Nonetheless, no significant difference was observed in MRD levels across the responses in groups of patients with IIMM  in LCMM, but not in IIMM . Our results show that sCR represents a deeper response that correlates with longer survival in patients with LCMM, but not IIMM.Stringent complete response (sCR) is defined as a deeper response than complete response (CR) in multiple myeloma. Whether achieving sCR correlates with better survival remains controversial. We evaluated the outcomes in patients with intact immunoglobulin multiple myeloma (IIMM) and light chain multiple myeloma (LCMM) who achieved a very good partial response (VGPR) or better. Multicolour flow cytometry was used to assess the depth of response. LCMM patients with sCR had significantly lower measurable residual disease (MRD) levels than those with CR (median MRD: 7.9 × 10 In the latest International Myeloma Working Group (IMWG) response criteria, CR with a normal FLC ratio (rFLC) was classified as stringent CR (sCR) and categorized as a deeper response than CR4. Several studies, including our previous report, describe the advantage of achieving sCR to prolong the time to next treatment (TNT) or overall survival (OS). However, in other studies, achievement of sCR had no impact on OS or TNT10. Owing to these contradictions, the impact of achieving sCR on the survival of patients with MM remains controversial. However, many studies show that measurable residual disease (MRD)-negativity after treatment could improve progression-free survival (PFS) or TNT13.Serum free light chain (FLC) measurements are widely used to diagnose and assess treatment response in patients with multiple myeloma (MM). Owing to recent developments in effective novel agents and their combinations for MM treatment, an increasing number of patients achieve complete response (CR), leading to their prolonged survival16. MFC is the most widely used technique to evaluate MRD in clinical trials and routine clinical practice because it is cost-effective and quicker than ASO-PCR or NGS17.  Despite sCR being a deeper response than CR, its influence in terms of MRD on longer survival in patients with symptomatic MM has not yet been demonstrated, especially in real-world clinical practice. Furthermore, the impact of rFLC normalization at the time of CR may differ between intact immunoglobulin multiple myeloma (IIMM) and light chain multiple myeloma (LCMM). Therefore, MRD levels in the same IMWG response categories may not be the same for IIMM and LCMM. MRD assessment allows for significant variability in the depth of response among patients with MM, even in those who achieve CR or sCR. Here, we present the relationship between the depth of MRD levels and IMWG responses in patients with IIMM and LCMM who achieved very good partial response (VGPR) or better.MRD-negativity evaluated using multicolor flow cytometry (MFC), allele-specific oligonucleotide-polymerase chain reaction (ASO-PCR), or next-generation sequencing (NGS) is broadly used in many clinical trials as an optimal biomarker of better survival in patients with MMP < 0.01).The baseline characteristics were similar across patients achieving VGPR, CR, and sCR from LCMM and IIMM Tables  and 2. SP = 0.83; CR vs. sCR, P = 0.04; VGPR vs. sCR, P = 0.04; Fig. P = 0.59; CR vs. sCR, P = 0.10; VGPR vs. sCR, P = 0.24; Fig. −4 vs. 5.6 × 10−5, P < 0.01); however, no difference in MRD was observed between patients with VGPR and CR  rates were 5.5%  and 12.2% , respectively.The median level of paraprotein in patients with LCMM was 2375 mg . MRD assessment using 6-color flow cytometry was performed in 24 (57.1%) LCMM and 64 (52.8%) IIMM patients, whereas 8-color flow cytometry was performed in 18 (42.8%) LCMM and 57 (47.1%) IIMM patients. Fewer than 50 abnormal plasma cells were detected in 35 samples in IIMM  and in 13 samples in LCMM . Best responses of VGPR, CR, and sCR were achieved by seven (16.6%), seven (16.6%), and 28 (66.6%) patients with LCMM; and by 27 (22.3%), eight (6.6%), and 86 (71.0%) patients with IIMM, respectively. The median OS in LCMM patients with VGPR and CR was 58 and 41 months, respectively, while it was not reached among patients with sCR . Based on the ROC analysis, the MRD cut-off value used for OS analysis was 1.0 × 10−4 for both LCMM and IIMM  in LCMM  and 50.4% (61/121) in IIMM . Patients with LCMM who achieved MRD levels < 1.0 × 10−4 had a significantly longer OS than those who did not  to elucidate whether achieving sCR showed a clearer improvement in OS than did other known risk factors. We included the ISS rather than the revised-ISS (R-ISS) because high-risk CA is included as a variable of R-ISS. Achieving sCR did not show risk reduction  among patients with IIMM, but showed significant risk reduction  among patients with LCMM , MRD < 1.0 × 10MM Table  and S4.18. The current study confirmed our previous observation that MRD levels during sCR were significantly lower than those during CR or VGPR in patients with LCMM. However, these levels were not different between CR and sCR in patients with IIMM. Additionally, in line with the MRD levels, we observed that in patients with LCMM, but not IIMM, achieving sCR versus CR prolonged survival. Quantitatively, residual MM plasma cell levels were 1-log higher in CR than in sCR in patients with LCMM , whereas MRD levels during VGPR, CR, and sCR did not differ among patients with IIMM. The current definition of CR could explain these differences in patients with LCMM, which is a negative urine immunofixation indicating the disappearance of monoclonal light chain in the urine. However, the amount of light chain excreted in the urine is affected by the excretion and reabsorption capability of the kidneys, which in turn affects light chain metabolism19. As previously reported, serum FLC measurement was more accurate for monitoring and assessing tumour plasma cell burden in LCMM; the findings using this approach translated to better survival in LCMM patients with sCR and identification of the risk group in LCMM patients with CR. Nevertheless, serum FLC levels do not always reflect paraprotein levels in IIMM, offering an additional explanation for the lack of a difference in MRD levels between patients achieving VGPR, CR, or sCR. This may also explain why previous studies comparing survival between CR and sCR have conflicting results10.This study extends and updates our previous observations on the different depths of MRD among IMWG responses in patients with LCMM and IIMM. We previously showed that the attainment of sCR in LCMM indicated deeper MRD levels, but MRD levels in IIMM did not differ between CR and sCR−4 could discriminate OS in patients with LCMM, but not IIMM, in this study. Patients with IIMM with ≥ VGPR who had < 1.0 × 10−4 MRD tended to have a longer OS compared to those with higher MRD; this observation suggests that a deeper MRD level is needed for the prognostic relevance in IIMM. Nonetheless, a favourable predictive value of low sensitivity MRD negativity combined with conventional CR criteria are reported in a Medical Research Council Myeloma IX study12. Moreover, the survival benefit of achieving a negative MRD status at the MRD level of 1 × 10−4 has been confirmed in pooled clinical trials20. Furthermore, MRD negativity defined at > 10−4 to 10−5 surpasses the prognostic value of achieving PFS and OS, regardless of the type of treatment and patient risk group21. Similar to our study, this report also used an MRD value of 1.0 × 10−4 to discriminate survival. Recently, a highly prognostic predictive value was reported in patients achieving a deeper MRD response (< 1 × 10−6) irrespective of the presence or absence of poor prognostic features and high-risk CA using NGS22. In addition, an intense level of MRD negativity (< 1.0 × 10−6) is also reported as a strong prognostic indicator regardless of treatment, cytogenetic risk, and ISS stage23. Although NGS allows the detection of a deeper response that may be more relevant to longer PFS or OS, these methods are expensive and time consuming. Another limitation of detecting a deeper response using NGS is the requirement of a large number of bone marrow cells (at least 1.5 × 106)24. Most patients with MM are of advanced age and are treated intensively; therefore, the resultant bone marrow hypoplasia makes the collection of enough cells for analysis challenging. However, sCR determined using the FLC assay could easily identify the subset of patients with LCMM with favourable prognosis in terms of MRD levels.A MRD cut-off level of 1.0 × 10−5. Although 8-color flow uses more antibodies than 6-color flow and the lower limit of detection used to define MRD negativity between 6- and 8-color flow cytometry differ, tumour-quantified MRD levels assessed by the two methods showed a good correlation. , and the background clinical characteristics were similar among patients with IIMM and LCMM who achieved response in each category. Another limitation is not using the EuroFlow, which is considered to be the standard for MRD analysis. Because the retrospective study period extended back to 2008, we instead used the less sensitive 6-color flow cytometry in a subset of patients diagnosed between 2008 and 2016. We also used DuraClone, which is a newly developed 8-color flow cytometry method that allows a sensitivity of 1.0 × 10In conclusion, this study demonstrated variable MRD levels in patients with IIMM and LCMM within the same IMWG response categories. We also showed that achieving sCR represented a deeper response and favourable prognosis in patients with LCMM, but not in those with IIMM.4 was adopted for assessment, and serum FLC and MRD evaluated at best response were used for analysis. In the determination of CR/sCR, we confirmed the disappearance of any soft tissue plasmacytomas by 18F-FDG PET/CT and the absence of clonal cells in bone marrow biopsy by immunohistochemistry in accordance with IMWG response guideline4. Because serum FLC did not necessarily correlate with the level of monoclonal immunoglobulin (Ig) levels in IIMM, the disappearance of the monoclonal band did not correlate with rFLC normalization. While FLC is a primary monoclonal protein in LCMM, its decrease is usually associated with rFLC normalization, although there are some exceptions. Therefore, we separated the patients into two groups, LCMM and IIMM, and compared the OS between each response category. To clarify whether achieving sCR contributed to longer survival, MRD levels between each response category were also evaluated to confirm whether sCR was a deeper response than CR in each group. We also analysed whether the reduction of MRD levels contributed to the longer survival in the included patients in real-world settings.The medical records of 206 patients with symptomatic MM  who were admitted at Kameda Medical Center, Japan, between April 2008 and December 2019 were reviewed. Treatment was selected at the physicians’ discretion and in agreement with the patient; nevertheless, all patients received doublet or triplet combinations of newer agents, such as bortezomib, thalidomide, or lenalidomide, as an initial treatment. Among them, 163 patients (79.1%) who achieved VGPR or better (≥ VGPR) were selected  for the analysis. Patients with monoclonal gammopathy of undetermined significance, smoldering MM, plasma cell leukaemia, and systemic amyloidosis were excluded. Patients without serum FLC or MRD data were also excluded. The best response during treatment according to the IMWG response criteria26. The 8-color flow cytometry was performed using a DuraClone RE PC kit  by staining with prefixed dry reagents comprising CD138-APC, CD38-PB, CD56-APC-A750, CD19-PC5.5, CD45-KrO, CD200-PC7, CD81-FITC, and CD27-PE to identify abnormal plasma cells. Flow cytometric analysis was performed using the NAVIOS flow cytometer and Kaluza software v1.5a (Beckman Coulter). To ensure the clonality of the abnormal plasma cells, we stained the cytoplasmic Igκ FITC and Igλ PE in combination with CD38, CD45, CD138, and CD19 monoclonal antibodies, as supplementary. DuraClone is considered equivalent