Objective: To analyze the factors associated with type 2 diabetes mellitus (T2DM) “remission” in non-bariatric Medicare patients 65 years and older.Research Design and Methods: A retrospective cohort analysis of a Medicare Advantage health plan was conducted using administrative data. An individual was identified as T2DM if the individual had: ≥ 2 medical claims for T2DM coded 250.xx excluding type 1 diabetes; or ≥ 2 pharmacy claims related to T2DM; or ≥ 2 combined medical claims, pharmacy claims for T2DM in 12 months. A T2DM individual was in “remission” if they had no T2DM related claims for more than 12 months continuously. This is different from the standard American Diabetes Association (ADA) definition of remission which includes HbA1c values and hence is represented in quotation (as “remission”). 10,059 T2DM individuals were evaluated over a period of 8 years from 2008 to 2015. Cox proportional hazards was used to identify significant variables associated with T2DM “remission.”Results: 4.97% of patients studied met the definition of T2DM “remission” in the study cohort. After adjusting for covariates this study found a number of variables associated with T2DM “remission” that were not previously reported: no statin use; low diabetes complications severity index score; no hypertension; no neuropathy; no retinopathy; race (non-white and non-African American); presence of other chronic ischemic heart disease (IHD) and females (p < 0.05).Conclusion: T2DM “remission” in Medicare patients 65 years and older is observed in a community setting in a small proportion of non-bariatric patients. Type 2 diabetes mellitus (T2DM) is generally considered as a chronic, incurable, progressive and life-long disease condition. T2DM disease management focuses primarily on glycemic control and management of the macrovascular and microvascular complications that may be associated with hyperglycemia , 2. Howepartial remission, defined as sub-diabetic hyperglycemia without active pharmacologic therapy or ongoing procedures for at least 1 year. Sub-diabetic hyperglycemia is characterized by HbA1c < 6.5% and/or fasting glucose 100–125 mg/dL [5.6–6.9 mmol/l]; b) complete remission, defined as “normal” measures of glucose metabolism without active pharmacologic therapy or ongoing procedures for at least 1 year. Normal measure of glucose metabolism is when HbA1c is in the normal range, < 5.7%, < 39 mmol/mol, fasting glucose < 100 mg/dL [5.6 mmol/l]); and c) prolonged remission, defined as complete remission for at least 5 years (The American Diabetes Association (ADA) has defined the term remission as “…achieving glycaemia below the diabetic range in the absence of active pharmacologic or surgical therapy.” ADA classifies remission into one of the following three types: a)  5 years .Current evidence on T2DM remission suggests that there are at least two key approaches for T2DM remission namely, metabolic/bariatric surgery and/or intensive lifestyle management . Of thespost hoc randomized controlled study from the Look AHEAD  and the 2014 Kaiser Permanente study have shown metabolic/bariatric surgery is not the only pathway to remission. Although rare, T2DM remission does occur in certain non-bariatric populations . However, laboratory test data are not always available to researchers, especially to those researchers who only rely on administrative claims data for their analytical work  “remission” and analyze demographic, clinical and pharmacological factors associated with T2DM “remission” in non-bariatric Medicare patients 65 years and older.This study was a retrospective cohort analysis using Medicare Advantage Prescription Drug (MAPD) health plan data. This administrative data includes patient level enrollment, medical claims and pharmacy claims data. These data were collected during the standard operations of a large multistate healthcare company. It was not originally collected for the purpose of research or any new study of human or animal subjects. Prior to the start of the study this study protocol was reviewed and approved by the University of Louisville Institutional Review Board. The study was also approved by the healthcare company's review board.The federal government offers original Medicare insurance, which includes Medicare Part A  and Medicare Part B . Part C (or Medicare advantage) are offered through private insurance companies. Part C includes the benefits of Part A, Part B and sometimes the optional Part D (prescription drug coverage) along with several choices of programs and services specific to each Managed Care Organization (MCO). Individuals can only be enrolled in Part C if they qualify for Medicare part A and part B. An individual may qualify for Medicare if they are at least 65 years old in the year of their Medicare enrollment and meet certain additional eligibility criteria. In addition, some individuals may qualify for Medicare for other reasons such as disabilities or end stage renal disease (ESRD). Individuals whose initial reason for Medicare enrollment was either disability or ESRD were excluded from the present study.The large multistate healthcare company offers Medicare Advantage Prescription Drug Plan (MAPD) a Part C plan, which includes the benefits of Medicare Part A, Part B and Part D, to individuals. The healthcare company insured more than 2.4 million individuals who had MAPD coverage from January 1, 2008 to December 31, 2015. Of these ≥ 2.4 million individuals more than 1.7 million MAPD individuals had “age ≥ 65” as the original reason for Medicare enrollment. The remainder qualified under Medicare disability or ESRD eligibility provisions. Of these > 1.7 million patients there were 265,554 patients who had continuous MAPD coverage with the healthcare company from January 2008 to December 2015.n = 619); (b) individuals with polycystic ovary syndrome (n = 131), and lipodystrophy (n = 718); (c) individuals with any diagnostic codes related to nephropathy were excluded because it indicates irreversible end organ damage. In the current study, the nephropathy exclusion resulted in excluding 41.3% of the study sample. Previous research suggested that the rate of chronic kidney disease (CKD) in individuals who are 65 years and older was 61%. CKD rate in the current study was likely lower because individuals with “disability” and “ESRD” as their initial reason for Medicare enrollment were excluded from the sample ; (e) individuals with type 1 diabetes were identified using ICD codes and excluded because type 1 diabetes has a different etiology from T2DM ; (f) individuals who had T2DM related diagnostics codes only during inpatient hospital stay and did not have T2DM diagnostic claims or T2DM related pharmacy claims in a non-hospital setting were excluded to account for potential surgery induced dysglycemia ; g) certain groups with contractual restrictions with the health plan prohibiting research .The initial sample after the above exclusions of 265,554 patients, additional exclusions were: (a) individuals who had a prior bariatric surgery  n = 6; (b) indc data. An individual was encoded as being in “remission” if they met the following criteria: (a) had two or more T2DM related claims in previous years; (b) had no T2DM related claims in the given year; (c) upon “remission” T2DM individuals were required to continue to have no T2DM related claims until the end of study period, 2015. This requirement excluded individuals who experienced T2DM relapse. This study's focus was only on individuals who experienced continued “remission” and not intermittent “remission.”The definition of “remission” in this study was different from that used by ADA, because of the lack of HbA1Individuals were required to have T2DM in the year 2008, 2009, 2010, and 2011 and either continue to have diabetes (no “remission” group) or achieve “remission” (the “remission” group) to be included in the T2DM “remission” study cohort. T2DM relapse can occur in some individuals after “remission.” Individuals who had relapse after first “remission” were excluded from the study. The study cohort included a total of 10,059 patients. The “remission” group included 500 individuals and 9,559 patients in the no “remission group  related to T2DM. A minimum of two claims was used in the identification of T2DM to exclude patients who were miscoded and patients who were suspected to have T2DM but were never formally diagnosed.The Diabetes Complications Severity Index (DCSI) models the severity of diabetes complication at a specific point in the patient's life. DCSI is calculated as previously described , 20. TheThis study used ICD-9 and ICD-10 diagnostic codes to identify diabetic ophthalmologic disease, background retinopathy, other retinopathy, retinal edema, cystoid macular edema/degeneration (CSME), other retinal disorders, proliferative retinopathy, retinal detachment, blindness, vitreous hemorrhage). An individual with any of the above clinical conditions was encoded as having retinopathy.This study used ICD-9 and ICD-10 diagnostic codes to identify diabetic neuropathy, amyotrophy, cranial nerve palsy, mononeuropathy, Charcot's arthropathy, polyneuropathy, neurogenic bladder, autonomic neuropathy, gastroparesis/diarrhea, orthostatic hypotension. An individual with any of the above clinical conditions was encoded as having neuropathy.This study used ICD-9 and ICD-10 diagnostic codes to identify diabetic PVD, other aneurysm lower extremities, PVD, foot wound and complication, claudication, intermittent, embolism/thrombosis lower extremities, gangrene, gas gangrene, ulcer of lower limbs. An individual with any of these clinical conditions was encoded as having PVD.T2DM related clinical conditions were defined using administrative claims data as described previously using both ICD-9 and ICD-10 codes, where applicable . Demogracounting process input style . Hadoop is an open source software framework for distributed processing and distributed storage of very large datasets . Processut style , 23. Meaut style . The adjn = 500) who met the definition of T2DM “remission” in the study cohort. At 2011, the average age of the “remission” group was 73.7 years, while that of the non- “remission” group was 72.6 years. In the “remission” group 62.2% were female, compared to 57.2% in the non-“remission” group  patients 65 years and older (n” group .p < 0.05) , neuropathy  and diabetic peripheral vascular disease. However, the “remission” group had higher cardiovascular disease  ( < 0.05) .p < 0.05).In multivariate analyses, T2DM “remission” was associated with no statin use, low diabetes complications severity index score, no hypertension, no hyperlipidemia, no neuropathy, no retinopathy, race (Asian/Hispanic), presence of other chronic ischemic heart disease (IHD) and female gender (p > 0.05) . However, these associations were attenuated in multivariate analyses and were not significant (P < 0.001)  of patients 65 years and older met the definition of T2DM “remission” in the study cohort. This was not significantly different from the 2.2% (1.34–3.54%) of the 2014 Kaiser Permanente report on ≥65 year old population . It is s< 0.001) . The stu< 0.001) , 24, 25.Consistent with the 2014 Kaiser and the Look AHEAD studies, this study also found that the absence of hyperlipidemia was associated with T2DM “remission.” As suggested in previous findings, hyperlipidemia, obesity, and impaired glucose tolerance may have a common biological pathway commonly called metabolic syndrome. These findings provide additional evidence of the role of managing impaired lipid metabolism to slow or reverse T2DM progression.p < 0.05).Further, this study evaluated multiple comorbidities related to macrovascular and microvascular comorbidities that are typically associated with T2DM. After adjusting for covariates this study found a number of variables associated with T2DM “remission” that were not previously reported: no statin use; low diabetes complications severity index score; no hypertension; no neuropathy; no retinopathy; race (non-white and non-African American); presence of other chronic ischemic heart disease (IHD) and females  (Consistent with