Transient receptor potential (TRP) channels are known as temperature receptors. Each channel has an activation temperature in vitro within the physiological temperature range. Mice deficient in specific TRP channels show abnormal thermal behaviors. However, the role of TRP channels in mouse thermal behavior is not fully understood. We measured thermal behavior using a new type of thermal gradient system, where mice can freely move around the ring floor, thereby avoiding the stereotypical habit that mice have of staying in a corner, as occurs in a rectangular system. With this system, we can also analyze various factors, such as “Spent time,” “Travel distance,” “Moving speed,” and “Acceleration,” to provide more accurate information about mouse behaviors. Further analysis using this system would lead to a better understanding of the molecular basis of thermal behaviors in mice, which could help us develop ways of making humans comfortable in different temperature conditions.The online version contains supplementary material available at 10.1186/s12576-022-00835-3. Ambient temperature is received by the skin and sensory neurons, and the information is transmitted to the central nervous system, which leads to various autonomous and non-autonomous responses that control body temperatures. The thermoregulatory system works in central neural networks. When sensory information is relayed to the cerebral cortex through the thalamus, this temperature information is perceived and discriminated . However−/− mice showed normal core body temperatures under healthy conditions.Transient receptor potential (TRP) channels are sensors for various kinds of chemical and physical stimuli, and some of them, called thermosensitive TRP channels, are activated by temperature changes –5. Curre−/− or TRPV3−/− mice were reported to show temperature preferences for the cooler side compared to WT mice. /[t(a)–t(b)]. The (b) refers to the time point before the (a) time point. The v and t indicate speed and time position, respectively.Behavioral assays were performed between 10:00 and 20:00. The day before the thermal gradient test, each mouse was acclimated for 30 min in the thermal gradient apparatus, while its floor was at room temperature (23–24 ℃). The stability of the gradient was monitored each day using a thermometer . Each mouse was placed in the temperature gradient apparatus individually. Behavioral data were collected by videotaping for 60 min and were automatically analyzed for “Spent time,” “Travel distance,” or “Moving speed” by ANY-maze software (Stoelting Co.). The ANY-Maze software recognizes speed when the mouse moves, but no speed is calculated when the mouse does not move. Moving speed was calculated about every 0.1 s with ANY-maze software. We then used this data to calculate ‘‘Acceleration” with the following equation: m/sWe inserted a temperature logger  to measure body temperature in the intraperitoneal cavity or subcutaneously in the back. After surgery, mice were allowed to recover for 1 week prior to any behavioral testing. The logger was controlled by Felica  to start or finish the measurement. The results were loaded using Felica and monitored by the nano tag viewer software. We began recording measurements before the behavior test. A mouse was housed for 30 min and transferred to the apparatus for a duration of 60 min. After the test, the mouse was housed for 30 min again, and we stopped recording the temperature.t test or a one-way ANOVA with Tukey correction using OriginPro 2020b . Error bars in the figures represent standard errors of the mean.The results of behavioral assays were analyzed using Welch’s We analyzed thermal behaviors in mice using a Thermal Gradient Ring system in which the ring floor was divided into 24 zones from cold to hot, with the same temperatures occurring in two zones, allowing mice to freely move based on their temperature preference Fig. A. We firMice are nocturnal animals and move more at night, at which time they also have higher body temperatures. Therefore, to examine the effects of body temperature on thermal behaviors, we put a small logger to measure the mouse’s body temperature . The logBecause both the subcutaneous and core temperatures changed similarly and because the temperature logger measurement in the