiyukai Eiko Masunaga Eye Hospital irrespective of the type of surgery (SICS or phacoemulsification) will be included in the study.Patients will be excluded if (1) there are complications during the surgery that warrant more frequent follow-up visits ; (2) the surgery is for complicated cataract, traumatic cataract, ocular hypertension, or if the patient has drug allergies; (3) the patients has a history of diseases that might affect postoperative visual outcomes, such as uveitis, glaucoma, viral keratitis, corneal scarring, hypertensive retinopathy, diabetic retinopathy, central retinal vein occlusion, branched retinal vein occlusion, or age-related macular degeneration; (4) the patient is unwilling to participate or does not provide written consent; and (5) the patient misses the follow-up visits.Sample size is calculated as follows: 2[Zα + Z1 – β] 2 × (sd2/d2) = 2 [1.96 + 1.645 ] 2 × [0.1125)2/(0.025)2] = 263 for one group, where reference mean values for postoperative visual outcomes are taken from the study conducted by Deshpande et al [2/(0.025)A proforma will be developed for data collection. A questionnaire will be prepared; counselors will ask the questions and the information will be recorded in a register. This information will be transferred to an Excel (Microsoft Corp) sheet on a daily basis.The study will be carried out by a team of investigators. The principal investigator will provide orientation for the involved staff 1 week prior to participant enrollment. The issues that might arise during the course of data collection will be resolved by mutual discussion between the investigators. The study will be supervised remotely by the Seva team.Study records that identify the patients will be kept confidential, as required by law. All records pertaining to the patients’ involvement in this research study will be stored in a locked file cabinet in the hospital. A case number will indicate the patients’ identity on these records. This information will only be accessible to the authors.We expect to determine whether we can defer the 1-week cataract surgery follow-up visit of patients to the hospital.Visual acuity measurements serve as the most important primary outcome. This includes assessing the level of visual improvement or stability achieved by patients during the 4-week follow-up period. Visual acuity outcomes can provide insights into the effectiveness of deferring follow-up visits for achieving comparable visual outcomes to the standard practice of early postoperative visits.The results of the cost-benefit analysis can be considered a secondary outcome. Conducting a cost-effectiveness analysis allows for the assessment of the economic implications and potential cost savings associated with deferring postoperative follow-up visits. This outcome provides insights into the financial feasibility and efficiency of implementing the deferred follow-up strategy.We will perform a cost-benefit analysis to compare the economic status of the intervention group and active control group. For this we have to collect financial data on direct and indirect costs. Data on direct costs will be retrieved from the hospital record system, because this is the money paid directly by patients for their treatment after the surgery is recorded, including payments for consultation, medicine, and investigation (if required) at each follow-up visit.Data on indirect costs, that is, costs incurred to the patient in the process of coming to the follow-up visit, include the following: (1) lost productivity costs , (2) transportation costs (the cost incurred while travelling), and (3) companion costs . Data on indirect costs will be obtained from interviews with the patients. We will calculate the average cost per visit, which will ultimately reveal the costs and benefits of the new approach, because the suggested new regimen will eliminate one follow-up visit.Research findings will be disseminated through the Reiyukai Eiko Masunaga Eye Hospital newsletter. Donors will be also informed. Results will be published in high-impact journals for better coverage.The Declaration of Helsinki will be adequately addressed. Ethical approval for this study has been obtained from the ethical review board of the Nepal Health Research Council (428/2022 P).Participants will be provided with detailed information about the study, including the deferral of postoperative visits, as well as potential risks, benefits, and alternatives. A written informed consent form will be provided to all participants.Study data will be collected and stored in a manner that ensures the anonymity or deidentification of participants. Personally identifiable information such as names, addresses, and social security numbers will be removed from the data set. Each participant will be assigned a unique identifier that is used instead of their personal information.More than 526 participants will be enrolled in the study, with a minimum of 263 assigned to the intervention group (deferred follow-up visits) and a minimum of 263 to the comparison group (standard practice of early postoperative follow-up visits). The sample will have diverse demographics, ensuring a representative population.P>.05).Visual acuity measurements will be obtained for both groups during the follow-up period. We will be able to statistically compute whether the deferred follow-up visit group demonstrates comparable visual acuity outcomes to the comparison group. This will be inferred if there are no statistically significant differences between the two groups in terms of visual acuity outcomes (Among the participants in the deferred follow-up visit group, the number of patients who experience postoperative complications during the 4-week follow-up period will be compared with the group with the 1-week follow up visit.We will also determine if participants