to EuroFlow in terms of MRD detection, with a lower limit of detection of 4.0 × 10−628.MRD assessment was performed using ethylenediaminetetraacetic acid-anticoagulated fresh bone marrow samples by staining with monoclonal antibodies using 6-color (2008–2016) or 8-color (2017–2019) flow cytometry, as previously described. In brief, 6-color flow cytometry analysis was performed using the CD38-PC7, CD138-APC, CD45-ECD, CD19-PE, CD56-PC5.5, and CD81-FITC antibody panels, based on the guidelines of the European Myeloma Network and its update6 total number of cells, the MRD would be 50/3 × 10629. We also compared the correlation between 8-color flow cytometry and 6-color flow cytometry in the MRD quantification using the same samples simultaneously.For quantification of MRD levels, we used the ‘Consensus Guidelines on Plasma cell Myeloma Minimal Residual Disease Analysis and Reporting’ with slight modifications. A minimum of 50 abnormal plasma cells were required for MRD analysis, and hemodilution was checked by the proportion of immature myeloid cells and erythroblasts in the bone marrow smear and clot histology of the samples. If < 50 abnormal plasma cells were detected, the numerator was set to 50 as a lower limit of quantification of abnormal plasma cells, and the total number of nucleated cells was used as a denominator; that is, for 20 abnormal plasma cells and 3 × 104. MRD assessment and FLC analysis were simultaneously performed within a month after achieving the best response.Serum FLC assay was performed using the Freelite assay , and normal serum rFLC (κ/λ) was defined as a range between 0.26 and 1.6530. iFISH from 2016 to 2019 was performed with CD138-enriched bone marrow cells using CD138-coated magnetic MicroBeads 30 and from 2008 to 2015 with whole bone marrow cells. All iFISH analyses were performed before initiation of the treatment.Cytogenetic abnormalities (CA) were analysed using interphase fluorescence in situ hybridisation (iFISH) including del(17p13), t, t, t, del(13q), and gain of chromosome 1q. High-risk CA was defined as del(17p13), t, and t according to the IMWG guidelinesThis study was approved by Kameda Medical Center institutional review board and performed in accordance with the Declaration of Helsinki. All patients provided written informed consent.P values between three or more sets were adjusted using the Benjamini and Hochberg method for controlling the false discovery rate31. OS was calculated from the time point of achieving the best response to that of death from any cause  to mitigate the potential confounding factors by guarantee-time bias on the basis of best response. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to determine the optimal MRD level for prolonged OS in LCMM and IIMM patients. The Cox regression hazard model was used to assess the association between variables of known risks and OS. All P values were two-sided, and P < 0.05 was considered statistically significant. All statistical analyses were performed using R .Comparison of continuous variables between three or more sets was performed using the Kruskal–Wallis test. Comparisons of categorical variables were performed using Fisher’s exact test. Survival analysis was performed using the Kaplan–Meier method. Survival of groups was compared using the log-rank test. Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.


We investigated the association between the frequency of laughter and lifestyle diseases after the Great East Japan Earthquake. We included 41,432 participants aged 30–89 years in the Fukushima Health Management Survey in fiscal year 2012 and 2013. Gender-specific, age-adjusted and multivariable odds ratios of lifestyle diseases were calculated using logistic regressions stratified by evacuation status. Those who laugh every day had significantly lower multivariable odds ratios for hypertension (HT), diabetes mellitus (DM) and heart disease (HD) for men, and HT and dyslipidemia (DL) for women compared to those who do not, especially in male evacuees. The multivariable odds ratios  of HT, DM and HD (non-evacuees vs. evacuees) for men were 1.00 (0.89–1.11) vs. 0.85 (0.74–0.96), 0.90 (0.77–1.05) vs. 0.77 (0.64–0.91) and 0.92 (0.76–1.11) vs. 0.79 (0.63–0.99), and HT and DL for women were 0.90 (0.81–1.00) vs. 0.88 (0.78–0.99) and 0.80 (0.70–0.92) vs. 0.72 (0.62–0.83), respectively. The daily frequency of laughter was associated with a lower prevalence of lifestyle disease, especially in evacuees. Scholars have recently reported the health benefits of daily laughter. Previous studies have shown that laughter is associated with a lower prevalence of cardiovascular disease  and may The Great East Japan Earthquake and the subsequent Fukushima Daiichi nuclear disaster in March 2011 constituted one of the most destructive catastrophes in Japan to date. Due to radiation concerns, most residents in nearby towns had to evacuate and, consequently, suffered long-lasting anxiety. Shortly after the disaster, the Fukushima Health Management Survey was launched . SimilarThe favorable effects of laughter on health may be associated with better health even after this disaster. Recently, the positive effects of laughter on human health have been recognized ,38, and The Mental Health and Lifestyle Survey was a part of the Fukushima Health Management Survey started after the Great East Japan Earthquake in 2011. The Fukushima Health Management Survey is a large cohort study that monitors the long-term health of residents in Fukushima Prefecture after the earthquake and promotes their future well-being. The details of this survey are described elsewhere . The subWe assessed the daily frequency of laughter by a single question, “How often do you laugh out loud?”. Possible responses included “almost every day”, “1–5 days per week”, “1–3 days per month”, and “almost never”. This variable was dichotomized. Those who responded “almost every day” were categorized as people who laugh every day, and those who responded otherwise were categorized as people who do not laugh every day. This measurement has been used in previous studies in Japan , and relWe assessed the lifestyle diseases of cancer, stroke, heart disease, hypertension, diabetes mellitus and dyslipidemia via questionnaires. A single question was, “Have you ever been diagnosed with [a disease]?” The respondents answered either “Yes” or “No”.We asked the question, “How have you changed your living condition as a result of the earthquake?” to check the experience of evacuation of participants. Possible answers were “evacuation shelter”, “temporary housing”, “rental house/apartment”, “relative’s house”, “owned house”, or “other”. Those who answered, “evacuation shelter” and “temporary housing” were defined as people who had experienced evacuation. For those without this information, we asked “Where do you currently live?”. Possible answers were “municipally subsidized rental housing”, “temporary housing”, “restoration public housing”, “rented house/apartment”, “relative’s house”, “owned house”, and “other”. Those who answered, “Municipally subsidized rental housing”, “Temporary housing”, “Restoration public housing”, were defined as people who had experienced evacuation; that is, they changed their living environment that would affect their lives more after the disaster.2). Smoking status was obtained and categorized into current and non-smokers, including past smokers. The alcohol consumption was determined and categorized into current drinkers (more than once a month) and non-drinkers, including past drinkers. Physical activity status was obtained by a single question, “Do you usually do exercise?” Possible answers included “almost every day”, “2–4 times per week”, “once a week”, and “almost never”. The respondents’ quality of sleep was assessed by asking a single question, “Are you satisfied with the quality of your sleep over the past month (regardless of sleep duration)?”. On the questionnaire, possible answers were “Satisfied”, “Slightly dissatisfied”, “Quite dissatisfied”, and “Very dissatisfied or haven’t slept at all”.We obtained data on height and weight by the questionnaire, and body mass index (BMI) was calculated as weight (kg)/height  scale ,43, and p-values for the trend analysis from a regression model for continuous variables, and a logistic model for dichotomized variables according to the categories of laughter adjusted by age. Using logistic regressions, we calculated gender-specific, age-adjusted odds ratios, multivariable odds ratios, and the 95% confidence interval of hypertension, diabetes mellitus, dyslipidemia, cancer, stroke, or heart disease for those who laugh almost every day compared to those who do not. The same analyses were performed and stratified by the presence or absence of an evacuation experience. The covariates included age (continuous), body mass index (continuous), smoking status , drinking status , habitual physical activity , quality of sleep , mental health distress , job status , connection with others  for the analysis of hypertension, diabetes mellitus and dyslipidemia, and history of hypertension, diabetes mellitus and dyslipidemia  in addition to other variables for the analysis of stroke, heart disease or cancer. We used the SAS software version 9.4  for all statistical analyses. All probability values for statistical tests were two-tailed and p < 0.05 was regarded as statistically significant.The gender-specific characteristics of the respondents were described according to the categories of the frequency of laughter. Multiple analyses were conducted for those who laugh 1–5 days per week, 1–3 days per month, almost never compared to those who laugh every day according to each factor. We derived the p for trend < 0.01).Among the 41,432 individuals, 6383 (34.6%) men and 6286 (27.3%) women reported hypertension, 2410 (13.1%) men and 1567 (6.8%) women for diabetes mellitus, 2604 (13.1%) men and 3169 (13.8%) women for dyslipidemia, 776 (4.2%) men and 514 (2.2%) women for cancer, 370 (2.0%) men and 213 (0.9%) women for stroke, and 1597 (8.7%) men and 1349 (5.9%) women for heart disease. Those who experienced evacuation had a lower prevalence of those who laugh daily. Compared to those who do not laugh every day, those who laugh every day had a lower prevalence of hypertension, diabetes mellitus, dyslipidemia and heart disease after the Great East Japan Earthquake, and the magnitude of associations was larger for evacuees especially men.To the best of our knowledge, this is the first study to examine the relationship between the frequency of laughter and lifestyle-related diseases stratified by evacuation experience after a large-scale disaster. So far, several studies have reported the deterioration of health status after major disasters, both physically and psychologically ,23,24,25We faced the following limitations of this study. First, as we conducted a cross-sectional study, the causal relationship is unclear. Therefore, we are preparing a prospective study for the next analysis. Second, in our study, lifestyle diseases and cardiovascular diseases were confirmed by self-administrated questionnaires, not measurements during health checkups, which could lead to non-differential measurement errors and attenuation of the associations. Third, the overall response rate to the questionnaires was not very high (40.7%), and there were some differences in the distribution of ages between the study sample and the target population. Therefore, the results of the study may not reflect all of the evacuation areas specified by the government.After the Great East Japan Earthquake, daily frequency of laughter was associated with a lower prevalence of hypertension, diabetes mellitus, dyslipidemia and heart disease. The association was particularly strong among men who had experienced evacuation, suggesting that positive factors such as laughter may be effective in the prevention of lifestyle diseases under stressful conditions, such as after a major disaster. Further longitudinal or intervention studies are needed to evaluate the relevant causal relationships.