previous studies the current study found that Hispanics and other non-white race (excluding African American) were more like to have a T2DM “remission” in the Medicare 65 years and older population ( < 0.05) . This fip < 0.05).Previous studies indicated that hypertension and T2DM generally occur together and share several common risk factors such as obesity, inflammation, and insulin resistance. Hypertension and T2DM also share common pathways such as those associated with the sympathetic nervous system (SNS) and renin-angiotensin-aldosterone system (RAAS) . The prep < 0.05). There is no previous literature on the role of statin in T2DM “remission.” However, its role on T2DM onset is well-established. Statin therapy is associated with a 9% increased risk of T2DM , as previously described, to quantify the severity of diabetic complications , 34. ThiRetinopathy is one of the most common microvascular damage sites associated with T2DM. Diabetic retinopathy is a major public health problem, with approximately one third of T2DM patients developing some form of retinopathy . The heap < 0.05). Taken in combination with previous recommendations, this study suggests that providers must educate T2DM about the possibility of “remission” by lifestyle modifications a self-incentive for enabling modified behavior, and that they still have a chance to reverse the progression of T2DM.Evidence in the present study found that the absence of any T2DM retinopathy was associated with T2DM “remission , compressive injury, ischemic injuries, inflammatory and/or immune response, and other comprehensive etiologies . This stc levels. Another explanation for this association is that individuals who have certain clinical conditions, such as those related to cardiovascular diseases, might be on constant alert to make healthier choices, especially due to the constant reminders that they might be receiving from their provider, insurance agency. These factors, and others, may be the reason why these individuals have a different outcome compared to those without this clinical condition.IHD is the loss of elasticity and thickening of the coronary arteries, leading to progressive arterial insufficiency. Individuals with “other chronic IHD” were more likely to go into T2DM “remission.” This association is not completely understood. One explanation of this association is that there may be a small proportion of individuals in the study sample where the provider takes them off their diabetic medication due to a variety of reasons, such as the caregiver is less aggressive in motivating patients to achieve lower HbA1c lab results, which would have provided a more accurate measure of T2DM “remission.” Therefore, a small proportion of individuals could have been asked by their doctors to stop taking T2DM medication for a variety of unknown reasons as noted. The probability of this occurring in the data is very low because even in those cases the physician will probably capture the T2DM related diagnostic code on the medical claims. Also, as with other similar retrospective studies there is a potential for error in information coded in the administrative data. As in other retrospective studies it is not possible to establish causation with such an analytic approach. The level or potency of the T2DM medications including statins was not evaluated in this analysis. DCSI is an unweighted composite index, and does not independently test individual outcomes associated with each complication associated with T2DM. Metabolic syndrome is not included in the study due to lack of data such as BMI and physician notes. T2DM onset age was not available to calculate T2DM disease duration. It is not known if an individual had bariatric surgery prior to 2008. Individuals in the study had T2DM from 2008 to 2011 and did not have bariatric surgery between 2008 and 2015. Typical remission rates are greatest by year 2 of bariatric surgery (There were several limitations to this study. First and most important, the study does not include clinical and laboratory findings such as physician notes and HbA1 surgery . TherefoIn conclusion, T2DM “remission” in patients 65 years and older with Medicare insurance is observed in a community setting in a small proportion of individuals who did not undergo bariatric surgery. This study helps establish additional evidence to the role of lifestyle intervention in T2DM progression and provides some insights into the possibility of using behavior and lifestyle interventions as alternate pathways for diabetes treatment and management.Lifestyle interventions such as increased physical activity, low carbohydrate diet, cessation of tobacco smoking, and reducing alcohol intake are already recommended by providers to T2DM patients as a means to control glucose, blood pressure and cholesterol. These study results can further help providers discuss disease prognosis with their T2DM patients and in designing efficacious interventions to the right individuals at the right time. This will help design better T2DM management programs. For example, if a patient 65 years and older individual is diagnosed with T2DM and has not yet developed any chronic comorbidities such as neuropathy or retinopathy, but has modifiable comorbidities such as hypertension and hyperlipidemia, then their provider can use the insights provided in this study to advise their patients about their possibility to control their T2DM progression by participating in lifestyle intervention programs. DCSI score can be used as an index to further tailor these management programs.These findings also have significant public health implications ranging from health policy, clinical guidance design, built environment and incentives to enable the success of these lifestyle intervention programs. Lastly, this study recommends further investigation into the lifestyle pathway for T2DM “remission” to improve our understanding of this beneficial alternative. Thus, it may be possible to increase the percent of individuals who might benefit from T2DM “remission.”The IRB for Human Subjects at the University of Louisville reviewed the study protocol and classified it as exempt status because previously collected information with no PHI.ST, BL, RE, GB, and AL contributed to the analysis and interpretation of results. ST conducted all data manipulations as the study data was a proprietary secured database.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


So far, Singlet Oxygen Sensor Green (SOSG) has been the only commercialized fluorescent probe for 1O2 imaging though it suffers from several limitations  that need to be carefully considered to avoid misinterpretation of the analysed data. Herein, we present results of a comprehensive study focused on the appropriateness of SOSG for 1O2 imaging in three model photosynthetic organisms, unicellular cyanobacteria Synechocystis sp. PCC 6803, unicellular green alga Chlamydomonas reinhardtii and higher plant Arabidopsis thaliana. Penetration of SOSG differs in both unicellular organisms; while it is rather convenient for Chlamydomonas it is restricted by the presence of mucoid sheath of Synechocystis, which penetrability might be improved by mild heating. In Arabidopsis, SOSG penetration is limited due to tissue complexity which can be increased by pressure infiltration using a shut syringe. Photosensitization of SOSG and SOSG endoperoxide formed by its interaction with 1O2 might be prevented by illumination of samples by a red light. When measured under controlled conditions given above, SOSG might serve as specific probe for detection of intracellular 1O2 formation in photosynthetic organisms.Formation of singlet oxygen ( In plant cells, it has been linked mainly to high light4 but it has been known to occur also in other types of abiotic stress as high temperature, wounding or heavy metals5. At high light, 1O2 is formed by triplet-triplet energy transfer from triplet chlorophyll to molecular oxygen formed either by change in the spin orientation and charge recombination of the triplet radical pair3 [P680•+Pheo•−] in the antennae complex and the reaction centre of photosystem II (PSII)8. Under high temperature and wounding, 1O2 was shown to be formed by triplet-triplet energy transfer from triplet carbonyls to molecular oxygen formed during lipid peroxidation11. The role of 1O2 in plant organism is ambivalent; under moderate stress, 1O2 appears to act in retrograde signaling pathways14, whereas under severe stress, it causes oxidative damage of proteins and lipids17. At low 1O2 concentrations, retrograde signaling is associated with acclimation response, whereas at high 1O2 concentrations it is accompanied with programmed cell death13.Singlet oxygen  is possible in PSII membranes, thylakoids and chloroplasts22. In vivo1O2 detection includes cytochemical techniques using fluorescent and chemiluminescent probes combined with microscopic imaging23. In the last two decades, dansyl-based (DanePy)24 and Singlet Oxygen Sensor Green reagent® (SOSG)9 fluorescent probes based on the formation of endoperoxides were used to monitor 1O2 formation in photosynthetic organisms. These probes are composed of an anthracene moiety (electron donor) that quenches the fluorescence of the fluorochrome (electron acceptor) through electron transfer. Once the anthracene moiety traps 1O2, the resultant oxygen adduct fails to be functional intramolecular electron donor and the fluorescence is recovered.Several spectroscopic and microscopic techniques have been used to detect 23. Unlike other available fluorescent and chemiluminescent 1O2 probes, SOSG fulfils the requirement of high selectivity and specificity. It does not show an appreciable response to superoxide anion radical (O2•−) or hydroxyl radical (HO•) which is important especially in chloroplasts where a wide range of ROS are formed4. This fluorescein-based dye probe initially exhibits weak blue fluorescence at 395 and 416 nm, under excitation at 372 and 393 nm, respectively. Upon reaction with 1O2, the imminent product SOSG endoperoxide (SOSG-EP) provides green fluorescence, with the excitation and emission maxima lie, based on several authors, in the range of 504–508 nm and 525–536 nm, respectively27. Spectral properties of SOSG are similar to those of fluorescein which made this probe suitable for observation with a wide range of fluorescence and confocal microscope systems equipped with standard filters.Singlet Oxygen Sensor Green has been released and tested on plants since mid-2000’s28. Further constraints of SOSG application in light-dependent studies is due to its photosensitization. It was demonstrated that SOSG can generate 1O2 under exposure to UV radiation (355 nm) and visible light (532 nm), respectively25. Later, it was shown that SOSG-EP can serve as photosensitizer even with much higher efficiency than SOSG26. A detailed study on SOSG photochemistry showed that singlet excited SOSG converts to triplet excited SOSG via intersystem crossing which further transfers the triplet excitation energy to molecular oxygen forming 1O227.However, SOSG is known to have several drawbacks  that must be taken into consideration. SOSG was introduced as a cell-impermeable probe but later studies shown its localization in most cell compartments under different stress conditions25. Pedersen and co-workers discovered and applied on mammalian cells, a modified 1O2 indicator called Aarhus Sensor Green (ASG) 29. Aarhus Sensor Green is characterized by spectral properties similar to SOSG omitting its negative trait of photosensitization. Nevertheless, ASG has neither been tested on plants nor available for routine applications. Thus developing new 1O2 sensors remained a goal of research teams. In chemical systems, more alternatives to SOSG exist, including FRET-based ratiometric monitoring of 1O2 with acene-doped conjugated polymer nanoparticles30 or 1O2 detection in solutions by Bodipy-based energy transfer cassette31. Recently, Tang et al.32 proposed an alternative indocyanine green (ICG), a near-infrared (NIR) tricarbocyanine probe that is decomposed after reaction with 1O2. However, the absorption and emission