in the deferred follow-up visit group report high levels of satisfaction with the extended recovery period and reduced travel burden. The number of participants who express that deferring follow-up visits provided them with convenience and comfort during the recovery phase will be mentioned.We will determine if reduction in early postoperative visits allows health care providers to allocate their time and expertise more efficiently, focusing on patients with immediate needs or complex postoperative care requirements.The proposed study will investigate the feasibility and potential benefits of deferring postoperative follow-up visits up to 4 weeks after uneventful cataract surgery in a tertiary level eye hospital in Kavrepalanchok.Deferring postoperative follow-up visits will allow patients to recover in the familiar environment of their homes, reducing the need for immediate travel and associated costs. This can have a positive impact on patient satisfaction and comfort during the recovery period. By avoiding unnecessary trips to the hospital, patients may experience reduced anxiety and stress, leading to an improved overall experience.By spacing out follow-up visits, health care providers can optimize resource allocation within the tertiary level eye hospital. This approach will allow health care personnel to allocate their time and expertise more efficiently, focusing on patients who require immediate attention or complex postoperative care. Moreover, it can potentially reduce the burden on health care facilities, as fewer appointments are scheduled during the early postoperative period.Deferring follow-up visits may also improve access to care, particularly for patients residing in remote areas. Traveling to health care facilities shortly after surgery can be challenging, especially for individuals with limited mobility or limited access to transportation. By extending the follow-up period, patients from distant locations can receive necessary care without the immediate need for travel, increasing the likelihood of their adherence to follow-up appointments.One major concern related to deferring follow-up visits is the potential delay in detecting postoperative complications. Early postoperative visits allow health care providers to closely monitor patients for any signs of infection, inflammation, or other adverse events. When extending the follow-up period, it is crucial to educate patients regarding the signs and symptoms of postoperative complications so that they can visit the hospital, if required, on time. Patients should receive comprehensive information about self-care, potential warning signs, and instructions on when to seek medical attention. This will empower patients to actively participate in their own recovery and promote a sense of responsibility for their eye health.There are potential challenges associated with deferring postoperative follow-up visits. It is important to carefully consider the selection criteria for eligible patients to ensure that only those with a low risk of complications are included in the study. Additionally, proper communication channels must be established to ensure patients have access to health care providers in case of emergency concerns or complications during the extended follow-up period.The proposed study on deferring postoperative follow-up visits up to 4 weeks after uneventful cataract surgery in a tertiary level eye hospital in Kavrepalanchok will offer several potential benefits for patients, health care providers, and health care facilities. By prioritizing patient convenience, optimizing resource use, and ensuring appropriate support and education, this approach has the potential to improve access to care and overall patient satisfaction. However, careful consideration should be given to the selection criteria and establishment of effective communication channels to address potential challenges and ensure patient safety. The findings of this study will contribute valuable insights to the delivery of eye care services and may have implications for similar settings.


This reduces the costs for hydrogen production to 1.26 €/kg by 2050. Hydrogen production switches from grey to green hydrogen, omitting the option of blue hydrogen. If electrolysis costs are modelled without dynamic learning-by-doing, then the electrolysis scale-up is significantly delayed, while total system costs are overestimated by up to 13% and the levelised cost of hydrogen is overestimated by 67%.Many studies have shown that hydrogen could play a large role in the energy transition for hard-to-electrify sectors, but previous modelling has not included the necessary features to assess its role. They have either left out important sectors of hydrogen demand, ignored the temporal variability in the system or neglected the dynamics of learning effects. We address these limitations and consider learning-by-doing for the full green hydrogen production chain with different climate targets in a detailed European sector-coupled model. Here, we show that in the next 10 years a faster scale-up of electrolysis and renewable capacities than envisaged by the EU in the REPowerEU Plan can be cost-optimal to reach the strictest +1.5 This study highlights the importance of including learning-by-doing for hydrogen production in energy models. It reveals that scaling up renewable capacities and electrolysis faster than the EU’s REPowerEU Plan can be cost-effective under strict climate targets, reducing hydrogen production costs and shifting from grey to green hydrogen. In the future, however, hydrogen produced with low carbon emissions is likely to play an increasing role in the energy mix as decarbonisation progresses with shares of up to 24% of the final energy demand by 20502. Applications could include the production of green steel3 or synthetic