However, changes to the cerebral self-regulation process after HI are still unclear. This study investigated the changes in cerebral metabolites and the potential relationship with the number of neurons and neural stem/progenitor cells (NSPC) using 1H-MRS, and finally clarifies the self-regulation of cerebral metabolism and neuroprotection after HI injury. Newborn Yorkshire pigs  aged 3–5 days were used for the HI model in this study. The pigs were randomly divided into the HI group (n = 24) and the control group (n = 4), then the experimental group was subdivided according to different recovery time after HI into the following groups: 0–2 h (n = 4), 2–6 h (n = 4), 6–12 h (n = 4), 12–24 h (n = 4), 24–48 h (n = 4), and 48–72 h (n = 4). Following the HI timepoints, 1H-MRS scans were performed and processed using LCModel software, and brain tissue was immunohistochemically stained for Nestin and NeuN. Immunofluorescence staining of creatine phosphokinase-BB (CK-BB), N-acetylaspartylglutamate synthetase (NAAGS), glutamate carboxypeptidase II (GCP-II), glutamate-cysteine ligase catalytic subunit (GCLC), glutathione synthase (GS), and excitatory amino acid carrier 1 (EAAC1) was then performed. The 1H-MRS results showed that cerebral N-acetylaspartylglutamate (NAAG), glutathione (GSH), and creatine (Cr) content reached their peaks at 12–24 h, which was consistent with the recovery time of hippocampal NSPCs and neurons, indicating a potential neuroprotective effect of NAAG, GSH, and Cr after HI injury. Hypoxic-ischemic (HI) injury is one of the major causes of neonatal encephalopathy, mainly causes damage to the cerebral cortex, hippocampus, basal ganglia, and thalamus and can lead to complications such as cerebral palsy, epilepsy, and cognitive impairment  and the control group (n = 4). According to the recovery time after HI, the experimental group was divided into 6 subgroups, including 0–2 h (n = 4), 2–6 h (n = 4), 6–12 h (n = 4), 12–24 h (n = 4), 24–48 h (n = 4), and 48–72 h groups (n = 4). The relevant procedures for experimental animals were performed in accordance with the Laboratory Animal Care and Use Guidelines issued by the National Research Council and approved by the Animal Care and Use Institutional Committee .Twenty-eight newborn Yorkshire pigs  aged 3–5 days were selected and randomly divided into the experimental group  injected intramuscularly at a dose of 0.6 ml/kg. After the animal's corneal reflex disappeared, tracheal intubation (diameter 2.5 mm) and ventilator-assisted breathing  was conducted with the respiratory rate set at 10 times/min and the pressure at 15–25 hPa. A Heal Force pulse oximeter  was used to monitor vital sign (pulse and blood oxygen saturation). An indwelling trocar in the right ear vein served as the venous channel.Skin was disinfected three times with iodophor followed by a median cervical incision, blunt dissection was performed in layers, and the common carotid artery was dissociated, with special attention paid to avoid damage to the carotid sinus and vagus nerve during the operation.HI injury: The bilateral common carotid arteries were located by surgical sutures and occluded by artery clamps. Pigs mechanically inhaled a mixture of 6% oxygen and 94% nitrogen  at the same time. After 40 min, the artery clamps were removed, 100% oxygen was resumed, and the cervical incision was sutured. The ventilator was then stopped and the tracheal tube was removed after spontaneous breathing resumed  was employed as the MR equipment has gradient coil transmission and 8-channel head coil reception. A single voxel sequence was utilized for 1H-MRS, and the scanning parameters were as follows: TR = 2,000 ms; TE = 37 ms; NSA = 64; VOI = 10 × 10 × 10 mm. The region of interest was selected in the right basal ganglia. The LCModel software package was used to analyze the 1H-MRS results  , NAAG synthetase (NAAGS), glutamate carboxypeptidase II, (GCP-II), glutathione synthase (GS), glutamate-cysteine ligase catalytic subunit (GCLC), and excitatory amino acid carrier-1 (EAAC1). The tissue sections were deparaffinized with xylene and hydrated with gradient ethanol, followed by antigen retrieval with microwave heating for 37 min using citrate buffer . The sections were then incubated with normal goat serum at room temperature for 40 min to block non-specific antibody binding, and followed by incubation of the sections overnight at 4°C with the following selected primary antibodies: rabbit polyclonal antibody to RIMKA ; rabbit polyclonal antibody to PSMA ; rabbit polyclonal antibody to Creatine kinase B type ; rabbit polyclonal antibody to GCLC ; rabbit monoclonal antibody to GS, , and rabbit polyclonal antibody to EAAT3 . Goat anti-rabbit IgG labeled with Alexa Fluor 488  was used as the secondary antibody and incubated with the sections at room temperature for 4 h. Finally, nuclear staining was performed by incubation with 4′,6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI) for 5 min. The immunofluorescence images were collected using a confocal laser scanning microscope  (×400) and the mean fluorescence intensity was measured using ImageJ software .P < 0.05 considered to be statistically significant. Pearson's correlation analysis was used to analyze the relationship among metabolites, and all statistical analyses were conducted using SPSS  and GraphPad Prism . The correlation heatmap was drawn with Origin Pro software .A one-way ANOVA analysis of variance and the LSD test were employed to compare the differences between the subgroups with 1H-MRS was used to observe the cerebral amino acid metabolism changes at different time periods after HI injury, as shown in P = 0.057; LSD test), but they both were significantly higher than those of the other groups . Asp content was significantly reduced at 0–2 h , and increased at 48–72 h compared with the 12–24 h group . Compared with the others, GSH content increased significantly in the 12–24 h group . Cr content in the 12–24 h group was significantly higher than that in the control group  and the 48–72 h group . Glu content in the 24–48 h group was significantly higher than that in the control group . Gln content in the 6–12 h group was significantly higher than that in the 2–6 h group . The correlation heatmap for each metabolite is shown in r = −0.661, P < 0.001) as was Cr and PCr . In contrast, Gln and GSH were significantly positively correlated  and there was a significant positive correlation between Cr and GSH .P < 0.001, P = 0.027, and P = 0.002, respectively; ANOVA). The differences in CK-BB, NAAGS, and GCP-II expression after HI injury are shown in P < 0.001; LSD test), increased at 2–6 h , and continued to increase at 12–24 h after HI injury. Compared with the 0–2 h group, the expression of NAAGS increased significantly at 2–6 h, 6–12 h and 12–24 h . The expression of NAAGS in the 24–48 h group was significantly lower than that in the 6–12 h group . Compared with the 2–6 h group, the expression of GCP-II decreased significantly at 6–12 h , and then increased at 24–48 h There were differences of CK-BB, NAAGS, GCP-II expression among subgroups after HI injury . P = 0.004; LSD test), increased at 6–12 h compared with the 2–6 h group , and decreased at 12–24 h after HI injury . GS expression tended to increase first and then decrease with a peak at 6–12 h , a slight decrease at 12–24 h, with no statistical difference , and significantly decreased at 24–48 h after HI injury . The peak expression of GCLC was at 2–6 h after HI, and it was significantly higher than that of the other groups .EAAC1, GCLC, and GS expression also differed among subgroups after HI injury . Compared with the control group, the number of hippocampal NSPCs decreased significantly in the 0–2 h, 2–6 h, and 6–12 h groups , with a rebound in the 12–24 h group  and there was no statistical difference compared with the 24–48 h and 48–72 h group   .P < 0.001, P = 0.004; ANOVA). Hippocampal neurons were significantly reduced at 0–12 h , but increased at 12–24 h after HI , and reduced at 24–48 h after HI . Compared with the control group, the number of basal ganglia neurons was significantly reduced at 2–6 h  and was further reduced at 24–48 h after HI  as shown in The numbers of hippocampus and basal ganglia neurons were significantly changed among control groups and HI groups (1H-MRS detects metabolites by measuring their relative quantification (ratio), which will help assess the severity of HI injury and predict the recovery of neurological function. However, the metabolite ratio cannot reflect the specific changes in a single case or the potential mechanisms of energy failure and metabolic disorders after HI injury  and 2 ATP-dependent enzymatic steps (Aoyama et al., 1H-MRS was employed to observe changes in Cr content after HI injury. The results showed a gradual increase at first, which peaked at 12–24 h, and then a decrease; this variation depends on the conversion from PCr and the catalytic enzyme activity of CK-BB. The 1H-MRS results in this study showed a significant negative correlation between Cr and PCr, and histopathology found that CK-BB expression increased at 2–6 h and lasted 12–24 h after HI injury. These results indicate that there is a mutual conversion between PCr and Cr during this period.In the past, cerebral Cr content was considered to be relatively constant, thus it is often used as an internal reference for the relative quantification of metabolites. In recent years, studies have found that Cr dynamically changes, and its content is related to physiological activity or vascularization in a specific area Rae, . In thisStudies have confirmed that the PCr-Cr system can compensate for the decrease in ATP caused by hypoxia or ischemia to a certain extent (Gaddi et al., In addition, this study found a significant positive correlation between the Cr content and GSH, suggesting that a correlation between energy metabolites and amino acid metabolites also exists. Since, GSH synthesis is dependent on ATP (Aoyama et al., This study also found an increased number of neurons in the hippocampus at 48–72 h after HI injury. Meanwhile, the Glu content decreased while the Asp content increased significantly, which may be due to the effect of MAS. The MAS shuttling of Glu into the mitochondria and the increase of Asp and α-ketoglutarate conversion provide energy for cell regeneration (Beckervordersandforth, Cerebral metabolites including NAAG, GSH, and Cr displayed a transient elevation following HI injury. This trend is consistent with the duration of increased hippocampal neurons and NSPCs, indicating that NAAG, GSH, and Cr may self-regulate and have neuroprotective effects after HI injury.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by Animal Care and Use Institutional Committee of Shengjing Hospital.KL: investigation, data curation, and writing-original draft. YZ: validation, resources, and supervision. XW: conceptualization, methodology, writing-review and editing, and project administration. All author participated sufficiently to take public responsibility for its content, read, and approved the submitted version.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.