peaks of ICG at ~807 nm and ~822 nm, respectively, make it less convenient for the fluorescence microscopy.Due to the SOSG and SOSG-EP photosensitization, an effort was initiated to discover fluorescent probe characterized by specificity and sensitivity similar to SOSG; however, lacking its negative trait of photosensitization1O2 formation by high temperature or leaf wounding itself. Illumination by a red light (λ ≥ 600 nm) is advised to avoid 1O2 formation due to SOSG and SOSG-EP photosensitization. As the experimental conditions may significantly affect the obtained results, we attempt to summarize the principal factors that must be taken into account when imaging 1O2 with SOSG in photosynthetic organisms.The present study deals with the re-examination of SOSG properties for its possible utilization in model photosynthetic organisms  under high light. Penetration of SOSG into the cells can be enhanced by mild heating (cyanobacteria) or moderate-pressure tissue infiltration (higher plants) under conditions avoiding Synechocystis sp. (PCC 6803) was grown in BG-11 medium supplemented with 5 mM glucose and 5 mM sodium bicarbonate as the main carbon source under a continuous white light (100 μmol photons m−2 s−1) at 25 °C33. The cell culture was permanently stirred using magnetic stirrer RT 5 power  to obtain a constant CO2 concentration in the growth medium. The cells from the end of log phase at a density of approximately 108–109 cells ml−1 were used for experiments. Chlamydomonas reinhardtii (CC-002) was obtained from the Chlamydomonas Genetic Center . The cells were cultivated in Tris-Acetate-Phosphate (TAP) medium or in Bold’s basal medium (BBM) in a continuous white light (100 μmol photons m−2 s−1) in Algaetron AG 230 . The growth was achieved under permanently stirred condition using shaker  to obtain a constant CO2 concentration in the growing medium34. The cells were studied at a density of approximately 7 × 107 cells ml−1 during the stationary growth phase. Arabidopsis thaliana (Col-0) seeds from the Nottingham Arabidopsis Stock Centre (NASC) were soaked in distilled water for 4 days at 4 °C and transferred into the growing pots filled with a peat substrate . The plants were grown for 4–6 weeks in a growing chamber at a photoperiod of 8/16 h light/dark (100 μmol photons m−2 s−1) at a temperature of 22 °C/20 °C, respectively and relative humidity 60%.Synechocystis cells , Chlamydomonas cells  and Arabidopsis leaf pieces with adaxial side of leaf up  were placed in 2 ml Eppendorf tube and treated with 50 μM SOSG  for 30 min either in dark or exposed to light. For Synechocystis and Chlamydomonas cells, the media containing SOSG was replaced with growing medium before measurement while for Arabidopsis leaf, the measurement was done in pure buffer.−2 s−1 transferred by optical fiber guide to the top of Eppendorf tube. Long-pass edge interference filter (600 nm)  was used to cut off the blue-green region of the spectra.The illumination was performed using a LED source with a light guide CL6000 LED Zeiss . Samples were exposed to continuous white light (λ = 400–750 nm) or red light (λ ≥ 600 nm) with an intensity of 1000 μmol photons mFluorescence emission spectra were measured using a fluorescence spectrometer F-4500 . For fluorescence emission spectra, the SOSG was excited at 488 nm and SOSG-EP fluorescence was recorded in the spectral range of 500–640 nm.35. Integral distribution of signal intensity (0–4096) in 12-bit microphotographs was evaluated by image analysis software FV10-ASW 4.0 Viewer (Olympus).Immediately after staining, the cells were transferred to corresponding fresh media and visualized by confocal laser scanning microscopy . The excitation of SOSG was performed by a 488 nm line of an argon laser and the emission was detected by a 505–525 nm filter. Cell morphology was visualized by transmitted light detection module with 405 nm excitation using a near ultraviolet (405 nm) diode laser and differential interference contrast (DIC) filters. The proper intensity of all lasers was set according to unstained samples at the beginning of each experiment1O2 formation in unicellular photosynthetic organisms, SOSG-EP fluorescence was detected in Synechocystis  was measured in Chlamydomonas cells exposed to red light. A considerable suppression in SOSG-EP fluorescence was observed  or white (400–750 nm) light Fig. . Figure lls Fig. , Chlamydlls Fig.  and Arablls Fig.   and longer incubation periods (30 min) has to be used. Since Flors and co-workers9 reported the applications of SOSG on diatom species and Arabidopsis leaves, several teams employed SOSG in their experiments with unicellular organisms41 and higher plant46. SOSG-EP fluorescence visualized in the multiple optical sections through cells of Synechocystis, Chlamydomonas and Arabidopsis confirmed that SOSG penetrates into cells in all tested model photosynthetic organisms  as used previously47 can substantially increase the uptake of SOSG , are limited to a minimum time necessary for the only fast sample navigation and focusing.Imaging of SOSG and SOSG-EP fluorescence signal is mostly provided by confocal systems employing a 488 nm laser for excitation. However, the complex part of the microscopic study is finding the site of interest within a biological sample. Several authors50. Confocal laser scanning microscopy enables detection of the signal from a single focal plane and therefore imaging of more cyanobacterial cells in a single figure might be confusing as it usually includes also cells out of focus. In Chlamydomonas, the presence of flagella exerting algal movement hampers the relatively long confocal imaging. Unfortunately, the requirement of intactness of the cell for in vivo studies excludes the use of heat or chemicals used for flagella release. Thus, for unicellular organisms  small volumes (ca 5–10 µl under cover slide) of the treated cell suspension can be advised for their immobilization throughout the microscopic observation. To study the 3-D distribution of SOSG-EP fluorescence within cells or tissues, Z-stacks of thin optical sections are employed in confocal microscopy. However, the time vs. resolution during scanning vs. number of optical sections must be compromised to avoid photobleaching. The mode how studied biological matrices affect the outcome of SOSG staining was compared in the three photosynthetic models and SOSG proved to be rather stable, i.e. suitable for multi-layer scans. To verify signal location, particularly in small objects like prokaryotic cells, it is advisable to decrease the pinhole size to 0.9–0.5 airy units, according to signal intensity. Based on the results when SOSG-EP fluorescence was detected in almost all optical sections of the various photosynthetic samples, ranging from unicellular prokaryote Synechocystis to complex tissue structure in Arabidopsis leaves, it can be pointed out that SOSG does not suffer from a significant photobleaching compared to other fluorescein probes.Synechocystis is characterized by a prokaryotic cell type  which size changes over the cell cycle; cells from the logarithmic and stationary phase of growth curve usually reach 1.5–2.5 µm in diameterOnce collected the whole set of microphotographs (taken under the same standardized conditions) can be post-processed in suitable software to optimize signal-to-noise ratio and thus unable more clear presentation of the signal on screen or in print, particularly for figures with merged channels. This can help especially in 12-bit and 16-bit images, characterized by 4,096 and 65,536 shades, respectively. A universal approach cannot be advised due to the variability of plant materials but the crucial point is to apply the identical algorithm to all variants compared in the experiment to avoid false positive signals. Especially in multi-celled tissues, like Arabidopsis leaf sections, the signal location in chloroplasts is better visible after filtering the background and highlighting the SOSG signal in merged figures Fig. .1O2 detection in the photosynthetic organism. Although SOSG penetration into the cell and photosensitization of SOSG by visible light entails the main limitation of SOSG applicability in studies focused on high light stress in the photosynthetic organism, awareness of all the previously mentioned criteria (suitable infiltration procedure and red light) may lead to credible results. When measured under these conditions, SOSG can attend as highly sensitive and specific probe to identify the site of 1O2 formation in unicellular photosynthetic organisms and higher plants. However, quantitative analysis of 1O2 formation performed exclusively based on SOSG-EP fluorescence images is misleading. Only combination of several methods, e.g. SOSG-EP fluorescence imaging in vivo and spin probe EPR spectroscopy in vitro, provides comprehensive spatiotemporal and quantitative characterization of 1O2 formation in photosynthetic organisms. The different level of 1O2 production in different organism can also be attributed to variable cell density, different chlorophyll content, antioxidant level, within the cell/tissues etc. and thus based on the parameters to be evaluated, proper control experiments are required.The data presented in this study are expected to contribute to better understanding of the limitation in the use of SOSG for Supplementary Dataset 1–5


The data presented in this article are related to the original research article entitled “A novel bioscaffold with naturally-occurring extracellular matrix promotes hepatocyte survival and vessel patency in mouse models of heterologous transplantation”   Specifications TableValue of the data•The data demonstrate for the first time that by using a novel approach to generate a decellularized liver scaffold (DLS) from regenerative liver (rDLS) which is different from the scaffold that generated from naïve livers (nDLS). The nDLS has been widely studies in the field.•The data providing here for the first time support the rDLS with “active ECM environment” for preventing thrombosis in vivo, and the idea opens a new window for other researchers promising step toward to engineer functional organoids.1Naïve decellularized liver scaffold (nDLS)-based tissue engineering has been impaired by the lack of a suitable extracellular matrix (ECM) to provide “active micro-environmental” support 22.1The C57BL/6 mouse strain and pigs  was used in this study. A surgical method and perfusion procedure 2.2DNA content was quantified as previously described 2.33), fixed in 4% glutaraldehyde stained with 1% osmium tetroxide  for 1 h. Then, sections were washed in PBS and dehydrated using a graded series of alcohol. Critical drying was performed in absolute ethanol. Each sample was mounted on an aluminum stub and sputter-coated with a 7-nm layer of gold  before examination using a JEM 6335F field emission scanning electron microscope .RDLS samples were assessed by scanning electron microscopy as previously described 2.4The fixed DLS samples were embedded in flash-frozen in liquid nitrogen and sectioned to 5 μm. Sections were blocked with 10% fetal bovine serum for 30 min and primary antibodies  were applied overnight at 4 °C. after secondary antibody, sections were mounted using fluorescence mounting medium . Fluorescence signals were observed under a fluorescence microscope . Analysis was performed using Image J software .2.5Mice were anesthetized and opened along the midline to expose the right kidney. After clamping the right branches of the renal artery and vein, a nephrectomy was performed. Once the right kidney was removed, the DLS was placed into the nephrectomy site. The portal vein and inferior vena cava of the host were introduced into the DLS graft׳s portal vein, and another of the same size was introduced and secured in the inferior vena cave. An end-to-end anastomosis was made between the cuff of the recipient׳s left renal artery branch and the scaffold׳s portal vein. The connection of the recipient׳s inferior vena cava to that of the scaffold was performed using end-to-side anastomosis and Any blood lost during surgery was replaced with lactated Ringer׳s solution.