fuels5. There are several ways in which hydrogen can be produced: (i) from fossil fuels such as coal or natural gas, (ii) from fossil fuels in combination with CO2 capture, or (iii) via electrolysis. The production costs for hydrogen depend in the first two cases on the price of fossil fuels, and in the second case on the cost of electricity. In order to reduce dependence on fossil fuels with high gas prices and to accelerate the energy transition, the European Commission aims to boost the deployment of hydrogen electrolysis. This objective has been concretised in the REPowerEU plan6 published in May 2022, which has set a goal to produce 10 million tonnes of hydrogen with renewable electricity and to import further 10 million tonnes to the EU by 2030.Today hydrogen plays a minor role in European final energy consumption with a 2% share9 or on expert surveys10. Way et al.8 explore various cost distributions for the world’s energy system up to year 2070 for multiple technologies via a Monte Carlo approach. Through the different investment cost trajectories they cover uncertainties in the cost developments, but the path for installed capacities are exogenously defined and energy dispatch is not modelled.Hydrogen produced from water electrolysis using renewable electricity, so-called green hydrogen, is considered to be crucial to decarbonise hard-to-electrify sectors of the energy system. Since it is an immature technology, it is highly likely that costs and efficiencies will improve as production scales up, a phenomenon known as ‘learning-by-doing’. Several studies have been carried out on how future hydrogen electrolysis investment costs develop over time or with capacity deployment. However, many are based either on predefined production or capacity developments11 described cost reductions related to aircraft production in a mathematical unit cost model. Since then, global cumulative production volume or overall installed capacity is used to quantify experience, attaining a good match with reality in many different technologies. However, experience curves are often neglected in energy system models, since they result in non-linear, non-convex optimisation problems which adds complexity. Modelling the endogenous cost curves leads to a better representation of the dynamics of new technologies12, but requires a significantly higher computational effort. This is especially important when modelling with perfect foresight over a large time horizon. If the cost and efficiency improvements are given exogenously to the model, the model can ‘wait’ with investments until they are profitable, while in reality, costs would only dynamically decrease as investments take place. Nevertheless, in many models, technology trends including cost reduction or efficiency improvement, are only considered as exogenously assumptions. An example of how these exogenous assumptions overestimate future investment costs is solar photovoltaic (PV). The investment costs of solar have decreased rapidly due to successful policy support. A comparison by Krey et al.13 of the cost assumptions in 15 Integrated Assessment Models (IAM) shows that the cost of PV in 2020 have already fallen below the model expectations for 2050. Way et al. 8 show that the progressive cost projections for solar, onshore wind, batteries and polymer electrolyte membrane (PEM) electrolysis from several IAMs and the International Energy Agency (IEA) are high compared to historical developments or even above costs in 2020. Several other studies17 criticise that the link between cost reduction and capacity installation is not well represented in models and that exogenously-set constraints such as floor costs or excessively low annual growth rates lead to an underestimation of cost reductions. This highlights the need to model investment costs endogenously without assuming unduly elevated floor costs, overly limiting constraints on maximum annual expansion rates or maximum penetration of renewable energies.The concept of the experience curve, which means that costs and efficiencies improve as production increases, is not a new idea. Already in 1936, Wright21 which consider learning in multiple sectors and global developments. But because of the low temporal resolution in these models, they cannot represent the challenges of an energy system with a high share of variable renewable generation. In addition, most IAMs apply the Hotelling rule22 to determine the CO2 prices, which with endogenous learning is not always applicable23. Bottom-up techno-economical Energy System Models (ESM) can better capture the temporal variability of the energy system. However, studies applying endogenous learning only deal with the power sector28 or focus on a single country29.Endogenous cost reductions are currently used in some IAMs30 with full sector coupling and enough temporal resolution to capture renewable variability. The period between 2020-2050 is investigated with seven investment periods and perfect foresight over the whole modelling horizon. We do not specify the maximum annual expansion rates of renewable capacities nor CO2 emission targets for every single year. These additional conditions often lead to a lower computational effort but also predetermine the transition paths. In this way, we determine the cost-optimal annual expansion rates without making any assumptions about expansion limits. Such limits have been estimated to be artificially low in many studies compared to actual capacity developments. We want to address the following two research questions: What are the possible cost developments of green hydrogen production in Europe under the assumption of different CO2 budgets without fixed capacity deployment projections? How do different methods of modelling cost reduction influence the results?In this work, we explore how