Background: This systematic review and meta-analysis aimed to assess whether tricuspid annular plane systolic excursion (TAPSE) could be used as a prognostic tool in patients with coronavirus disease 19 (COVID-19).Studies on the relationship between TAPSE and COVID-19 since February 2021. Standardized mean difference (SMD) and 95% confidence intervals were used to assess the effect size. The potential for publication bias was assessed using a contour-enhanced funnel plot and Egger test. A meta-regression was performed to assess if the difference in TAPSE between survivors and nonsurvivors was affected by age, sex, hypertension or diabetes.P < .00001; I2 = 71%), and a subgroup analysis indicated that TAPSE was also lower in critically ill patients , P < .00001; I2 = 46%). Heterogeneity was also significantly reduced, I2 < 50%. Pooled results showed that patients who developed right ventricular dysfunction had lower TAPSE , P = .004; I2 = 82%). There was no statistically significant difference in the TAPSE of patients who sustained a cardiac injury vs those who did not , P = .31; I2 = 88%). No significant publication bias was detected (P = .8147) but the heterogeneity of the included studies was significant. A meta-regression showed that heterogeneity was significantly greater when the incidence of hypertension was <50% (I2 = 91%) and that of diabetes was <30% (I2 = 85%).Sixteen studies comprising 1579 patients were included in this meta-analysis. TAPSE was lower in nonsurvivors , Low TAPSE levels are associated with poor COVID-19 disease outcomes. TAPSE levels are modulated by disease severity, and their prognostic utility may be skewed by pre-existing patient comorbidities.PROSPERO CRD42021236731 On August 5, 2021, the cumulative number of COVID-19 cases globally surpassed 200 million, just 6 months after reaching 100 million. As a result, the understanding of the disease and its risk factors are key factors for implementing public health policies at present.Coronavirus disease 19 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), thereby creating an inflammatory state that can lead to cardiopulmonary injury.–10 Endothelial dysfunction induced by shear stress, hypoxia, autoimmune phenomena, viral infections may initiate the process of excess vasoconstriction, inflammation, and uncontrolled cellular growth,–15 and lead to pulmonary hypertension.,15 In response to an increase in pulmonary vascular resistance by a factor of 5 to 10, the right ventricle (RV) undergoes hypertrophy, chamber dilatation, fat deposition, fibrosis, and metabolic shifts as pulmonary hypertension progresses.–17 Right ventricular function (RVEF) is the major determinant of clinical outcomes and survival among patients with pulmonary hypertension.COVID-19 induces cytokine release, Hugues et al proved in their study that tricuspid annular plane systolic excursion (TAPSE) was significantly correlated with RVEF results measured by magnetic resonance imaging . TAPSE has also proven to be easy to perform, accurate, reproducible and with little variation between observers. And the American Society of Echocardiography currently recommends using TAPSE measurement as one of the tools to assess RV function. However, diagnostic use of echocardiography during the COVID-19 pandemic is largely based on expert recommendations and lacks data from large samples of evidence-based scientific results . The protocol for this study was registered in PROSPERO (CRD42021236731).2.1Eligible articles were identified in the process illustrated in Figure Inclusion criteria: Studies with primary data on patients with COVID-19 with TAPSE measurements that were used as a prognostic metric. The primary outcome of the work was a “poor outcome,” defined by the authors of the study using some composite of death, disposition to a long-term acute care facility (LTAC), cardiac injury, and right ventricular dysfunction (RVD).Exclusion criteria: Preprints, review articles, nonresearch letters, commentaries, case reports, and articles not written in English. Preprints were excluded due to inconsistent credibility.2.2; TAPSE was measured using guidelines set forth by the American Society of Echocardiography. In short, TAPSE was assessed by placing an M-mode cursor through the lateral tricuspid valve annulus in the apical 4-chamber view and measuring the total systolic excursion distance of the tricuspid annulus; Mortality was defined as clinically validated death/nonsurvival; LTAC centers are unique institutions that provide a full range of medical services for medically complex patients who require an extended stay in an acute care facility; Cardiac injury was defined as an increase in high-sensitivity troponin T or troponin I levels above the 99th percentile upper reference limit in the setting of a newly abnormal electrocardiograph and/or transthoracic echocardiography; and RVD was defined by impairment of both TAPSE and S′ so as to reduce false positive classification.The following definitions were used for this analysis: COVID-19 was diagnosed with a positive RT-PCR for SARS CoV-2 with a throat swab, sputum or endotracheal suction sample2.3Articles published as of February 2021 in the PubMed, Cochrane Library, Embase, and Web of Science databases were included. The search strategy used across all databases combined medical subject headings and free words with “AND” and “OR” as the 2 logical operators. Key search terms included “(coronavirus or corona virus) AND Wuhan”, “2019 nCoV”, “2019nCoV”, “2019-nCoV”, “2019 novel coronavirus”, “COVID19”, “COVID 19”, “COVID-19”, “new coronavirus”, “novel coronavirus”, “novel corona virus”, “SARS CoV-2”, or “severe acute respiratory syndrome coronavirus 2” combined with “(cardiac or heart or right) AND ventric∗”, “RV”, “tricuspid annular plane systolic excursion”, or “TAPSE”. Only studies published in English with an available full text were included. When several publications involved the same patient group, the most recent or most complete (largest sample size) study was chosen. Potentially relevant articles were then assessed using our inclusion and exclusion criteria.2.4Two independent authors extracted data from eligible studies with the help of a standardized data form that included fields for first author, year of publication, country of publication, study design, sample size, age, male sex, hypertension, diabetes, chronic obstructive pulmonary disease, current smoker, outcome , LTAC disposition, cardiac injury, and RVD.The quality of each cohort study was evaluated with the Newcastle–Ottawa Scale, and cross-sectional studies were evaluated with the Agency for Healthcare Research and Quality scale. Quality assessments were performed by 2 independent authors. Discrepancies at the end of the assessment were addressed through the inclusion of a third party.2.5Z-test, and a P-value of <.05 was considered significant. In light of possible sources of heterogeneity, studies were stratified by disease severity. The potential for publication bias was assessed using a contour-enhanced funnel plot and Egger test. A meta-regression was performed to assess if the difference in TAPSE between survivors and nonsurvivors was affected by age, male sex, hypertension or diabetes.The meta-analysis of extracted data was performed using Review Manager 5.4  and STATA 16 . Standardized mean difference (SMD) and corresponding 95% confidence intervals were estimated with a random-effect meta-analysis. The significance of pooled SMD was evaluated with a 33.1,24,26–39 Baseline characteristics of the included studies are displayed in Table The initial search identified 1190 studies. Excluding duplicates, 577 articles were screened with a title and abstract review. The entire manuscript body of 63 relevant articles was assessed, of which 16 met the inclusion criteria for data analysis Fig. . This yi3.2P < .00001; I2 = 71%) , P < .00001; I2 = 46%) , 3.32 (I2 = 82% > 50%) and chi-square test P-values (P < .05), we chose a random effects model to analyze differences in TAPSE. Pooled results showed that patients with RVD had lower TAPSE , P = .004; I2 = 82%)  , 3.5P = .8147) and suggestive of a lack of publication bias. The included studies were therefore deemed comprehensive and yielded statistically reliable results.A contour-enhanced funnel plot was symmetric Fig. , which w3.6P = .047) and diabetes (P = .006), but not age (P = .398) or male sex (P = .925). A subgroup analysis showed that heterogeneity was significantly greater when the incidence of hypertension was ≥50% (I2 = 61%) and that of diabetes was ≥30% (I2 = 0%) , whereas our subgroup analysis of critically ill patients indicated that TAPSE (I2 = 46%) was equivalent, accompanied by significantly reduced heterogeneity. It may be more applicable in critically ill patients.Significant heterogeneity was a limitation of the analysis of the cohort and cross-sectional studies included in this study. The first reason for the observed heterogeneity was clinical severity. Our meta-analysis of TAPSE showed that nonsurvivors had lower TAPSE (IP = .047) and diabetes (P = .006), and a subgroup analysis showed that heterogeneity was significantly reduced when the incidence of hypertension was ≥50% or the incidence of diabetes was ≥30%. The fourth reason is that TAPSE may be angle-dependent when the image is off-center due to the enlargement of the RV, and may also be affected by cardiac displacement. Hsiao et al also found the correlation between severe tricuspid regurgitation affecting TAPSE and RVEF in their study, and the effect of severe tricuspid regurgitation should be considered comprehensively when applying TAPSE to evaluate RVEF.The second reason for the observed heterogeneity was that different research centers did not have a standardized protocol for treating patients but rather relied on their respective training and experience. The third reason was that the results of the present work were based on unadjusted estimates. A more precise analysis could be conducted if individual patient data were made available, thereby permitting the assessment of COVID-19 disease severity by age, sex, past medical history, and personal history. A meta-regression demonstrated that differences in TAPSE were affected by hypertension ( With the increase of pulmonary artery (PA) pressure (right cardiac afterload), stroke volume decreased nonlinearly. TAPSE, which can be used to evaluate RV function, has been proved as an independent prognostic marker of specific pathologies including ARDS. Fauvel et al also reported TAPSE as a good marker of RV contractility, with relative load-independence in the setting of pulmonary hypertension. However, the