Despite recent advances in observational data coverage, quantitative constraints on how different physical and biogeochemical processes shape dissolved iron distributions remain elusive, lowering confidence in future projections for iron-limited regions. Here we show that dissolved iron is cycled rapidly in Pacific mode and intermediate water and accumulates at a rate controlled by the strongly opposing fluxes of regeneration and scavenging. Combining new data sets within a watermass framework shows that the multidecadal dissolved iron accumulation is much lower than expected from a meta-analysis of iron regeneration fluxes. This mismatch can only be reconciled by invoking significant rates of iron removal  to balance iron regeneration, which imply generation of authigenic particulate iron pools. Consequently, rapid internal cycling of iron, rather than its physical transport, is the main control on observed iron stocks within intermediate waters globally and upper ocean iron limitation will be strongly sensitive to subtle changes to the internal cycling balance. Iron is crucial for marine photosynthesis, but observational constraints on the magnitude of key iron cycle processes are lacking. Here the authors use a range of observational data sets to demonstrate that the balance between iron re-supply and removal in the subsurface controls upper ocean iron limitation. Even where nitrogen (N) and phosphorus (P) are the main limiting factors, Fe continues to play a key role by driving rates of N fixation2 and acquisition of dissolved organic P3. Fe limitation ultimately arises owing to a deficiency in the supply of Fe, relative to N and P4. Away from regions of dust deposition, the dominant component of Fe delivery, relative to N or P, is its relative concentration in thermocline waters5. This is particularly apparent across the south Pacific Ocean, where transport by sub-Antarctic mode water (SAMW) and Antarctic Intermediate water (AAIW) plays a key role in setting thermocline nutrient levels6. Accordingly, any fluctuations in the relative balance between Fe and major nutrients N and P in mode and intermediate waters in response to changes in climate will influence upper ocean Fe limitation and consequently modify global carbon and nitrogen cycles.Upper ocean primary production is limited by the availability of iron (Fe) over much of the ocean7. Thus, despite a relatively long legacy of modelling the ocean iron cycle9, significant uncertainties in the magnitude of the major processes remain10. This means that although shifts in Fe inventories may indeed drive end-of-century trends in simulated productivity across much of the global ocean15, confidence in model projections is diminished by the lack of mechanistic constraints on their behaviour.At present, there is low confidence in model projections of how modulations to climate will affect Fe supply to the upper ocean, as models generally show poor skill and substantial disagreement in their representation of the present-day ocean iron cycle. This lack of fundamental understanding of iron biogeochemistry is well illustrated by the order of magnitude inter-model variability in the residence time of iron in global models, despite aiming to reproduce the same ocean distributions from state of the art data sets16. In the past decade, continental margins and hydrothermal vents have been acknowledged to augment dust deposition as important external iron sources18. Perhaps, most striking has been the recognition that the internal cycling of iron is typified by a range of biotic and abiotic transformations linked to Fe uptake (by both phytoplankton and bacteria), recycling, regeneration, scavenging and colloidal dynamics19. These processes act to shuttle dissolved iron between soluble and colloidal phases22 and drive transitions of particulate iron between biogenic, lithogenic and authigenic  components24. Despite these new insights, the relative magnitude of regeneration and scavenging, and crucially, the realised rate of net regeneration, is unknown at the spatial and temporal scales of mode and intermediate water transport. In part owing to these missing constraints, global ocean models used to assess the response of ocean ecology, biogeochemistry and the carbon cycle to environmental change are free to tune their internal iron cycle with residence times that vary from a few tens to a few hundreds of years7.The ocean iron cycle is affected by an array of processes that interact together to set the dissolved iron concentrations in different parts of the ocean26 should facilitate model improvement, but only if quantitative insights into the governing processes can be determined. A particular challenge is to disentangle the balance between biogeochemical and physical processes in setting nutrient levels in the oceans’ interior. For example, total phosphate (PO4) at depth is made of up of two components: one associated with physical transport to depth (preformed PO4) and the other from the regeneration of P from organic matter degradation (regenerated PO4), which is quantified using apparent oxygen utilisation (AOU)28. A similar framework can be outlined for Fe, but Fe may be decoupled from P as it is affected by additional processes, such as extra Fe inputs onto intermediate water surfaces, unique regeneration of Fe, or Fe removal by scavenging29. Although scavenging of Fe will add complexity to the two-component model used for P, its magnitude remains an unknown quantity in observations. This lack of understanding is encapsulated by the evolving view of the ocean iron residence time from models and observations31.Newly expanded data sets of dissolved Fe (DFe) distributions from international ocean survey efforts within the GEOTRACES programme4, within the mode and intermediate waters of the south Pacific Ocean for the first time, using AOU to derive the role played by physics, regeneration and scavenging. We focus on mode and intermediate waters as they support the majority of global productivity through nutrient supply to surface waters6. This approach illuminates a highly dynamic interior ocean Fe cycle, within which the commonly measured DFe pool is only a small residual component. Consequently, additional measurements of the ocean iron cycle pools beyond DFe and in particular, Fe fluxes are necessary to better constrain internal cycling and reduce uncertainty in global climate model projections.Here we use observations to quantify the large scale modification of DFe, benchmarked to PO33 and their equatorward transport is well sampled by the southern part of the CLIVAR P16S cruise track along 150 °W , and enough parallel observations of DFe, PO4 and oxygen were available (n = 89). As intermediate water moves equatorward, its core depth varies between 200 m and 800 m and AOU increases from 20 to 160 mmol m−3 as the constituents transported within the watermass, or delivered via sinking from above, undergo further remineralisation  during this part of the P16S transect  of phytoplankton Fe is accumulating as DFe in intermediate waters owing to regeneration. It is possible that living phytoplankton are not representative of the sinking detrital pool39, which could be addressed by examining Fe/C ratios within bulk particulate matter. However, total particulate Fe also includes relatively inert lithogenic Fe, which would then overestimate the labile  Fe content. To account for this, we estimated lithogenic Fe (see methods) from the only GEOTRACES particulate Fe data set from the Pacific Ocean  using three different lithogenic models that account for a range of end-members from the Pacific basin40. After this correction, median non-lithogenic Fe/C ratios within all particulate samples, shallower than the lightest intermediate water isopycnal, range from 48.2 to 196.4 μmol Fe mol C−1, whereas the median P/C ratio is 12.73 mmol P mol C−1 (Table 4 (11.48 mmol P mol C−1) along the intermediate water pathway. This suggests that the simple two-component balance between regenerated and preformed pools that explains the internal cycling of PO4 is not applicable for Fe and the processes of subsurface DFe solubilisation during regeneration and removal via scavenging that control the net observable Fe remineralisation remain unconstrained.While the accumulation of POts Table  range frThere are three main hypotheses to explain the mismatch between accumulation of DFe and the magnitude of phytoplankton and particulate Fe stocks that fuel DFe replenishment. The first hypothesis states that particulate Fe is not exported from the surface ocean and is instead retained in the zone shallower than the upper bound of intermediate waters. The second hypothesis states that particulate Fe is exported out of the upper ocean but is not regenerated. Finally, the third hypothesis states that ample Fe is exported and regenerated, but strong removal (e.g. by scavenging) of regenerated Fe leads to minor accumulation of DFe.43, which indicate substantial turnover of the particulate Fe pool. Measurements of particulate Fe exported from the upper ocean from trace metal clean sediment traps are very rare, but, where available, also support substantial export of particulate Fe. Sinking particulate Fe flux data from the SAZ-Sense and Fe Cycle I and II (at ~100 m depth and either directly accounting for lithogenic Fe or taking a conservative 80% estimate of the lithogenic fraction44) results in non-lithogenic Fe/C export ratios of between 30 and 400 μmol Fe mol C−1 and P/C export ratios of ~6–8.5 mmol P mol C−1 across all data46  through a set of experiments that incubated a subsurface particle assemblage resuspended from McLane pump 142 mm filters and monitored the release of DFe, as well as by iron budget calculations. These estimates are also sparse, but for two distinct field experiments, dFe release rates range between 511 and 1314, and 120 and 460 nmol m49 and would also explain observations of an increasing association of sinking non-lithogenic particulate Fe with authigenic phases in deep-moored sediment traps  in the Atlantic50. For the Pacific, we calculate that 20–40% of the particulate Fe within the intermediate water in the western portion of the GP16 Pacific section cannot be accounted for by the sum of lithogenic and algal biogenic components. This implies a non-negligible authigenic particulate Fe component that would be consistent with removal of regenerated Fe by scavenging.Based on our rejection of the first two hypotheses, we are required to invoke a significant loss of regenerated Fe when considering hypothesis three. This would reconcile the low rates of dFe accumulation within