learning-by-doing on the full hydrogen production chain interacts with a fully sector-coupled energy model. We apply endogenous learning for electrolysis and renewable energy in the European model PyPSA-Eur-SecWe consider three different competing options for producing hydrogen: (i) grey hydrogen (via steam methane reforming (SMR)), (ii) blue hydrogen , and (iii) green hydrogen (via electrolysis). Hydrogen can be used for methanation, for heating (hydrogen boilers), electricity (fuel cells and retrofitted open cyclic gas turbines (OCGT)), in the industry, and in the transport sector. The synthetic gas from methanation can be used in the heating sector (gas boilers or combined heat and power plants (CHPs)), for industry processes or in the power sector (OCGTs or closed cyclic gas turbines CCGTs). The demand pathway for hydrogen in parts of industry and transport is exogenously defined, while in all others sectors, hydrogen competes with other ways of supplying demand. This means both demand for hydrogen (e.g. where it competes with heat pumps in the heating sector) and the supply side (e.g. if the hydrogen is produced via electrolysis or SMR with or without carbon capture), are part of the optimisation. OCGT and gas boilers for heating can be retrofitted to run flexibly with natural gas or hydrogen see Fig. .Fig. 1Ov31 based on current projects to be 2% of the investment costs. The cost of replacing the stack is not included in the fixed operational and maintenance cost (FOM) since it is assumed that the stack does not need to be replaced within the technical lifetime. The investment costs do not include costs for water purification, transformer costs, or connection fees to the transmission system. AEC is currently the dominant technology, but there are other types of electrolysis, such as PEM or solid oxide electrolysis cell (SOEC), which may play a more prominent role in the future. We analyse the impact of higher initial investment costs (comparable to the investment cost of PEM) in the There are various technologies for hydrogen electrolysis. In this study, cost and efficiency assumptions of alkaline electrolysis cells (AEC) are used since they are currently the most common electrolysers available on the market. We only consider large plants (above 100 MW) to avoid the scaling effects of very small plants. These costs consist of the equipment and installation costs. They include the expenses for the stack, power electronics, gas conditioning, balance of the plant and labour. The annual operational and maintenance costs are estimated in ref. 2 budgets. The investment costs of renewable generation capacity  and hydrogen electrolysis are endogenous and thus a result of the optimisation in the form of a piecewise-linearised one-factor experience curve. This results in a mixed integer linear problem MILP which is further described in the Methods Section. Endogenous global learning of the renewable generation capacities for solar PV, onshore and offshore wind is applied. Global learning assumes that global capacity grows proportionally to European capacity. The relative factor corresponds to today’s share. For example, if 1 GW of solar PV is newly built in Europe and the share of European solar PV capacity is 22%, the global capacity grows by about 4.5 GW. Further details about the initial investment cost assumptions of renewable generation are given in the We minimise total system costs for the period 2020-2050 with perfect foresight and investments in new capacity in a five-years interval for three different CO2 budgets and analyse the impact of various cost reduction modelling methods. Here, we show that hydrogen production costs drop to as low as 1.26 €/kgIn this study, we investigate the potential cost developments of green hydrogen production in Europe under different COoC budget, the direct total annualised system costs (ignoring any costs of climate change damages) are up to 27% higher than the +2.0oC budget  existing assets with high CO2 emissions are phased out before the end of their lifetime, (ii) large-scale investments are made before the costs are reduced by learning, and (iii) major parts of the oil demand are already supplied by synthetic fuels in 2030, which are more expensive to produce. However, total annualised system costs of the scenarios in 2050 are similar and vary only by 2% between the +1.5oC and +2.0oC budget since the investment costs decline. The annualised system costs of the +1.7oC scenario in 2050 are 2% lower compared to the +2.0oC scenarios. The reason is that investments in low-carbon infrastructure with the +1.7oC budget happen earlier, while for the +2.0oC scenarios a major infrastructure transformation with associated higher costs is needed in 2050 to meet the condition of climate neutrality. The European Union target of 55% greenhouse gas reduction in 2030 compared to 199032, applied to CO2, is within the +1.7oC and +2.0oC scenarios  for +1.5oC and +2oC respectively , the electrolysis investment costs range between 317 and 360 €/kWoC scenario sees the cost-optimal deployment of 435 GW of electrolysis which produce 36 million tonnes of hydrogen in 2030. This is clearly not realistic given the necessary scale-up of production facilities that would be required. In the scenario corresponding to a temperature increase of +1.7oC, 60 GW of electrolysis are installed by 2030 which produce 4 million tonnes of hydrogen. Under the +2oC scenario, 4 GW of electrolysis are deployed and 0.1 million tonnes of hydrogen are produced . A large sequestration potential of 2000 MtH2) and capture rate (90%) leads to production of blue hydrogen with a share of 19% of total production. With more optimistic assumptions on capture rate (100%) and CO2 storage potential (2000 MtH2.The production of hydrogen switches from SMR (grey hydrogen) to electrolysis (green hydrogen) in all scenarios, while SMR in combination with carbon capture (blue hydrogen) is not installed in any scenario. To further explore the option of SMR with carbon capture, we run sensitivity scenarios for blue hydrogen production depending on capture rate, investment costs and available COoC budget, green hydrogen is already produced in 2025, while in the scenario with a +2oC budget hydrogen is supplied by SMR until 2040–2045  or 2045 (+2.0oC). The REPowerEU plan6, with targeted PV capacities of 750 GWDC37 (which corresponds to 600 GWAC) by 2030, lies within our results of +1.7oC with 764 GW and +2oC with 422 GW. However, the wind expansion targets of REPowerEU with 480 GW38 by 2030, lag behind our findings with at least 555 GW in the +2oC scenario by 2030.In order to achieve net-zero emissions in 2050, renewable generation capacity must be strongly expanded in all scenarios to at least 3.2 TW solar, 1.7 TW onshore wind, and 175 GW offshore wind. The timing of the capacity build-out differs across the individual scenarios: with an ambitious budget, there is a strong expansion between 2030–2040, while in the other scenarios the strongest expansion takes place in later years from 2040  for renewable generation capacity in 2050 range between 171–237 €/kW for solar PV, 818–900 €/kW for onshore wind, and 1327 €/kW for offshore wind from the tight +1.5oC budget are significantly higher than the historical record, which emphasises the challenge to stay within this budget. For example, the average annual build out rate is 77 GW for solar PV in the +1.5oC scenario between 2020–2030. This corresponds to almost a tripling of the historical maximum annual expansion rates in Europe39. The same applies to onshore and offshore wind, with average annual build-out rates of 97 GW and 16 GW between 2020–2030 in our scenario, which are roughly five times the historical maximum annual build-out rates41. Between 2030–2040 even higher build rates are required of up to 200 GW solar capacity per year while the annual expansion of onshore wind decreases to 58 GW. A sensitivity analysis, in which the maximum annual expansion rates of renewables are limited, can be found in the oC scenarios more achievable.The build-out rates of renewables and electrolysis in scenarios with a +1.52 emissions are assumed in order to investigate what a cost-optimal system without a carbon target looks like. As in the previous scenarios, the investment costs of renewables and green hydrogen are subject to endogenous experience curves. Compared to the current system, the share of renewable hydrogen increases even without climate targets. CO2 emissions reduce from historical levels of 3.2 Gt/a in 2020 to 1.9 Gt/a in 2050  the endogenous method where investment costs are adjusted according to the installed capacity, (ii) the standard exogenous method with given fixed cost trajectories for each technology and investment year and (iii) the sequential method. In the sequential method, the optimisation problem is first solved using the exogenous cost assumptions, then the investment costs are updated depending on the optimised installed capacities. The same experience curves are assumed as in the endogenous method. The process of solving and updating investment costs is iteratively repeated until the difference of investment costs between two iterations is below a threshold. The threshold is set to a maximal mean square difference between optimised investment costs of the current and the previous iteration of 5%. The sequential method is a linear problem that requires less computational resources than the MILP of the endogenous method, but unlike the exogenous method, the investment costs are adjusted based on the installed capacities.oC budget, the sequential and exogenous methods result in up to 7% and 13% higher annualised total system costs compared to the endogenous method. The cost difference between the +1.5oC and the +2.0oC budget is smaller with the endogenous method compared to the sequential and exogenous method, because although larger capacities of renewables and electrolysis are needed at an earlier point in time, the costs also decrease more due to the faster scaling up and the foresight of the endogenous method of potential investment cost reductions. The endogenous method, therefore, allows a better comparison of costs of scenarios with different infrastructure needs or transition speeds.The cost difference of not using endogenous learning-by-doing is shown in Fig. oC scenarios in 2025, 214 GW of electrolysis are installed in the endogenous scenarios and only 20 GW electrolysis in both the sequential and exogenous cost scenarios. With the endogenous method, the hydrogen is used for the synthesis of fuels and methane earlier and to a larger extent. In scenarios corresponding to a +1.7oC budget about 1800 TWhThe endogenous method leads to an earlier deployment of the learning technologies since it is the only one of the three methods that has the foresight of how far costs can decrease. As a result, investments are made early in order to reduce investment costs. With the sequential and exogenous methods, investment is delayed and the model ‘waits’ until costs fall. For example in +1.5oC budget hydrogen costs drop to 1.32 €/kgThe prices for hydrogen are an output of the linear optimisation and are determined by the dual variables. For the exogenous and sequential method, these are obtained directly from the optimisation. In the endogenous scenarios, the investment costs and capacities of the learning technologies determined from the optimisation are assumed fixed and the optimisation is rerun as a linear problem. The exogenous method overestimates the cost of hydrogen by up to 67% in 2030 and 38% in 2050 compared to the endogenous scenarios. For example, in endogenous scenarios