use of TAPSE as a prognostic marker remains controversial. Shadi et al did not find a correlation between severe sepsis or septic shock and TAPSE. This may have been owing to various factors such as patient comorbidities.COVID-19-induced systemic inflammation can lead to ARDS, pulmonary hypertension, myocardial injury, and ultimately RV failure. Studies have found that among hospitalized non-Intensive Care Unit patients with COVID-19, pulmonary hypertension was associated with signs of more severe COVID-19 and with worse inhospital clinical outcome. And some measurement methods are good, such as strains or 3D-derived measures, but they need better image quality and special software. On the other hand, most TAPSE measurements take less than 30 seconds, with high operability and repeatability, TAPSE seems to be more attractive to critically ill patients or primary hospitals.The important role of the RV in the management of the critically ill is increasingly acknowledged, and many novel measures of RV function have been developed. For example, RV free wall longitudinal strain overcomes some of the limitations of conventional diagnosis of RV systolic function parameters, but both lack standardized data and consensus. With developing pulmonary hypertension, the RV responds to a significantly elevated afterload by increasing its contractility. This response may be accompanied by an increased TAPSE. However, with persistent and progressive pulmonary hypertension, TAPSE may not correspond to an increase in PA pressure. Schmeisser et al also reported that combining TAPSE with PASP did not improve the noninvasive right ventricular (RV) to PA coupling information. Therefore, it seems more meaningful to evaluate RV and PASP separately.It has also been suggested that the ratio of TAPSE to PA systolic pressure (PASP) has a stronger association with outcome in COVID-19 patients than TAPSE. Studies by Zhou et al found that patients with cardiac injury had lower lymphocyte counts and higher levels of inflammatory biomarkers compared with those without cardiac injury. Studies by D’Alto et al,34,47,48 also found significant differences in myocardial enzyme levels in nonsurvivors compared with survivors, suggesting that these enzyme levels may have prognostic potential and affect pooled effect estimates. Therefore, the present work did not find a statistically significant difference; this may be related to the limited number of patients and the varying severity of the disease cases that were included in this meta-analysis.Prior studies have suggested that the cytokine profile of secondary hemophagocytic lymphohistiocytosis is related to COVID-19 severity. It has also been suggested that novel blood pressure and pulse pressure estimation based on pulse transit time and stroke volume approximation may better enable ubiquitous monitoring of blood pressure and management of cardiovascular risk indicators such as hypertension. Therefore, these indicators are likely to be used as part of a risk prediction model to predict the prognosis of COVID-19 patients.Furthermore, obese patients may suffer from obstructive sleep apnea hypopnea syndrome, in which the body is chronically deprived of oxygen for a long time, leading to cardiopulmonary damage. When these patients are infected with SARS-CoV-2, they are likely to develop heart and respiratory failure, which can lead to worsening of the illness and even death. Similarly, obesity is a risk factor for diabetes mellitus, which is reportedly associated with an increased risk of adverse outcome events in patients with COVID-19. However, our study analyzed TAPSE not only in relation to mortality, but also in relation to RVD and cardiac injury. At the same time, TAPSE is a good bedside method for judging right heart function, and also a good guide for treatment. We also concluded that the relationship between TAPSE and COVID-19 patients was affected by disease severity. Our results further complement previous findings of an impact of hypertension and diabetes on mortality. We also discuss why TAPSE was chosen as a predictor and what factors have the opportunity to be part of a predictive model.Previous meta-analyses have concluded that TAPSE is lower in nonsurvivors compared with survivors.5There were several limitations to this study. Only English-language sources were included, excluding potentially eligible studies that were not written in English. Since this index measured by different echocardiographers may lead to deviations in the results, echocardiographers should be trained to unify the measurement standards and a larger sample size is needed for research. In addition, because of the different factors and outcomes investigated in each study, we were not able to comprehensively analyze only 1 factor. Finally, although we performed a subgroup analysis of TAPSE, a robust risk prediction model is required to accurately predict the prognosis of patients with COVID-19.6Low TAPSE levels are associated with poor COVID-19 disease outcomes. TAPSE levels are modulated by disease severity, and their prognostic utility may be skewed by pre-existing patient comorbidities.Conceptualization: Ye Tian, Huaihai Lu, Pei Zhang.Data curation: Ye Tian, Huaihai Lu, Xuefang Liu, Yinlong Zhao.Formal analysis: Ye Tian, Huaihai Lu.Funding acquisition: Ye Tian, Huaihai Lu.Investigation: Ye Tian, Huaihai Lu, Pei Zhang.Methodology: Ye Tian, Huaihai Lu.Project administration: Ye Tian, Huaihai Lu.Resources: Ye Tian, Huaihai Lu.Software: Ye Tian, Huaihai Lu.Supervision: Ye Tian, Huaihai Lu, Pei Zhang.Validation: Ye Tian, Huaihai Lu.Visualization: Ye Tian, Huaihai Lu.Writing – original draft: Ye Tian, Huaihai Lu.Writing – review & editing: Ye Tian, Huaihai Lu, Pei Zhang.


Sphaeroforma-close unicellular relatives of animals with brief transient multicellular life stages-we demonstrate that sedimentation rate is a highly variable and evolvable trait affected by at least 2 distinct physical mechanisms. First, we find extensive (>300×) variation in sedimentation rates for different Sphaeroforma species, mainly driven by size and density during the unicellular-to-multicellular life cycle transition. Second, using experimental evolution with sedimentation rate as a focal trait, we readily obtained, for the first time, fast settling and multicellular Sphaeroforma arctica isolates. Quantitative microscopy showed that increased sedimentation rates most often arose by incomplete cellular separation after cell division, leading to clonal “clumping” multicellular variants with increased size and density. Strikingly, density increases also arose by an acceleration of the nuclear doubling time relative to cell size. Similar size- and density-affecting phenotypes were observed in 4 additional species from the Sphaeroforma genus, suggesting that variation in these traits might be widespread in the marine habitat. By resequencing evolved isolates to high genomic coverage, we identified mutations in regulators of cytokinesis, plasma membrane remodeling, and chromatin condensation that may contribute to both clump formation and the increase in the nuclear number-to-volume ratio. Taken together, this study illustrates how extensive cellular control of density and size drive sedimentation rate variation, likely shaping the onset and further evolution of multicellularity.Significant increases in sedimentation rate accompany the evolution of multicellularity. These increases should lead to rapid changes in ecological distribution, thereby affecting the costs and benefits of multicellularity and its likelihood to evolve. However, how genetic and cellular traits control this process, their likelihood of emergence over evolutionary timescales, and the variation in these traits as multicellularity evolves are still poorly understood. Here, using isolates of the ichthyosporean genus  The transition to multicellularity is associated with the emergence of new features, including an increase in sedimentation rate, but how does such a key transition first occur? An experimental evolution study of ichthyosporeans, close unicellular relatives of animals, shows how cellular control of density and size drive sedimentation rate variation, likely shaping the evolution of multicellularity. The emergence of multicellularity from single-celled life represents a major transition, which has occurred many times independently across the tree of life –8. MultiSaccharomyces cerevisiae and Chlamydomonas reinhardtii in response to gravitational selection .–70.regulAmong the variants detected in evolved clones with intermediate-settling phenotype, we highlight an intergenic mutation 118 bp downstream of Dynamin-1 known to be essential for cytokinesis across different taxa –77, and Sphaeroforma species , and, thus, we cannot distinguish whether this trait is “ancestral” or “novel.” Rather, our experimental data suggest that clumpiness and density could evolve over short microevolutionary timescales such as those we have measured in the lab and this in both directions. Evolved lines with highly clumping phenotypes had the competitive edge over their nonclumping common ancestor in relative fitness assays. In contrast, during exponential growth and in the absence of selection, evolved clumpy line were selectively disfavoured relative to their common ancestor, which suggests that clumpy phenotypes are prone to rapid replacement by less clumpy or dense morphs when environments change.We found that closely related species exhibit widespread variation in both clump formation and nuclei-to-volume ratio. Given the evolvability of the trait and the few natural samples, we examined it is difficult to argue which cellular states affecting sedimentation rates are ancestral. For example, considering the species tree and the trait of clumpiness , only 3 Sphaeroforma species are remarkably spherical, immobile, lack flagella, and yet exhibit a substantial increase in cell size and density over the life cycle, thus representing a challenge to maintaining buoyancy in marine habitat. This work establishes that Sphaeroforma’s cell size and density are subject to tight cellular control and are highly evolvable traits. Taken together, these observations suggest that sedimentation rate is a highly evolvable trait, which itself likely shapes the gain and loss of multicellularity.Indeed, marine organisms have evolved various passive or active means of maintaining their position in the water column, for example, using motility and/or ingenious approaches to regulate buoyancy ,86–90. SSphaeroforma sp. cultures were grown and synchronized as described previously for S. arctica p of cell−3). We then used least-squares