intermediate waters with the significant export of non-lithogenic Fe and large rates of dFe solubilisation from sinking particles. The potential role of the removal of regenerated algal biogenic Fe has been previously observed using synchrotron-mapping of particles derived from sediment traps51, highlights the important role this may play in producing small particles. Similarly, number spectrum analyses (using underwater video cameras) across the upper 200 m of the water column in the S. Pacific Gyre reveal much higher abundances of small particles than larger ones52. As scavenging of trace metals like Fe is highly dependent on surface area55, these particle disaggregation/fragmentation processes can catalyse further scavenging of the dFe released by regeneration. Scavenging of regenerated Fe into authigenic phases may also enhance particle sinking rates by increasing the specific gravity of particles (as noted for lithogenic Fe56). These abiotic processes may act in concert with the removal of solubilised Fe by heterotrophic bacteria operating within particle microenvironments58. If we take our median estimated regeneration rate of dFe and the estimated accumulation rate of dFe  off Bermuda, and the concomitant opposite trend for large (>64 μm ESD) particles at depth62 and is not in agreement with our analysis. However, the muted increase in DFe we observe is very consistent with the apparent saturation of strong Fe-binding ligands by DFe pools in the south Pacific Ocean60. This would imply that strong Fe-binding ligands, rather than their weaker counterparts, may play a key role in shaping the dissolved Fe distribution in the oceans’ interior. An additional role may be played by the interplay between soluble and colloidal iron pools, which can also be part of the ligand pools22 and in the future it may be useful to compare the net regeneration derived from the DFe-AOU slope to observations of colloidal iron. Finally, we emphasise that the putative production of authigenic Fe from the DFe solubilised during regeneration, that we term here as scavenging, might not occur in the water column, but instead within particles and their associated microenvironments58 in a manner disconnected from the wider water column ligand pool.We observe a small, but significant, accumulation of DFe with time Fig. , suggest−1 from our analysis , with the remaining one-third due to regeneration28. In contrast to PO4, the proportion of the DFe pool controlled by internal cycling in intermediate waters  across the entire available GEOTRACES data set26 of DFe and AOU has a median value of 0.57  and hence upper ocean iron limitation, will be strongly sensitive to small changes in the gross fluxes that govern the net regeneration of Fe. For instance, the Fe content of upper ocean phytoplankton is highly variable and fluctuations due to changing iron supply or phytoplankton species composition will affect the gross regeneration fluxes. Alternatively, biological and chemical transformations of particles, strong iron-binding ligands, bacterial demand and/or iron speciation will modify gross scavenging rates. Both these examples would change the net regeneration rate and hence the relative supply of Fe to the upper ocean microbes. Our isopycnal framework also provides a mechanistic methodology to assess ocean biogeochemical models more rigorously in future model evaluation efforts. A new generation of in situ processes studies64. In brief, 15 mL aliquots of acidified  sample were spiked with 100 µL of an 57Fe isotope-enriched solution (Fe concentration of 177 nm) and UV-oxidised (>1 h). After cooling overnight, samples were buffered with ammonium acetate to pH 6.4 ± 0.2 prior to being passed through a column packed with Toyopearl AF-Chelate-65 M. Extracted Fe was subsequently eluted with 1 m HNO3 into 1 mL aliquots and analysed by high resolution-inductively coupled plasma-mass spectrometry (Thermo Finnigan Element 1). dFe concentrations were quantified using a standard isotope dilution equation. The analytical limit of detection  averaged 0.019 nm (n = 20) during the analysis period, while the procedural LOD  averaged 0.034 nm (n = 29). Accuracy and precision were assessed through the replicate extraction and analysis of SAFE and GEOTRACES seawater reference materials64. Typical within run precision averaged 2.2%  at iron concentrations ~1 nm and 11.8%  at lower iron concentrations (~0.1 nm). AOU was calculated from oxygen saturation . DFe, PO4 and AOU were binned within the intermediate water density layers (28.6–27.2) and between latitudes of 46 °S and 10 °S using P16S data. Statistics were performed using Type II regressions via the R package ‘lmodel2’. The net regeneration (FeREG’) that results from the near-balance between regeneration and scavenging is derived by combining the Fe/AOU slope from the P16 with AOU using oxygen, temperature, salinity and DFe data from the indepedent GEOTRACES IDP201726 between the 26.8 and 27.2 isopycnal layers that represent intermediate water. Field data from the P16 voyage is available from BCO-DMO65.Sampling along the CLIVAR P16 section was conducted during two cruises, from Tahiti to Kodiak, Alaska aboard the R/V Thomas Thompson , and from Tahiti to Antarctica aboard the R/V Roger Revelle . Samples for dFe were analysed following previously published protocols24 does not significantly alter their composition. To estimate and correct for lithogenic Fe we quantified the number and composition of potential lithogenic endmembers. The ratios of Al, Ti and Th were used to address the compositional gradients of lithogenic particles in the GP16 transect and estimate the fractional composition of each endmember , lithogenic , and authigenic sub-fractions, we estimate the authigenic, or scavenged, Fe (PFeAuth) by sequentially subtracting estimated lithogenic (PFeLitho), non-lithogenic (PFeNonLitho) and authigenic (PFeAuth) fractions via the following three balances: PFeTotal = PFeLitho + PFeBio + PFeAuth, PFeNonLitho = PFeTotal − PFeLitho and PFeAuth = PFeNonLitho − PFeBio. In this study, we based lithogenic Fe corrections on two assumptions: (1) lithogenic material in the ocean is ultimately derived from a crustal source(s) with estimable, fixed composition(s), and (2) lithogenic particles are refractory, meaning that elemental exchange with dissolved or other particulate pools during their marine residence times (weeks to years)Supplementary Information


They were screened rationally through different computational approaches to explore their potential applications as photosensitizers for dye-sensitized solar cells (DSSCs). Compelling photophysical properties such as electron injection driving force, electron injection time, and dye regeneration were studied for the isolated dyes under the DFT and TD-DFT frameworks. Index of spatial extent , the strength of charge transfer and separation along with the charge transfer process is explored. First principle approach including van der Waals density functional calculation of dye@TiO2 interface indicates that all of the designed dyes have optimal interfacial behavior. Bader charge analysis, partial density of state (PDOS), charge density and electrostatic potential difference calculation confirms that THQ7 and THQ9 are the most efficient dye-sensitizers. The other five designed dyes also possess the required properties to emerge as effective dye-sensitizers potentially better than those already utilized.Seven ‘lead’ dye-sensitizers from Tetrahydroquinoline (THQ) family were proposed and designed based on the structural attributes  In DSSC system, an organic photosensitizer is anchored to the wide band gap semiconductor and possesses a donor-π-acceptor (D-π-A) structure motif with well-developed intramolecular charge transfer (ICT) characteristics. The photoactive sensitizer of DSSCs absorbs sunlight in the form of the photon and photo excited dye injects electrons to the conduction band (CB) of the semiconductor such as TiO2. The performance of DSSCs relies on fundamental quantum properties like the lowest unoccupied molecular orbital (LUMO), the highest occupied molecular orbital (HOMO), and their distributions in the photosensitizers. Most significantly, the kinetics of the electron-transfer processes at the interface between the dye and the semiconductor as well the dye bound to the semiconductor surface and the hole-transporting substance9. Thus, it is imperative that the microscopic interfacial properties of organic metal-free dyes on TiO2 photoanodes need to be thoroughly investigated. This will elucidate the details of charge transfer and anchoring state at the interface, band gap offset of dyes, HOMO and LUMO shift, and their redistribution.Captivating advantages of high molar extinction coefficient, flexible assembling, low-cost fabrication, and environmental cleanliness has helped metal-free organic dye-sensitized solar cells (DSSCs) to gain considerable interest as an alternative renewable energy source over the last decadeIn silico modeling to design, followed by quantum and electrochemical parameters calculation, represents the complete scheme for the rational solar cell development prior to experiment13. Our research group has efficiently employed quantitative structure-property relationship (QSPR) approach for modeling and virtual screening of polymer-based solar cells for the very first time14. It was followed by first QSPR modeling of arylamine organic dyes (AOD) for DSSCs explicit to cobalt electrolytes that were recently reported5. In our previous study, we have modeled 273 AODs from 11 diverse chemical classes explicit to iodine electrolyte followed by quantum chemical analysis employed to understand the primary electron transfer mechanism and photophysical properties. Further, identified features from QSPR models were applied in designing of 10 dyes - each for Tetrahydroquinoline (THQ), N,N-dialkylaniline and Indoline, respectively maintaining required electrochemical parameters with encouraging enhancement in predicted percent power conversion efficiency (PCE) value13. It is important to mention that finally, based on requisite electrochemical and photo-physical parameters seven lead dyes have been screened from the ten designed dyes for each chemical class. Expanding the research work to the next level, in the present study, we have investigated the seven THQ lead dyes to explore the interfacial properties as well as further photophysical properties of the isolated dyes. Compare to Rubidium based organic dyes; metal free organic dyes are receiving more endorsement to devise DSSCs due to the advantages of molecular engineering including the possibility of tuning donor-acceptor of dyes, their broad optical absorption, and exceptional electrochemical properties17.Computational approaches have become an integral part of research in material science following their successes in drug design and discovery process. 