with a +1.52 budget and not by the modelling method , which are below the sequential optimised investment costs of 79 €/kWelec and the exogenous assumptions of 250 €/kWelec. The produced volume of hydrogen differs between the methods. For example, in scenarios corresponding to a +1.5oC budget, 24% and 30% less hydrogen is produced with the sequential and exogenous methods in 2030. With the endogenous method, more hydrogen is used for methanation and for re-electrification in fuel cells compared to the other two methods.The system composition in 2050 is primarily influenced by the assumed COTechnology learning depends on global developments while most ESM model single regions or continents. Cost in Europe, for example for solar PV, would reduce if solar panels are employed at a large scale in China. In this study, we have assumed that global renewable capacities increase in proportion to the growth in Europe. For electrolysis, we assume local learning. The exogenous method can provide a better description of cost developments for technologies for which large expansion rates outside the modelled region are expected. One could circumvent this problem with the sequential and endogenous method by splitting up the technology learning into two factors describing regional and global learning. The regional factor depends on the installed capacities in the regional model, while the global factor describes an exogenous cost decrease dependent on expected global development. The split into two factors was outside the scope of this study.42 with 12 threads, the scenarios with endogenous learning need about 21 hours to solve and require 30 GB RAM, while the sequential models solve in less than 1 hour with only 3 GB RAM and the exogenous method solves in 1 minute with only 3 GB RAM. The sequential and the exogenous method require lower computational effort and thus offer the possibility to calculate e.g. with higher spatial resolution. The solution time and memory requirements for the endogenous method increase with the number of modelled learning technologies. In contrast, the computational effort of the sequential method is independent of the number of learning technologies, since the costs are only updated after optimisation. This allows us to consider cost reductions for all technologies depending on the installed capacity without increasing the computing time.The exogenous and sequential method require significantly less memory and computing time compared to the endogenous method. For example, using the commercial solver GurobiThe chosen method of modelling technology learning should depend on the particular research question. The sequential and exogenous method are advantageous if the available computational resources are small or a greater level of detail  is important. The number of technologies that are subject to learning can be increased with the sequential method without additional computational effort. The exogenous method can represent developments outside the modelled region. If the cost reductions are expected primarily through local learning within the modelled region, the sequential and endogenous methods are appropriate. The endogenous method is favourable if the timing of the investment is analysed, costs of different climate target scenarios are compared or trade-offs between emerging technologies are considered. Technologies with a large learning potential can be better modelled with the endogenous method, since small additional capacities already lead to strong cost reductions.43, Hydrogen council of 1.4 €/kg44 and BloombergNEF of 0.68-1.55 €/kg45. In contrast to our study, these studies assume an average electricity price and a fixed number of full load hours for electrolysis and thus cannot reflect the system advantage of electrolysis running at very low electricity prices. However, we only assume local learning for electrolysis and do not consider learning due to capacity expansion in other regions, while the other studies analyse global developments. Odenweller et al.46 show that fast scale-ups of electrolysis capacities until 2030 as we see in our +1.5oC scenarios may be infeasible. However, in this study, we want to show the cost-optimal capacities that are necessary to achieve a given CO2 budget. The demonstrated findings have multiple limitations, firstly in the approach used to model experience curves and secondly in the overall assumptions made for the scenarios. For example, we make the simplified assumption that the electrolysis costs are only influenced by local learning. However, in case electrolysis is scaled up in China or the US, this would also lower the costs in Europe. Furthermore, we assume no imports of hydrogen, which would reduce the need for renewable capacity and electrolysis in Europe.In the following, we compare our costs for green hydrogen production with other studies and outline the limitations of this work. In the 2 budgets. In all scenarios, CO2 neutrality is required as an additional condition in the model by 2050. In the second part, we explore the trade-offs of three different methods modelling learning-by-doing.In this study, we use a sector-coupled European model with endogenous cost reductions through experience curves for electrolysis and renewable generation technologies, assuming different COoC warming require a rapid transformation of the energy system already in 2025 and high build-out rates of both electrolysis and renewable generation. This indicates that, without negative emissions after 2050 or further efforts such as sufficiency measures, the scenarios with +1.5oC warming are difficult to achieve. Hydrogen production shifts from grey to green hydrogen. Depending on climate targets, more than half of hydrogen demand is met by electrolysis in 2025 (+1.5oC scenario) or 2045 (+2.0oC scenario). The option of