minimization to estimate 2 parameters: a scalar S of the AU velocity measurements, dODdt, which could best match our dataset with these expected velocities, and an average density parameter pp_hat, which could predict these values and the Smayda dataset’s values based on Stokes’ law. For this, we minimized the loss functionV0 is either the seawater density–corrected velocity from Smayda or the dODdt parameter we calculated above, and H is the one-hot binary scalar in  corresponding to whether the i’th data point in our N observations was from Smayda’s or our dataset, respectively. We used R’s optim function with the “L-BFGS-B” method with initial parameters values of S = 0.03 and pp_hat = 100 and repeated this fit for 500 10-fold (10% out-of-bag) bootstrap samples of individual observations across our full dataset and Smayda’s to gain an estimate of the error on the parameters.To estimate the sedimentation velocities of our dataset, we assumed that our data  would faS and pphat fits were positively correlated across testing/training folds (Pearson’s ρ = 0.97); however, mean values were fairly limited, with S = 0.028 ± 0.002 and pp_hat = 78 ± 6.4 kg·m−3 . These narrow estimates indicated that the fits were reasonably well defined by the underlying dataset. The means and standard deviations across predictions of out-of-bag samples are what we have reported as means and standard error in S. arctica cultures using a Branson 450 Digital Sonifier . Cells were then diluted in fresh MB (1:250 dilution), and cell concentration was then measured using a hemocytometer. Approximately 20 cells were then transferred per well in a 96-well plate. Cells were then monitored every 24 hours using an inverted optical microscope, and the percentage of clumps observed after cellularization was measured manually using a tally counter. This experiment was performed 3 independent times, and error bars are standard deviations.To measure percentage of clumps across all mutants and transfers in To perform the fitness experiment (head-to-head competition), we used saturated and sonicated cultures of the ancestor (AN), S01 and S03 evolved isolates. Following the measurement of the cell concentration using a neubauer chamber, we independently mixed both S01 and S03 with the AN at an approximately 1:1 ratio and subjected them to either the same selective regime as the evolution experiment (see above) or a control culture that was simply maintained in exponential growth without sedimentation selection. The experiment was maintained for 72 hours with passages every 24 hours. To quantify the outcome of the experiment, every 24 hours for 72 hours (7.5 to 12.5 generations), after dilution of the competition culture, we counted the number of clumpy and nonclumpy cells at each passage. For this, a fraction of the cultures was sonicated and highly diluted to obtain fewer than 10 cells per well of a 96-well plate. These cells were allowed to undergo a full life cycle in order to count by microscopy the proportion of cells, which bore the clumpy phenotype. The high penetrance of the clumpy phenotype in these clones therefore allowed not only a quantification of the clumpy phenotype itself in competition, but also the measurement of overall clonal fitness of these derived isolates.https://CRAN.R-project.org/package=binom).Each data point for the fitness assay was a count of cells with an associated binomial error. In Figs The fitness measurements began from highly synchronized cultures, and, therefore, reporting a per-generation fitness value was clouded by a sudden jump in cellular counts that occurred at 48 hours. Therefore, for simplicity and given the clear time-dependent changes in clumpy phenotype frequencies, we report fitness in terms of log2 fractional change per unit time (“log2 selection coefficient” in days). To obtain these numbers, we first used R’s rbinom function to resample the dataset 500 times, with each resampling using each sample’s original measurement of clumpy cell count, total cell count, and proportion clumpy. For each resampling, after calculating the new value for proportion clumpy, we logit2 transformed the data and rescaled to the initial measurement with the following equation:To obtain a coarse understanding of how heritable our phenotypes were, we used a statistical genetic approach to quantify the heritability of traits, or what proportion of the variance in phenotype is due to an individual’s inherited state. Briefly, the total Variance of a measured phenotype V_P can be variance partitioned between Environment, Genetics (or other inherited factors), and technical error or stochastic events (epsilon):The different partitioning terms in V_P(T) can be expressed as a proportion of V_P(T):And, therefore, we can refer to the proportion of variance due to genetics as pV_P(G).In the lab, we eliminated environmental variance with tightly controlled experimental conditions such as temperature and the number of hours a measurement was taken after cell cycle synchronization. This allowed us to define heritability therefore as the environment-controlled genotypic variance:p = 0). This means that for a typical individual genotype in a given environment, we could predict its average phenotypic measurement with >97% accuracy.For this, we determined H for DNA content, perimeter length, sedimentation rate, and fitness phenotypes by calculating the variance explained by mean phenotype values within distinct genotype/environment combinations . The resMicroscopy of live and fixed cells was performed using a Zeiss Axio Observer Z.1 Epifluorescence inverted microscope equipped with Colibri LED illumination system and an Axiocam 503 mono camera. An EC Plan-Neofluar 40x/0.75 air objective was used for images of fixed cells, and an N-ACHROPLAN 20x/0.45na Ph2 air objective was used for all live imaging, unless indicated otherwise.Sphaeroforma cultures were mildly sonicated prior to diluting them 250X in fresh marine broth to initiate a synchronized culture. To assess for any temperature dependency, cultures were grown at both 17°C and 12°C, and measurements were conducted every 12 hours for a duration of 72 hours. For every time point, cells were fixed using 4% formaldehyde and 250 mM sorbitol for 30 minutes before being washed twice with PBS. For nuclei staining, cells were centrifuged at 1,000 rpm for 3 minutes after fixation and washed again 3 times with PBS before adding DAPI at a final concentration of 5 μg/mL to 5 μl of concentrated sample. DAPI-stained samples were imaged to measure DNA content and coenocyte size. It is important to note that results obtained from fast-settling mutants prior to cell release correspond to measurements of unicellular coenocytes (24 hours for 17°C and 36 hours for 12°C), whereas results collected after cell release correspond to measurements of multicelled clumps (48 hours for 17°C and 72 hours for 12°C). For cell wall staining, cells were incubated with Calcofluor-white (Sigma-Aldrich) at a final concentration of 5 μg/ml from a 200X stock solution prior to fixation. Cells were then fixed as previously mentioned and concentrated before being disposed between slide and coverslip.Throughout this study, saturated For live cell imaging, saturated cultures were diluted 250× in fresh MB medium inside a μ-Slide 4- or 8-well slide (Ibidi) at time 0. To ensure oxygenation during the whole period of the experiment, the cover was removed. To maintain the temperature at 17 or 12°C, we used a P-Lab Tek (Pecon GmbH) Heating/Cooling system connected to a Lauda Ecoline E100 circulating water bath. To reduce light toxicity, we used a 495-nm Long Pass Filter (FGL495M- ThorLabs). For plasma membrane live staining , FM4-64 Sphaeroforma sp.’s life cycle, fixed and DAPI-stained coenocytes were imaged, and the number of nuclei per coenocyte was counted using the ObjectJ plugin in imageJ. To compute nuclear duplication times, log2 of geometric mean of DNA content was calculated as: S. tapetis, nuclear doubling times could not be computed due to the asynchrony in growth. For measurements of cell volume in live and fixed cells, we used the oval selection tool to draw the contour of each cell and measured cell perimeter. As cells are spherical, we computed cell volume as V = 4/3πr3 where r is the cell radius. For measurements of clumps perimeter, we transformed the images into binaries to ensure later segmentation. We then used the particle analysis function in ImageJ with a circularity parameter set to 0.15 to 1 to measure cell perimeter. For nuclear number-to-volume ratios, the number of nuclei was divided by the coenocyte volume measured as previously described for fixed cells. All figures were assembled with Illustrator CC 2020 (Adobe). Several figures were generated using ggplot2 in R version 4.0.5 [Image analysis was done using ImageJ software (version 1.52) . For nuon 4.0.5 .2 flasks. The Qubit (Invitrogen) quantification ranged between 3 and 13 μg of genomic DNA in total. All of the subsequent steps were performed by the CRG Genomics Unit (Barcelona): Sequencing libraries were prepared from the pure high molecular weight DNA using TruSeq DNA HT Library Preparation kit (Illumina HiSeq Sequencing v4 Chemistry). A paired-end library with a target insert size of approximately 500 bp was sequenced on an Illumina HiSeq2500 platform in paired-end mode, with read lengths of 125 bp. The resulting paired raw read files were demultiplexed by the sequencing facility and data stored in 2 separate, gzip-compressed FASTQ files of equal sizes. Genome sequencing data have been deposited in NCBI SRA under the BioProject accession PRJNA693121.Genomic DNA was extracted for the ancestral strain (AN) and a single clonal isolate from each evolved population (S1-S10) using QIAamp DNA Blood Midi Kit (Qiagen) following the manufacturer’s recommendations from 50 mL culture incubated at 17°C for 5 days in 75 cmtrimmomatic v0.36 [On average, each paired-end sequencing library contained approximately 58.8 million reads of 125 bp sequence lengths , equalinic v0.36 , yieldinic v0.36 . In moreILLUMINACLIP:Nextera+TruSeq3-PE2.fa:3:25:10 CROP:110 LEADING:30 TRAILING:25 SLIDINGWINDOW:4:28 MINLEN:50.This translates into the following trimming steps:cut adapters and other Illumina-specific sequences using a combined file of default adapters (“Nextera.fa” AND “TruSeq3-PE2.fa”) to catch as many spurious contaminations during library prep as possible, with seed mismatches = 3; palindrome clip threshold = 25; simple clip threshold = 10;end-clipping of the final 15 bp of all reads, due to evidence for elevated adapter content;quality-clip all bases on leading ends as long as bases were of