19 reported on non-metal organic dyes-sensitizers. Computational approaches focused on the semi-classical20 and Density Functional Theory (DFT) approaches combined with Time-Dependent DFT (TD-DFT)24, have been used to explore the electron injection process at the interface of metal and non-metal based organic dyes. First principle approaches were carried out to study the characteristics of the designed THQ dyes before and after the adsorption on TiO2 surface to compare qualitatively different aspects of the complex phenomena. Initially, we have analyzed the crucial parameters of the isolated dye like open-circuit voltage, short-current density, electron injection efficiency, reorganization energy, injection time, and intramolecular charge transfer (ICT) parameters by DFT and TD-DFT calculations. On the next level, structural and electronic properties, charge density and electrostatic potential energy differences of the adsorbed system, variations in the band gap and projected density of state were computed with the use of plane-wave DFT including Hubbard correction. The encouraging outcomes of the performed study allow selecting new efficient THQ dye sensitizers for DSSCs to obtain higher photo-to-electron conversion efficiency.Though a substantial number of studies have been directed towards metal-based organic dyes, only a limited workJSC and VOC are the most relevant parameters to calculate theoretically with precision to compare its performance as an efficient sensitizer. Electron injection mainly depends on the absorption capacity of photon, ground state oxidation potential and optimum HOMO-LUMO energy gap of the respective dye. Parameter like JSC can be estimated in terms of ΔGinject and the computed critical energy parameters25 of the present study are tabulated in Table The seven lead dyes from the THQ family studied by us are represented in Fig. Ginject for all of the cases are negative which implies the spontaneity of the electron injection process. Also, Islam et al. proved that the injection efficiency of the electrons in the excited state (Φinject) tends to approach 1 when ΔGinject is greater than 0.20eV26. THQ1 (−1.18 eV) is showing the highest efficiency while THQ7 (−0.93 eV) displays the lowest one. The sequence of dyes considering ΔGinject is: THQ1 > THQ2 > THQ5 > THQ8 > THQ3 > THQ9 > THQ7. Comparable assessments of dyes regeneration efficiency have been calculated (See equation in methods section) and the computed values of ΔGregn of all the dyes are negative which implies the feasibility of the process. The values of ΔGregn vary from 9.79 to 10.9 eV that is much higher than the accepted value of ΔGinject and confirming that the dye regeneration process is more favorable compared to electron injection27. The THQ9 with the lowest ΔGregn value suggests that it will minimize the undesirable charge recombination between oxidized dye and photoexcited electrons of the dye molecule efficiently, due to the presence of diketopyrroleopyrrole (DPP) unit with one benzene ring.The obtained values of ΔVOC) that can be expressed28 by the Equation  redox couple of electrolytes, and ii) oxidized dyes2. Richards et al.33 established that electron recombination to the electrolyte dominated by the free iodine concentrations and thus I2 is more inclined to bind with heteroatoms  of dye molecules via non-covalent halogen bond interaction. To estimate the electron recombination on TiO2 surface, we considered three different configurations of the isolated-dye…I2 complex. Three configurations are: (a) I2 (IA − IB) binds with the N of CN moiety; (b) S atom of thiophene ring (S1) near acceptor units binds with I2 (IC − ID) atom; and (c) S atom of thiophene ring (S2) far form acceptor units binds with I2 (IE-IF) atom. The optimized structures of dye…I2 complex for all the seven dyes are depicted in Fig. 2 complex, intramolecular I-I bond lengths are larger than the covalent radii (2.86 Å), which imply the iodine dimers are weekly bound and the distance of CN and iodine (CN–IA) atom is smaller than the net van der Waals radii 3.53 Å but larger than the net covalent radii 2.03 Å. Also, the bond lengths between thiophene S and iodine (S–IC/E)are larger than the covalent radii, while smaller than the net van der Waals radii (3.78 Å) of the binding atoms. Similar outcomes was also observed by Xu et al.34.Electron recombination is one of the most important factor which reduce ene ring  near accA bond lengths is: THQ9 > THQ7 > THQ8 > THQ2 > THQ1 > THQ3 > THQ5 while the length of IA-IB is following: THQ5 > THQ3 > THQ1 > THQ2 > THQ8 > THQ7 > THQ9. Moreover, the sequence of S1–IC bond length (except THQ1 and THQ2) is THQ5 > THQ7 > THQ8 > THQ9 > THQ3 and the trend for IC-ID is: THQ3 > THQ9 > THQ7 > THQ8 > THQ5. In both cases, the interaction of dye and iodine is increasing with the increase of separation distances of iodine. On the other hand, THQ1 and THQ2 are showing different trend in case of S2–IE bonds. It might be possible due to the presence of two different thiophene rings, as strongly supported by literature34. Based on above analysis, it is quite difficult to choose which dye will transfer the I2 away from the semiconductor (TiO2) surface and help to reduce the charge recombination by inhibiting the local concentration of I2 on TiO2.The trend of CN–I35. Index of spatial extent method is used to describe the ICT process qualitatively. Spatial index including distance between two barycenters of the density depletion and density accumulation distribution upon excitation (DCT) in Å, transferred charge (qCT) in e, and overlap integral (S) are reported in Table DCT which confirms proper charge separation of donor and acceptor moieties35. The predicted trend of S is as following: THQ9 > THQ7 > THQ8 > THQ2 > THQ1 > THQ3 > THQ5, while DCT displays the following tendency: THQ7 < THQ9 < THQ3 < THQ2 < THQ1 < THQ5 < THQ8. The increased value of S implies that the addition of DPP unit as an additional donor and acceptor moiety makes the smooth flows of electron transfer from the ground state to excited state. However, from the above sequence, it is not possible to select which lead dye will be more efficient for DSSCs based on ICT. Again, upon the analysis of the amount of charge transferred, the qCT sequence of dyes is as following: THQ1 > THQ9 > THQ2 > THQ3 > THQ7 = THQ8 > THQ5. There are some literature evidences that also claimed advantages of DCT and S which may reduce the charge recombination of the sensitizers, besides excellent charge separation36. If one investigates the Fig. C+) or donor and the centroid of electron (C−) or acceptor.Built-in push-pull structure in the designed dyes possesses significant charge separation that confirms the high overall efficiency of photo-to-electron conversion37 by calculations of the charge transfer rate using the following equation:λtotal, all the parameters on the right-hand side are constant. Thus electron transfer rate constant (kET) depends only on λtotal, which is the sum of hole reorganization (λh) and electron reorganization (λe) energy, whose values can be estimated by the Equations  is a crucial factor to modulate the charge transfer phenomenon efficiently. This can be estimated by the relationship from Chaitanya et al.38E (cm1−) is the excitation energy for S1 and f is the oscillator strength for the respective state. The dye with a more extended lifetime in the first excited state is predicted to transfer charge efficiently39. Our calculations of charge transfer shows the following trend: THQ5 (3.42 ns) > THQ7 > THQ9 > THQ8 > THQ3 > THQ2 > THQ1 (1.42 ns). The difference between the redox potential and the EHOMO13, as well the energy gap of the sensitizer affect the lifetime to reduce undesirable reaction like charge recombination40. On the basis of Marcus theory and lifetime in the excited state, we can preliminary comment that among the seven designed lead dyes THQ5, THQ7, THQ8, andTHQ9 are more proficient than THQ1, THQ2, and THQ3.Difference between the 2 and charge transfer phenomenon are also important factors to determine the efficiency of DSSCs, which are computed and thoroughly discussed in the next section.Based on the above analysis, we can firmly confirm that all of the designed dyes have the desirable optoelectronic properties including electron injection and lifetime in the excited state, open circuit voltage, ICT parameters, and regeneration of the dye itself. However, we are not able to conclusively comment on the overall performance because the effectiveness of isolated dye is not the only factor to determine the efficiency of DSSCs. The adsorption characteristic of the dye, the interface of dye@TiO2 (101) surface, we have calculated the adsorption energy, 3D charge density difference (CDD) plot, Bader charge analysis, planar averaged electrostatic potential and CDD with respect to Z-direction, and partial density of states (PDOS) using first principle approach.To elucidate the stability and interfacial charge transfer phenomenon of the designed THQ dyes on anatase TiO2 surface. In literature, three different types of binding such as monodentate, chelated and bridged bidentate41 are prevailed and later one being the most stable42. Thus in the present study, the equilibrium interface geometry and adsorption energy of the dyes on anatase TiO2(101) surface were calculated based on the bridged bidentate bonding. Optimized configurations of the interface for all the dyes are illustrated in Fig. 2 (101) surface (positively charged)44. Geometrical parameters and adsorption energy for the different systems were calculated and reported in Table 2 surface and a dye. In our study, THQ9 is showing highest adsorption energy (−3.77 eV) whereas THQ5 having the least (−1.42 eV) energy. The adsorption energy is calculated according to the Equation  slab energy, respectively. The adsorption energy has been computed by the equation  surface decreased from 1.70 eV to 0.40 eV  for dye@TiO2 system; while the range is 0.40  to 1.25 eV (THQ3/TiO2) for all studied composite Dye@TiO2 systems. Band gaps for THQ1, THQ2, THQ3, THQ5, and THQ9 are 0.90 eV, 1.01 eV, 1.25 eV, 0.53 eV and 0.42 eV, respectively. The narrow band gap of specific dyes can be explained due to the lower order occupied orbitals of the dyes. Thus this characteristic helped in moving the conduction band towards lower energy. For all