intraperitoneal space was not affected by the ring floor temperature, we compared only the subcutaneous body temperatures between WT mice and mice lacking TRP channel genes. Subcutaneous body temperatures did not differ significantly between mice examined in the daytime Fig. E and nigTrpv1, Trpv3, Trpv4, Trpm2, Trpm8, or TrpA1 using the Thermal Gradient Ring system with different behavioral parameters. When we analyzed "Spent time" on the floor with no thermal gradient, all genotypes visited all zones equally and showed no spatial preference or avoidance  compared to WT mice. TRPM8−/− and TRPV3−/− mice stayed longer in the lower temperature zones, as previously reported [−/− mice showed obvious differences in the time spent at the 11.5 ℃ to 17.3 ℃ zone  gradually moved to the warm zone over time, similar to the WT mice that stayed longer in the zone of around 34 ℃, and the other group  continuously stayed at about 30 ℃. To examine the difference among WT, TRPA1−/−, and TRPM2−/− mice, we extracted data during the 40–60 min window, when mice tended to settle at specific temperatures   showed reduced Travel distance across time, which was similar to WT mice, and the other  traveled further than WT mice and kept travelling throughout the entire 60-min experiment. Interestingly, this grouping was the same as the one we found for the Spent time analysis  in each temperature zone using the 11.5–43.6 ℃ gradient setting. Travel distance was shorter at the low temperatures for all mice except for TRPM8sis Fig. B. This f−/− mice had a consistent Moving speed, even at the coldest zone of 11.5 ℃, while the other genotypes, including WT mice, showed a higher Moving speed in the coldest zone  or hot (54.9 ℃) temperature zones, indicating that TRPA1−/− mice avoided noxious temperatures. These results suggest that TRPA1−/− mice have a high temperature sensitivity.In one previous report using a cold plate assay, TRPA1 WT mice , but ano−/− mice . Previoueferable . Our res-/- mice were previously reported to lose a preference when presented a choice between 33 ℃ and 38 ℃ in the two-plate assay, but when offered plates of 33 ℃ or 28 ℃, they spent longer at 33 ℃ like WT mice [-/- mice spent similar amounts of time in the 31.9 ℃ to 37.7 ℃ zones, whereas WT mice preferred the 34.8 ℃ zone over the 31.9 ℃ or 37.7 ℃ zones  showed a peak of Spent time at about 34 ℃ and these mice traveled less, and the other group  showed a peak of Spent time at about 28 ℃ to 30 ℃ and travelled more  Transition of subcutaneous (black) and core (gray) temperatures of WT mice in three consecutive days in the home cage. Nighttime (when the light is turned off from 20:00 to 8:00) is indicated by gray bars. (B) Transition of subcutaneous (black) and core (gray) temperatures of WT mice on the Thermal Gradient Ring during the daytime (13:00–18:00). The Thermal Gradient Ring system is turned on at the 30-min mark of the mouse being placed in the apparatus and the ring floor temperatures are considered stable at 60 to 120 min (shown in gray). (C) Transition of subcutaneous (black) and core (gray) body temperatures before, during (a gray bar), and after a thermal behavior test during the nighttime (21:00–6:00). (D) Transition of subcutaneous temperatures before, during (a gray bar), and after a thermal behavior test in wild-type mice and in mice lacking TRP channels during the nighttime (21:00-) (n=3/genotype). Averaged subcutaneous temperatures of all genotypes in the first and last 20 min are shown in the gray box. Error bars represent standard errors of the mean.Additional file 2: Figure S2. ‘‘Spent time’’ in the condition from 11.5 to 43.6 ℃. (A) “Spent time” for all genotypes without a thermal gradient across a 60-min experiment . (B) “Spent time” for all genotypes at each temperature zone from 11.5 ℃ to 43.6 ℃ across 60 min, as broken down by the first (top), middle (middle), and last (bottom) 20 min of the experiment. . All error bars represent standard errors of the mean. (C) Comparison of Spent time of wild type and TRPM8-/- mice at 11.5 oC in 60 min.Additional file 3: Figure S3. “Spent time” under the condition from 19.7 ℃ to 35.3 ℃. (A) “Spent time” for all genotypes at each temperature zone under the condition of 19.7 ℃ to 35.3 ℃ in a 60-min experiment . (B) “Spent time” for all genotypes at