lower quality than Q < 30;removing all bases on trailing ends as long as bases were of lower quality than Q < 25; and,finally, conducting a sliding window approach, where the reads were trimmed once the average quality within a window of 4 consecutive bases falls below a threshold of Q <28.The trimming output consisted of 4 FASTQ files for each genome, of which 2 files contained intact paired-end reads, and another 2 files containing all unpaired reads for each end separately.S. arctica reference genome  assembly version of the   for varr v4.1.1  relying otations  and applQiagen), and our analytical pipeline was structured into the following steps:We performed read alignment, variant calling, variant filtering in CLC Genomic Workbench v20.0.4 , and both paired and unpaired reads were aligned against the reference genome . Finally, reads were deduplicated (maximum representation of minority sequence = 0.2).Variant calling. We called variants using CLC’s “Fixed Ploidy Variant Detection” assuming a haploid genome (Ploidy = 1) for S. arctica. We further required a variant probability of at least 90%, ignoring positions in excess of 90× coverage, broken read pairs and nonspecific matches. All variants needed to be covered by at least 10 variant-bearing reads and a minimum consensus of 80% . We also applied the following read quality filters: neighborhood radius = 10; minimum central quality = 20; minimum neighborhood quality = 20), and read direction filters ; read position filter = yes (significance = 0.05).Variant filtering. (1) To consider only variants that have emerged through the course of evolution, we automatically removed mutations present in the ancestor (AN). (2) We then went on to manually curate all mutation predictions. We did this by aligning the mapping tracks (profiles) of all resequenced genomes  and screening all initial candidate mutations. The overwhelming majority of variant calls were visually shared across all evolved clones but not called universally due to extremely low statistical support, or low local coverage in some of the genomes. The final dataset hence only contained 25 predicted variants  and exported these for each of the 10 evolved single clones as Variant Call File (VCF) format.Variant annotation. We annotated filtered variants from converted VCF files with breseq v0.33.2 [breseq’s Genome Diff (GD) file format using the command “gdtools VCF2GD”. We then annotated all genomes in GD format jointly by running the command “gdtools ANNOTATE” and specifying original S. arctica genome assembly (Sarc4) in GenBankFormat (GBK) as reference. Mutations were tabulated and sorted. Finally, we counted and categorized both observed mutations and nonsynonymous and synonymous sites at risk across the reference genome using the command “gdtools COUNT -b” for statistics and the calculation of the compound dN/dS ratio. v0.33.2  for convS1 Data3. “pp_se” the maximum likelihood estimate of density—the standard deviation of all out-of-bag samples used in fitting—in kg/m3.“V_au” is sedimentation rate in AU OD600 units per second. “R_meters” is the mean radius of cells for the genotype and environment. “Species,” genotype, temp, and hours_growth are information for aggregating the data based on genotype and environment. “V_mu” is the maximum likelihood estimate of cellular sedimentation rate—the mean of all out-of-bag samples used in fitting—in meters per second. “V_se” is the standard deviation of cellular sedimentation rate across all out-of-bag samples in the fitting. “pp_mu” the maximum likelihood estimate of density—the mean of all out-of-bag samples used in fitting—in kg/m(XLSX)Click here for additional data file.S2 DataAdapted from [Appendix Table 1. “classification” species are from his table annotation. “salt_percent” is the percent salt reported in the table. “V_meters_per_second” is the velocity reported, converted from meters per day to meters per second. “Pf” is the density of the media based on the reported temperature and salt concentration, estimated by the data from Millero and Huang. “R_meters” is the mean radius in meters. When a range was given, this is the average of that range. “Pp_” is the density of the sample in kg/m3. “Pp” is the excess density kg/m3. “V_exp” is the expected velocity in the seawater used in our experiments (37.4 g/l).ted from  Appendix(XLSX)Click here for additional data file.S3 Data3. “density” density in kg/m3.“temp_C” temperature in degrees Celsius. “salinity” salt in g/l. “density_kgm3” excess density in kg/m(XLSX)Click here for additional data file.S4 Data“batch” is an arbitrary alphabetical letter referring to the date the replicate was performed (either D or E). “rep” is the replicate within batch/day (rep1 or rep2). “geno” is the genotype information . “time” is the time the sample was collected in units’ hours. “sel_cond” is the condition of the selection—either the initial population (“init”), no selection for sedimentation rate (“NO_SEL”), selection for sedimentation rate (“YES_SEL”). “NOT_CLUMPY” is the count of microcolonies with not clumpy phenotype. “YES_CLUMPY” is the count of microcolonies with clumpy phenotype. “cell_density” is the measured cell density at time of dilution after period of growth. “N” is the total population growth calculated from cell_density per fold dilutions (1/250 per day). “gen” is the number of generations of growth based on cellular counts. Note for both “N” and “gen” that due to the synchronized coenocytic growth the cultures, they underwent a large increase in cell count after 48 hours. “date” is the date the replicate was performed (April or May 2019). “temp” is the temperature of the competition. The data underlying this figure may be found in (XLSX)Click here for additional data file.S1 Fig(A) Measurements of technical error or heritability of traits show that the phenotypes measured for the evolved isolates were highly heritable. Arithmetic mean values for 4 key phenotypes were calculated for each genotype + environment combination  and plotted against individual measured values  across replicates performed on separate days (“batches”—colors). Environments included different temperatures and different timings after cell cycle synchronization. Horizontal facet strips indicate the phenotype, its unit of measure, and the heritability of the trait, i.e., the variance explained by the mean genotype + environment value. Gray lines represent where X = Y.(TIF)Click here for additional data file.S2 Fig= 36.4 g/L), 2X , 0.5X , PBS = phosphate-buffered saline 1X, Proteinase K 200 μg/mL and sonication . Representative images on the right show the dissociation of S1 clumps after sonication. Bar, 50 μm. (C) Percentage of clumps formed after 72 hours of all evolved mutants per number of transfers (n > 200 coenocytes at cell release per strain). Example images on the left of detached or clumpy cells after cell release. Bar, 50 μm. (D) Raw data for the frequency with respect to time of each independent replicate of strains ancestor (AN) and clumpy evolved isolates S01 and S03 in head-to-head competitions with the ancestral strain . Each point represents a measurement of a given replicate at the given time point under the 2 selection regimes , and error bars are 95% confidence intervals. Lines are least-squares regressions of each replicate across time points to guide the eye. The data underlying this graph may be found in S4 data; see n = 13 clumps for AN and >50 clumps for all evolved mutants of at least 5 attached cells together). (F) Linear correlation between clump size of the fast-settling mutants  and number of cells per clump compared to the ancestral strain (AN). The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1.(A) Approximation of the constant population size derived from nuclear doublings over the course of 48 hours. Nuclei numbers were estimated from total cell number and average number of nuclei per cells (after cell sorting). At 24 hours, a constant fraction of cells has been transferred to fresh medium, which induces a bottleneck. The data underlying this figure may be found in (TIF)Click here for additional data file.S3 Fign > 28 coenocytes each). The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1.(A) Cell wall staining show presence of a separating cell wall between individual cells in the clumps of all fast-settling mutants. Bar, 50 μm. (B) Experimental design and measurements of S1 clump formation by aggregation. AN and S1 cells are separated by sonication and stained with different cellular dyes before being mixed together for a complete life cycle of 72 hours. Clumps of each separate or mixed colors are then counted by microscopy. Circles represent the number of clumps observed in each condition (more than 5 cells attached together). We show that when S1 is mixed with either AN or itself (1:1 ratio), it mostly forms monocolored clumps. Representative images of monocolored clumps. Bar, 50 μm. (C) Mean coenocyte perimeter over time (10 cell traces per strain) at 12°C, aligned to time 0, reveals discreet differences in coenocyte perimeter and life cell stages among fast-settling mutants. Orange squares represents the flip time point in each trace. (D) Duration of growth, cellularization, and post-flip represented as box-plots at 12°C ((TIF)Click here for additional data file.S4 FigS. arctica AN and evolved mutants during the life cycle at 12°C. Every trace represents an independent experiment. (B) Average perimeter measured from fixed cells every 12 hours over a complete life cycle of 72 hours at 12°C shows that fast-settling mutant increase their size upon cell release. Every trace represents an independent experiment (n > 130 measurements per time point for each independent experiment). (C) Average perimeter of fast-settling cells and clumps at 36 and 72 hours, respectively, show that S4 and S9 single cells and clumps have a smaller size when compared to S1 at 12°C. Every square represents an independent experiment, and the white circle represents the median. (D) Excess cellular density of fast-settling mutants (before cellularization) and clumps (after cellularization) at 36 and 72 hours, respectively. Every square represents an independent experiment, and the white circle represents the median. (E) Distributions of nuclear content of S. arctica AN and fast-settling mutants during the life cycle at 17°C measured by microscopy of DAPI-fixed cells (n > 400 coenocytes per time point). (F) Distributions of nuclear content of S. arctica AN and fast-settling mutants during the life cycle at 12°C measured by microscopy of DAPI-fixed cells (n > 420 coenocytes per time point for each independent experiment). (G) Quantification of mean DNA content per time point for fast-settling mutants grown in marine broth at 12°C. Every trace represents an independent experiment (n > 420 coenocytes per time point for each independent experiment). (H) Nuclear doubling time, calculated by linear regression of mean nuclear content at time points from 0 to 24 hours at 12°C. Every square represents an independent experiment, and the white circle represents the median (n > 420 coenocytes per time point for each independent experiment). (I) Boxplots of cell volume measurements of DAPI-stained fixed cells at 12°C. For 1-, 4-, 16-, and 64-nuclei cells. Cells with 1 nucleus represent newborn cells at the end of the experiment (n > 50 coenocytes per DNA content). (J) Boxplots of nuclear number-to-volume ratio of DAPI-stained cells at 12°C. Every square represents an independent experiment, and the white circle represents the median (n > 300 coenocytes per strain). (K) Temporal transcript abundance of genes mutated in intermediate and slow-settling phenotypes across the native life cycle of S. arctica. The data underlying these graphs may be found in https://doi.org/10.6084/m9.figshare.18319232.v1.