the cases, LUMO of the adsorbed dyes is coupled with the CBM of TiO2 surface that allows intermolecular electrons to be excited to the surface and increases the overlap between the LUMO and CBM, improving the electron injection process. Also, inner LUMO of the dye expedites electron injection process. In Fig. 2 semiconductor, which means that dye introduces few pi-occupied levels on top of the TiO2 valence band. The newly pi-occupied levels from the dye become the new top-part of the valence region. In case of DI293 , the band gap is 1.0 eV  surface was investigated with the use of first principle approach with van der Waals density functional, in conjunction with on-site Coulomb interaction for Ti 3d orbital. Outcomes from the first step can be summarized as follows: THQ9 emerged among the top three dyes concerning charge transfer characteristics  while giving lower value for open circuit voltage (VOC), and electron injection energy (ΔGinject) than other considered dyes. Furthermore, three different binding modes and their structural attributes of dye–I2 complex are evaluated to address electron recombination process. Analyzing dye regeneration efficiency, THQ3 shows the highest value. Based on calculations for the isolated dyes, it can be concluded that all of the considered dyes possess potential properties to become an efficient dye-sensitizer for DSSCs. Introspecting the geometrical interface structures, PDOS, planar averaged charge density and potential energy difference of dye@TiO2 system; we infer that there is robust electronic coupling between all dyes and TiO2 surface. The PCEs of THQ7, THQ8, and THQ9 dyes are better than for the other four dyes when dye@TiO2 are considered, due to their lowest adsorption energy, lower band gap and higher Bader charge. Interestingly, the other four dyes also possess all required electrochemical parameters to act as efficient and improved dye sensitizers for DSSCs, compared to the products available on the market. Therefore, the insertions of double bonded oxygen to the aromatic carbon certainly improve the photovoltaic performance and energy conversion efficiency of sensitizers. In conclusion, based on the comprehensive analysis, modeling and design followed by the structural and electronic properties evaluation of the lead dyes, we proposed them as efficient, potential sensitizers for future DSSCs with improved PCE.In summary, we have performed systematic, comprehensive investigations to evaluate seven ‘lead THQ dyes’. These dyes were selected by our group in previous study as characterized by high-predicted %PCE. The current work was carried out to reveal the relation between dyes’ structure and their photo-conversion efficiency. The optic-electronic properties of the isolated dyes were computed by DFT and TD-DFT with solvent effect, whereas necessary. Interfacial properties of the adsorbed dye on TiO48. Ground state and excited state calculations were done with B3LYP and CAM-B3LYP functional within DFT and TD-DFT framework, respectively. It is notable to mention that CAM-B3LYP functional for the TD-DFT method provides reasonable prediction for the excitation energies and the absorption spectra due to its long-range coulomb-attenuating method49. In all the cases, 6–31 g basis set is used for C, H, O, N, and S atoms while LANL2DZ effective core potential (ECP) for I atoms. For the ICT parameters and to visualize centroids calculations were performed by code Multiwfn 3.3.950. The overall efficiency of photo-to-electron conversion in DSSCs is determined by the integral of short-circuit photocurrent density (JSC), open–circuit photo voltage (Voc), fill factor (FF) and incident solar power on the cell1:Geometry optimization and estimation of all photophysical parameters of the isolated dyes were performed in Gaussian09 packageJSC depends on the absorption coefficient of the dye and the interaction between the dye and the nanocrystalline TiO2 surface. It can be computed by the following equation:LHE is light harvesting efficiency at wavelength λ, Φinject is quantum yield for electron injection efficiency and ηcollect is the charge collection efficiency which only depends on the architecture of DSSCs while other parameters strongly depend on the structural and quantum phenomenon of the dye. Katoh et al.51 related injection efficiency to the free energy change for the electron injection (ΔGinject) that can be defined through following equation:2). The max) from the ground state oxidation potential (Gregn) between electrolyte and oxidized dye via the following expression:2 surface and the interface of dye@TiO2 were performed using the Vienna ab initio Simulation Package (VASP)54 code under generalized gradient approximation (GGA). To account for the van der Waal (vdW) interactions, non-local density functional like vdW density functional, optB86b-vdW55, was employed as implemented in VASP by J. Klimeš et al.57. A Hubbard onsite Coulomb repulsion term58 was added (DFT + U) to improve the description of the on-site Coulomb interaction in the Ti-d states with an effective U parameter. It is crucial to choose effective U parameter, as the band gap of anatase is directly proportional to the U parameter with the overestimation of lattice description59. Our study also follows the same trend. Based on the previous work60 by our group and Paes et. al.61 study we choose effective U value of 3.5 eV.The 2 system, we describe C-1s2, O-1s2, N-1s2, Ti-[Ne]3s2 and S-[Ne] core electrons with the projector augmented wave (PAW) pseudopotentials62. The cutoff energy of the plane-wave basis set was 400 eV with a Γ centered Monkhorst-Pack grid of 0.03 Å−1 separation -c(4 × 2) supercells, respectively). The total energy converged to <1 meV/atom. All the structures considered in this study were fully relaxed until all the forces and energy were less than 0.025 eV/Å and 10−6 eV/atom, respectively. The DFT + U computed optimum lattice parameter of bulk anatase TiO2 were a = 3.85 Å and c/a = 2.52 Å; while the indirect band gap is lower in the order of 2.4 eV (direct band gap 2.9 eV) compared to experimental value of 3.2 eV. The band structure, corresponding Brillouin zone with direct and indirect band e gap are shown in Fig. 2(101) surface was modeled with three O-Ti-O layers along the [001] direction and 15 Å vacuum thickness between slabs resulting in approximately 25 Å dimension in [001] direction.In the dye@TiOThe results of calculations can be obtained by request from the authors.Supplementory information


Opn1mwR), we generate conditions that differ in color  while providing identical melanopsin and rod activation. When biased toward S-opsin activation (appearing “blue”), these stimuli reliably produce weaker circadian behavioral responses than those favoring L-opsin (“yellow”). This influence of color  aligns with natural changes in spectral composition over twilight, where decreasing solar angle is accompanied by a strong blue shift [In humans, short-wavelength light evokes larger circadian responses than longer wavelengths , 2, 3. Tue shift , 10, 11. •Cone-derived blue:yellow color signals influence circadian entrainment in mice•The blue colors associated with twilight suppress circadian light responses•Color signals support circadian entrainment to low-amplitude light:dark cycles•Color signals buffer the clock against cloud-related changes in light levels Changes in the spectral content of ambient light are detectable to most mammals as a blue shift in the color of twilight. Mouland et al. show that these “blue” colors suppress circadian responses to light, supporting robust circadian entrainment when environmental conditions render light intensity a weak or unreliable indicator of time of day. Cone-derived color signals reach the suprachiasmatic nuclei (SCN) and can influence clock phase , but it R; hereafter termed red-cone) [The mammalian circadian system tracks light intensity via a combination of melanopsin and outer-retinal signals relayed by intrinsically photosensitive retinal ganglion cells (ipRGCs) , 14, 15.ed-cone) , where tWe started by establishing a housing environment that provided diffuse overhead illumination from independently controllable light-emitting diode (LED) sources A. We theWe first used this approach to evaluate the impact of color on the circadian period of voluntary wheel running under constant illumination, a paradigm used extensively to assess the impact of light on the mammalian clock . Here, Interestingly, another common impact of increasing light intensity on mouse behavior, compression of activity bout duration (α), was not similarly impacted. Hence, although there was a robust decrease in α as a function of intensity, we did not detect any significant influence of color D. This mCnga3−/− mice [Because our experimental stimuli selectively modulate the ratio of L- to S-cone opsin activation, circadian behavior should be indistinguishable under equi-luminant L+S− and L−S+ conditions in animals that lacked cone phototransduction  illumination simply reflected a reduction in effective cone illuminance, circadian periods should be even more reduced under the L−S− (dim) condition. As above, we once again found a significant decrease in circadian period under L−S+(blue) versus L+S−(yellow) illumination , we first presented 2-week blocks of L+S−(yellow) and then L−S+(blue) stimuli followed by blocks of two additional stimuli of intermediate color  but varying cone illuminance  behavioral rhythms in response to acute pulses of L+S−(yellow) versus L−S+(blue), presented immediately following transfer from a light:dark (LD) cycle to constant dark. We chose this approach to avoid long-term adaptation effects that might accompany testing under constant dark housing (where resetting responses are dominated by rod contributions) . With thBecause we identified clear effects of color under much longer durations of illumination than those used above , we nextOur data provide a straightforward mechanism by which color signals could aid circadian entrainment—by reducing responses to light whose color is indicative of late stages of twilight. To probe the ecological significance of this mechanism, we next established a new housing environment that allowed more dynamic control over the intensity and color of illumination and fitted this with passive infrared sensors that detect even small wake-related behaviors rather than just daily variations in locomotion .Using this system, we first asked whether a primary function of color input was to support circadian entrainment when diurnal changes in light intensity are small. In the natural world, especially in the regions