blue hydrogen is not used in any scenario.We find that in scenarios with ambitious climate targets, costs for green hydrogen electrolysis can be reduced to 1.26 €/kgoC target. While for the former, the European Union’s targets for electrolysis are in line with our +1.5–1.7oC budget scenarios, for the latter, the proposed capacities for wind and solar in REPowerEU6 are behind the capacities necessary to provide sufficient green electricity.A rapid build-up of both electrolysis and renewable generation capacity is necessary by 2030 to stay within a +1.5–2We show that ignoring the virtuous circle between capacity expansion and lower costs leads to delayed investments. A comparison of total costs of scenarios with different transformation speeds should consider cost reductions depending on the usage of technology. A faster expansion of technology than predicted in the exogenous cost assumptions thus leads to lower costs, a slower expansion to higher costs. In our results, total annual system costs are thereby up to 13% higher and levelised cost of hydrogen increase by up to 67% with the exogenous compared to the endogenous method. A middle ground approach where costs are updated sequentially offers the advantage of a lower computational effort compared to the endogenous method and maintains the correlation between investment costs and capacities.Significant cost reductions in the production of green hydrogen to 1.26 €/kg30, which minimises total system costs while optimising generation, storage and distribution capacity and dispatch. It covers energy and feedstock demand in the sectors electricity, heating, transport and industry. The model has already been presented in detail in various publications49, so in the following only the newly implemented features for this study are presented.We use the open-source European sector coupled model PyPSA-Eur-Sec2 budgets of 25.7, 45 and 73.9 Gt CO2 are assumed for the whole modelling horizon. These budgets correspond respectively to warming of +1.5oC, +1.7oC, +2oC, assuming a per capita share of the global CO2 budgets (further explanations about the chosen budgets are given in an earlier publication33). The CO2 emission paths are not fixed, except that CO2 neutrality (or negative emissions) is enforced for 2050, in line with the European Commission’s target for net-zero greenhouse gas emissions.The period from 2020 to 2050 is modelled, with perfect foresight and 7 investment periods at 5-year intervals. Three different CO9 and the open-source package powerplantmatching50. Costs, lifetime and efficiencies are assumed for the respective year of the Danish Energy Agency DEA31. The investment costs of renewable generation capacity  and hydrogen electrolysis are in the endogenous case not exogenously specified but are part of the optimisation in form of a piecewise-realised one-factor experience curve. This results in a mixed integer linear problem MILP which is further described below. The total system costs are not discounted by a social discount rate that reflects the value of future investments in order to investigate the impact of learning in isolation from other effects. A high discount rate leads to a significantly lower weighting of costs in 2050 and thus shifts investments into the future while endogenous learning leads to earlier investments.Existing generation capacities  are taken from the IRENA 2020 reportThere are three different competing options for producing hydrogen in the model: (i) grey hydrogen (via steam methane reforming (SMR)), (ii) blue hydrogen (SMR + carbon capture) and (iii) green hydrogen (via electrolysis). Hydrogen can be used for methanation, for heating (hydrogen boilers), electricity (fuel cells and retrofitted OCGT), in the industry, and in the transport sector. The demand pathway for hydrogen in parts of industry and transport is exogenously defined, while in all others sectors, hydrogen competes with other ways of supplying demand in these sectors. This means both demand for hydrogen (e.g. where it competes with heat pumps in the heating sector) and the supply side, for example if the hydrogen is produced via electrolysis or SMR, are part of the optimisation. OCGT and gas boilers for heating can be retrofitted to run flexibly with natural gas or hydrogen Fig. . There a2 emission paths are specified. Those constraints often reduce strongly the solution time of the optimisation problem, but also predetermine the transition paths. Since we consider multiple sectors and investment periods, the optimisation problem is aggregated spatially and temporally to make it computationally solvable. Spatially, energy transmission networks are reduced to a single node for Europe, while six different typical regions are used to represent the variability of renewable generation. We optimise the power transfer capacity between transmission and distribution level. No existing grid infrastructure of distribution grids is assumed. Losses in distribution are neglected. Costs of the distribution grid of 500 €/kWelec are applied. Electricity demands, heat pumps, resistive heaters, rooftop PV, home batteries, and electric vehicles are connected to the distribution grid. All remaining technologies of the power sector  are connected to the high-voltage grid. In the 51 or Heuberger et al.28 using 11 temporal days. The 10 typical days are obtained through k-medoids clustering using the Python package tsam53, so that they represent the average statistics of weather and demand, while also capturing more extreme events. To find the optimal solution we use the commercial solver Gurobi42 using 12 threads.In contrast to other studies, no maximum annual expansion rate of renewable generation capacity and no annual COc decrease by a constant factor α with each doubling of experience E55. In this study, the global cumulative