(A) Sedimentation rates of (TIF)Click here for additional data file.S5 FigSphaeroforma species during the life cycle at 17°C of DAPI-fixed cells measured by microscopy. Note that S. tapetis is asynchronous compared to all other species (n > 300 coenocytes per time point for each independent experiment). (B) Average perimeter measured from fixed cells every 12 hours over a complete life cycle of 72 hours at 17°C of Sphaeroforma sp. Every trace represents an independent experiment (n > 140 measurements per time point for each independent experiment). (C) Average perimeter of Sphaeroforma sp. cells and clumps at 0, 24, and 48 hours, respectively, show that S. gastrica, S. nootakensis, S. napiecek, and S. sirkka are smaller in size at 17°C. Every square represents an independent experiment, and the white circle represents the median (n > 140 measurements per time point for each independent experiment). (D) Coenocyte volume at flip, measured from time-lapse movies, show that all Sphaeroforma species apart from S. arctica have significantly smaller coenocyte volume at flip (n > 50 coenocytes per strain). (E) Boxplots of cell volume measurements of DAPI-stained fixed Sphaeroforma sp. coenocytes at 17°C. For 1-, 4-, 16-, and 64-nuclei cells. Cells with 1 nucleus represent newborn cells at the end of the experiment. (n > 50 coenocytes per DNA content). The data underlying this figure may be found at https://doi.org/10.6084/m9.figshare.18319232.v1.(A) Distributions of nuclear content of (TIF)Click here for additional data file.S1 VideoTime interval between frames is 0.5 second. The movie is played at 7 frames per second (fps). We can observe rapid cell sedimentation in S1, S4, and S9 when compared to AN. Note that S1 clumps are bigger and sediment faster than the 2 other mutants. The movie was acquired for cultures pregrown for 72 hours at 12°C and obtained with a mobile phone (Samsung A20).(MP4)Click here for additional data file.S2 VideoTime interval between frames is 30 minutes. The movie is played at 7 fps. Four distinct cells can be seen undergoing a full life cycle at 12°C with the release of detached newborn cells for AN or clumps for the mutants. Bar, 50 μm.(MP4)Click here for additional data file.S3 VideoTime interval between frames is 15 minutes. The movie is played at 7 fps. Clumps can be seen being formed after plasma membrane invaginations followed by cell release. Obtained with an epifluorescence microscope. Bar, 50 μm.(MP4)Click here for additional data file.S4 VideoS. tapetis, the capacity to clump of S. gastrica and S. nootakensis, and the small cell size of all Sphaeroforma species compared to S. arctica. Bar, 50 μm.Time interval between frames is 30 minutes. The movie is played at 7 fps. Note the asynchrony of (MP4)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


In the original article, there were mistakes in In Meanwhile, there were mistakes in Consequently, a correction has been made to “RESULTS”, “Survival Outcomes”: paragraphs 1 and 2:“In univariate analysis of the 642 patients who received intrapleural IL-2, having had any prior radiotherapy (p = 0.007) and having had extracranial radiotherapy (p = 0.003) were associated with longer PFS. Multivariate analysis revealed that having had any radiotherapy and extracranial radiotherapy were independent predictors of PFS Table 2.“In univariate analysis of the 642 patients who received intrapleural IL-2, having had any prior radiotherapy (p < 0.001) and extracranial radiotherapy (p < 0.001) were associated with longer OS. Multivariate analysis revealed that having had any radiotherapy and extracranial radiotherapy were independent predictors of OS Table 3.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


Silicate weathering and physical erosion markedly increased during the rapid warming of the PETM, allowing climatic recovery. The Paleocene-Eocene Thermal Maximum  was a geologically rapid warming period associated with carbon release, which caused a marked increase in the hydrological cycle. Here, we use lithium (Li) isotopes to assess the global change in weathering regime, a critical carbon drawdown mechanism, across the PETM. We find a negative Li isotope excursion of ~3‰ in both global seawater (marine carbonates) and in local weathering inputs . This is consistent with a very large delivery of clays to the oceans or a shift in the weathering regime toward higher physical erosion rates and sediment fluxes. Our seawater records are best explained by increases in global erosion rates of ~2× to 3× over 100 ka, combined with model-derived weathering increases of 50 to 60% compared to prewarming values. Such increases in weathering and erosion would have supported enhanced carbon burial, as both carbonate and organic carbon, thereby stabilizing climate. The Paleocene-Eocene Thermal Maximum (PETM) was a geologically rapid hyperthermal event that occurred around 55.9 million years (Ma) ago. It was associated with substantial release of carbon to the atmosphere, which caused up to 8°C surface ocean warming in high latitudes  and alkalinity to the oceans  had a rapid “onset phase” of around 1 to 5 ka, a “body phase” of around 70 to 100 ka characterized by stable but anomalously low δ2 drawdown), as documented in modern river systems  to clay formation , which is known as the weathering congruency  rate to the total [erosion (E) + chemical weathering (W)] denudation rate (D) , and hence high riverine δ7Li, but lower Li fluxes. In high-intensity regimes, there is little primary rock dissolution, and instead secondary clays dissolve, leading to a low δ7Li, but also a low Li flux  changed during this hyperthermal event. Unlike many other geochemical signatures, Li isotopes provide a global weathering proxy that is dominantly controlled by silicate weathering processes. Although evaporite weathering can affect the local composition of some surface waters, on a global scale these sources appear to have relatively little impact on the seawater Li isotope compositions . Combined with Sr/Ca values of <2.6 mmol/mol, these observations suggest that our samples have not been significantly diagenetically altered. We note that there is typically a fractionation factor between bulk carbonate and seawater Δ7Licarb-SW (δ7Licarb − δ7LiSW) of −6.1 ± 1.3‰ (We report Li isotope ratios from three marine carbonate sections  or when W/D is very high   to be unlikely.An alternative explanation for the change in riverine δ related  ,solving) . The lat/D world . Such a W/D values, whereby rock dissolution outpaces clay formation and dissolved δ7Li decreases as a result (W/D would have required an even greater increase in erosion (sediment flux) rates than the weathering increase. This agrees with the modern global large river behavior, where increasing runoff causes erosion (sediment flux) rates to increase faster than silicate weathering rates (fig. S18)  appear surprising and beyond those likely to be forced by plausible changes in the mean annual precipitation. However, climate models for the PETM, based on our modern understanding of the link between warming and extreme precipitation (5 to 106 years (Sustaining increases in erosion rates over very long (Ma) time scales would require changes in the geodynamic forces controlling uplift, because over these long time scales the erosional system tends toward a steady state between uplift and erosion rates. However, the PETM reflects a clear disequilibrium state, in both climate and related surface processes such a fluvial incision. Our model results based on the Li isotope excursion suggest that erosional disequilibrium due to fluvial incision, whether of primary material or clays, persisted for several hundreds of thousand years. Independent models of the non–steady-state responses of erosion (riverine sediment flux) rates to changes in rainfall suggest that erosion perturbations can persist on time scales of 102 release, reflecting the relatively long time scale over which weathering regulates the carbon cycle and Pco2 levels. In comparison, the climate perturbation at the Cenomanian-Turonian boundary (Ocean Anoxic Event 2) required a silicate weathering increase of ~1.5 to 2.5× for ~300 to 500 ka combined with a decrease in riverine δ7Li of ~12‰  and ~10-ka (cGENIE) time scale of peak CO2 sequestration by marine carbonate precipitation and burial , which caused both an increase in silicate weathering and a marked increase in global sediment erosion rates. This, in turn, promoted not only CO7Li) using several different methods. At sites 865 and 1051, bulk carbonates were analyzed using a weak leach of 1 M Na acetate buffered to pH 5 with acetic acid  to determine elemental ratios such as not only Li/Ca, Mg/Ca, and Sr/Ca but also Al/Ca and Mn/Ca, to determine whether any silicate particles had been leached. The methods are described in detail elsewhere (7Li for a solution of 5 ng/ml was achieved (using 1011-ohm resistors and an uptake rate of ~100 μl/min). Therefore, for a solution of 0.5 ng/ml, signal intensity was still 13 pA, making it easily possible to analyze samples at this low concentration. The results of several different rock standards analyzed by this method have been reported previously . Of particular relevance for low-concentration carbonate samples is that seawater analyzed at concentrations of 0.5 ng/ml (n = 6) yields a long-term analytical uncertainty of ±0.4‰ (2 SD).Samples were purified for Li with a two-column method, using AG 50W-X12 resin and dilute HCl as an eluent (