where the ancestors of laboratory mice evolved, this is an uncommon circumstance. Nonetheless, such a possibility has been proposed to explain how some animals maintain entrainment during the arctic summer, where daily variations in light intensity are very markedly reduced . MoreoveIn initial experiments, we evaluated whether mice could maintain entrainment in the presence of large diurnal variations in color without any associated change in light intensity. Accordingly, we first entrained mice to a conventional 12 h:12 h LD cycle and then replaced the light phase with either L+S−(yellow) or equi-luminant L−S+(blue) and the dark phase with the opposite color A. In botWe next then investigated whether daily changes in color would facilitate entrainment to very low amplitude diurnal variations in light intensity by generating two new sets of lighting conditions. The first provided a modest (0.75 log unit) daily variation in intensity for melanopsin and rods  were then initially entrained to a 16 h:8 h LD cycle (providing stable daily changes in color and light intensity) and thereafter experienced naturalistic daily variations in light intensity with simulated clouds that included or lacked the associated variations in color A and 4B.This observation could reflect either a simple reduction in the magnitude of daily rhythms in the absence of color or an increase in the day-day variability of these activity patterns. Subsequent analyses implicated both factors. Hence, the percentage of daily activity that occurred outside the night was increased under intensity-only F, while In summary, naturalistic diurnal variations in color confer enhanced robustness and stability to daily activity patterns in the face of weather-related fluctuations in light intensity. To determine whether this results from a specific impact on the circadian control of activity, interspersed between blocks of natural and intensity-only days, we also included 24-h epochs of constant dim illumination of intermediate color A and 4B.Contrary to common beliefs, it is yellow rather than blue colors that have the strongest effect on the mammalian circadian system. This relationship aligns with natural shifts in the color of ambient illumination, detectable during twilight by mammals with di- and tri-chromatic visual systems . AccordiTheoretically, reduced circadian responses to blue colors could arise indirectly as a result of chromatic changes in pupil diameter; however, our previous work indicates that chromatic blue:yellow modulations do not produce observable pupillary responses in mice . By contAlthough the observed reductions in circadian responses to stimuli that resemble twilight are logical from an ecological perspective, this effect is surprising given the overall positive relationship between SCN firing and circadian resetting , 34 and An especially pertinent question, however, is whether the effects of color described here extend to other mammals, such as humans. The qualitative relationship between sun position and blue-yellow color should be retained for any mammal capable of color vision , and theSuch an arrangement is potentially important for practical approaches intended to adjust the circadian impact of artificial light. Current approaches typically rely on manipulating the ratio of short- and long-wavelength light, achieving modest differences in melanopic illuminance at the expense of perceptible changes in color . As a reTimothy.Brown@manchester.ac.uk).This study did not generate new unique reagents. Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Tim Brown (Opn1mwR [Cgna3−/−) [All experiments received institutional ethics committee approval and in accordance with UK Animals (Scientific Procedures) Act 1986, and European Directive 2010/63/EU. Adult (> 8 weeks) male mice from a C57BL/6 background strain were used throughout. For the majority of the experiments we used mice expressing the human L-cone opsin in place of their native M-cone opsin . Additgna3−/−) ).ad libitum food and water. Cages were located in light tight cabinets where the ambient light could be carefully controlled. Under several paradigms  mice were also housed with access to a running wheel to assess locomotor activity.Mice were individually housed with One light tight cabinet  and 6 violet bulbs , to allow for 5-primary illumination. A PTFE diffuser was mounted to floor of light boxes/roof of the cabinet and the interior of the cabinet was painted white to provide uniform illumination. The LIFX bulbs were connected wirelessly over a local network, UV bulbs were connected to LED drivers  via a multichannel analog output module . LED intensities were then controlled on a second by second basis using a PC running Python (2.7.10). Neutral density gels were added to each box to adjust the overall brightness as required.Stimuli were presented via a custom light source (components from Thorlabs) consisting of three independently controllable LEDS  combined by dichroic mirrors. Light stimuli were then and projected (via a concave lens) onto a 1.5mm thick PTFE diffusing sheet (Direct plastics) that sat on top of a cylindrical chamber coated with spectrally neutral reflective paint .Stimuli were designed and calibrated as described previously , 17 usinFor experimental paradigms 1-3 outlined below  and L+S-(‘yellow’) conditions (see spectra and quantification in Opn1mwR mice aged 10-15 weeks) were housed under constant light conditions (at ND1) and received 2 successive 2 week blocks of L+S-(‘yellow’), L-S+(‘blue’), L-S-(‘dim’) and L+S+(‘bright’) cone selective stimuli . After one week the mice were then subjected to a 6 hour phase delay or phase advance with the light phase rendered in either L-S+(‘blue’) or L+S-(‘yellow’) as in Eight Opn1mwR mice aged 6-16 weeks were housed under standard 12h:12h LD for 2 weeks. Subsequently, during early (ZT14-15) or late night (ZT21-22) mice were transferred to a test chamber (described above) where they were illuminated for 5 minutes with L-S+(‘blue’) or L+S-(‘yellow’) illumination (spectra and quantification in Sixteen Opn1mwR mice (n = 6/condition) aged 16-19 weeks were housed for a week under a 12h:12h LD cycle . After entraining to these conditions, the LD cycle was changed to a 12h:12h ‘yellow’:’blue’ or ‘blue’:’yellow’ cycle (spectra and quantification in Opn1mwR mice aged 10-11 weeks were housed for a week under a 12h:12h LD cycle (day component was the ‘mel/rod+’ stimuli from Six Opn1mwR mice (11-13 & 15-18 weeks) were used for this experiment. Mice were housed under a 16h:8h lighting cycle that provided a daily variation in color and intensity resembling changes occurring between solar elevations of +6 and −4 degrees relative to the horizon , with criteria for significance set at p < 0.05. Sample size and the other relevant statistical details are provided in the main text.2-periodogram [2, 13 = 36.2; p < 0.0001) but not color  or interaction with irradiance .Circadian period was determined by χiodogram , performFor comparison of circadian period effects of color and-cone illuminance signals , color , interaction ; Redcone Delay (Offsets) - Time , color , interaction ; Redcone Advance (Onsets) - Time , color , interaction ; Redcone Advance (Offsets) - Time , color , interaction .Circadian phase was assessed as the midpoint between activity onset and offset on each day (respectively defined as the start of a 30min epoch where activity exceed the daily mean and the start of a 90min epoch where activity fell below the daily mean). For analysis, circadian phase markers for each mouse were normalized by subtracting the mean for the 5-day epoch preceding a shift in the LD cycle. The data for L-S+(‘blue’) and L+S-(‘yellow’) stimuli under each condition were then analyzed by two-way RM ANOVA, with Sidak’s post-tests where we detected main effects of color or color X time interactions. For analyses of red-cone mice there were no exclusions. For analyses of coneless mice, one animal was excluded from analysis of delay shifts due to poor running during the first LD epoch that prevented reliable determination of starting phase prior to ‘blue’ delay. Analysis performed as described above but using just onsets or offsets revealed qualitatively equivalent impact of color to that reported in the manuscript: Redcone Delay (Onsets) - Time , by extrapolation of a line of best fit through activity onsets to the day of the pulse. Values reported in the manuscript are the average of those obtained by the three investigators . Subsequent analysis performed by unpaired t test. Data were excluded from this analysis in a few cases when variability in activity onsets made phase-shifts hard to reliably measure (as assessed by at least two of the investigators); this was the case for 1 individual following ‘blue’ delay, 1 individual following ‘blue’ advance and 3 individuals following ‘yellow’ delays .2-periodogram (as above). For statistical analyses period estimates obtained under LD conditions and following the subsequent experimental manipulations were compared against each other by paired t tests and against an expected period of 24h by one-sample t tests. No data was excluded.In all cases, circadian period was determined by χn representing the total number of data points, P the number of time bins):Analyses of diurnal activity patterns were based on the last 48h of each 3 day block (to reduce effects associated with the transition from preceding stimuli), for a total of 10 days of matched ‘natural’ and ‘intensity-only’ cycles for each individual. Analyses of circadian activity patterns were based on the 24h epochs of constant routine immediately following ‘natural’ and ‘intensity-only’ stimulus blocks, providing a total of 5 days for each condition per individual. To determine mean activity waveforms, PIR reported activity profiles for each individual (10 min bins) were first smoothed by a 2h running mean. We then averaged across the relevant days (as indicated above) and normalized by subtracting the daily mean and dividing by the maxima of the resulting time series. Onsets and offsets were determined from daily mean crossings of the 2h smoothed time-series, all other analyses were performed on unsmoothed data and analyzed by paired-test between ‘natural’ and ‘intensity-only’ conditions (or subsequent constant routine as appropriate). Interdaily Stability (a measure of rhythms robustness) was assessed by quantifying the fraction of variance that was accounted for by a stable 24h rhythm  using thpatterns ,) was astimothy.brown@manchester.ac.uk).Raw data and analysis code will be provided upon request by the Lead Contact, Tim Brown (