capacity is used as a proxy for experience. c represents the investment costs [EUR per MW], c0 the initial investment costs [EUR per MW] Tables Experience curves are an economic concept based on empirical evidence in which the specific investment costs entclass1pt{minimaentclass1pt{minima25 to integrate experience curves within ESM: direct non-linear implementation and piecewise linear approximation of cumulative costs. In this paper, we follow the latter and use special ordered sets of type 2 (SOS2). Compared to the non-linear implementation this has several advantages: it can find a global minimum rather than getting stuck in a local one, it does not depend on the initial starting point of the solver, and it can be solved faster using commercial solver algorithms.Experience curves make ESM optimisation problems both non-linear and non-convex, which makes solving particularly challenging. There are two main approachesα ≠ 1 → LR ≠ 0.5 this results inThe cumulative investment costs for technology can be obtained by integrating the experience curve Equation . For α ≠nts Fig. . The gre56 6, global MARKAL 6–8, MARKAL-Europe 6–2057. Heuberger et al. 28 5. In this study we assume five line segments. This corresponds to six interpolation points i ∈  where N is the number of interpolation points.Other models use the following number of segments: In ERIS27: line segments at the beginning of the learning are shorter to capture the steep part of the experience curve more precisely. With each line segment the cumulative cost increase doubles.The definition of the line segments follows Barreto’s approachδs,t,i ∈  for each technology s (e.g. solar PV), investment period t and interpolation point i ∈  of the linearisation. N is the total number of interpolation points used for the piece-wise linearisation. The continuous variable indicates which line segment is active. Only two adjacent delta variables are non-zero at the same time t for technology s and their sum is equal onei is active then only δs,t,i and δs,t,i+1 are non-zero.We define a set of continuous variables Es,t of a technology s, time t and interpolation points i is defined as a summation of the product of the continuous variable δ and the x-position of the interpolation points i is active, then (4) will interpolate between The cumulative capacity s,t per investment period in Europe is the difference of the cumulative experience weighted by a global factor gfThe new installed capacity capE with the help of the SOS2 variables δ and the y-position of the interpolation points If no time-delay for the learning effects would be considered, one could express the cumulative cost TC similar to the cumulative experience t depends on the cumulative installed capacities at the previous investment period t − 1. This represents learning effects more realistically, as investment costs do not decrease immediately in the same reference year as the emerging technology is employed, but are subject to a time lag. One should be aware that this results in an overestimating of the cumulative cost curve shown exemplary in Fig. ms,t−1,i is the slope of the line segment at the previous investment period t − 1. As the total costs are the integral of the experience curve, the slope ms,t,i is equivalent to the specific investment costs.In this study we consider a temporal-delayed learning effect which means that the investment cost decrease in an investment period s,t are defined asThe overall investment costs per investment period invSupplementary InformationPeer Review File


Following the publication of this article , concernIn Fig 7B, clusters of cells within the HepG2 non-treated well appear similar to each other, and to clusters of cells in the SMMC-7721 non-treated well.○ MEK and p-MEK○ ERK and p-ERKIn S1 Raw images, the original blots underlying the following pairs of total and phosphorylated blots appear to have similarities in marker bands and background pattern:In the Bcl2 image in S1 Raw images, there appears to be a discontinuity in the background between the marker lane and lane 1.In the p-p38MAPK image in S1 Raw images, the band in lane 1 appears to overlap with the marker lane.The SMMC-7721 cell line reported in this study as a human liver cancer has been identified as a contaminated cell line, and was shown to be a HeLa derivative cell line  prior toThe corresponding author stated that all data underlying the results in this article are available, except for raw data underlying the flow cytometry experiment in Fig 8.PLOS ONE editors remain concerned regarding similarity of cells within and between wells.The corresponding author acknowledged that clusters of cells in two wells in Fig 7B appear similar, and they stated that the similarity was observed in other experiments using these cells. The For pairs of total and phosphorylated proteins (MEK and ERK), the corresponding author clarified that blots were not stripped and reprobed. They stated that total and phosphorylated protein were run on the same gel, blots were cut prior to incubation with antibodies, then imaged independently using the same marker lane. They provided different versions of the original blots without the marker lane added. The editors remain concerned about the similarities between the images for total and phosphorylated blots, in particular the similarities in background pattern.The corresponding author’s responses regarding the Bcl2 and p-p38MAPK images in S1 Raw images did not resolve the concerns. They also stated that the SMMC-7721 cells used for this study were not authenticated prior to use.PLOS ONE Editors retract this article.In light of the concerns affecting multiple figure panels that question the integrity of these data, the MB did not agree with the retraction. SA, NM, MG, MEK, and AIE either did not respond directly or could not be reached.