each temperature zone under the condition of 19.7 ℃ to 35.3 ℃ across 60 min, as broken down by the first (top), middle (middle), and last (bottom) 20 min. Error bars represent standard errors of the mean. (C) Comparison of Spent time of wild type and TRPA1-/- mice at 33.9 oC in the 40–60 min period.Additional file 4: Figure S4. “Travel distance” under the condition of 11.5 ℃ to 43.6 ℃. (A) “Travel distance” of all genotypes without a thermal gradient across a 60-min experiment . (B) Transition of “Travel distance” across whole temperature zones for each genotype in the first, middle, and last 20 min of an experiment. The color of each genotype is the same as shown in panel A. Mice were classified into two groups, as shown by the brackets. * p < 0.05, ** p < 0.01 vs. Wild type. ns not significant. (C) “Travel distance” of all genotypes at each temperature zone from 11.5 ℃ to 43.6 ℃ across the 60 min, as broken down by the first (top), middle (middle), and last (bottom) 20 min of the experiment. Error bars represent standard errors of the mean.Additional file 5: Figure S5. “Moving speed” under the condition of a 11.5 ℃ to 43.6 ℃ gradient. (A) “Moving speed” of all genotypes without a thermal gradient across a 60-min experiment . (B) Transition of “Moving speed” across all temperature zones for each genotype in the first, middle, and last 20-min interval of a 60-min experiment. The color of each genotype is the same as in panel A. Mice were classified into two groups, as shown by the brackets. (C) “Moving speed” of all genotypes at each temperature zone under the condition from 11.5 ℃ to 43.6 ℃ across a 60-min experiment, as broken down by the first (top), middle (middle), and last (bottom) 20 min. All error bars represent standard errors of the mean.


To better understand the sensation of bladder “pressure” and “discomfort”, and how they are similar or distinct from the “pain” and “urgency” symptoms in IC/BPS and OAB.IC/BPS and OAB patients rated their bladder pain, pressure, discomfort, and urinary urgency on separate 0–10 numeric rating scales (NRS). Their NRS ratings were compared between IC/BPS and OAB, and Pearson correlations were performed.p < 0.001). The correlations between urgency and pain, and between urgency and pressure were weak in OAB (0.21 and 0.26). The correlation between urgency and discomfort was moderate in OAB (0.45). The most bothersome symptom of IC/BPS was bladder/pubic pain, while the most bothersome symptom of OAB was urinary urgency and daytime frequency.Among IC/BPS patients (n = 27), their mean numeric ratings of pain, pressure, discomfort, and urinary urgency were almost identical . The three-way correlations between pain, pressure, or discomfort were very strong . Among OAB patients (n = 51), their mean numeric ratings of pain, pressure, and discomfort  were significantly lower than urgency (6.1 ± 2.6, IC/BPS patients interpreted bladder pain, pressure, or discomfort as the similar concepts and rated their intensity similarly. It is unclear whether pressure or discomfort provide additional information beyond pain in IC/BPS. Discomfort may also be confused with urgency in OAB. We should re-examine the descriptors pressure or discomfort in the IC/BPS case definition.The online version contains supplementary material available at 10.1186/s12894-022-01164-8. The descriptors “pain, pressure or discomfort” are used collectively to describe the abnormal bladder or pelvic sensations associated with interstitial cystitis/bladder pain syndrome (IC/BPS). For example, the AUA/SUFU Guideline defines IC/BPS as “an unpleasant sensation  perceived to be related to the urinary bladder, associated with lower urinary tract symptoms […]” . The ESSPresumably “pressure” or “discomfort” were included along with “pain” because not all IC/BPS patients complained of pain. Humphrey et al. noted that some IC/BPS patients mentioned bladder discomfort and pressure in addition to bladder pain during qualitative interviews . Bogart pressure is the urgency. When I get the pressure I’ve gotta make a dash for the bathroom because it is very uncomfortable.” [Overactive bladder (OAB) syndrome is defined by the cardinal symptom of urinary urgency . Howeverrtable.”  About 30rtable.” . This cartable.” , 2. In oIn this study, we asked IC/BPS and OAB patients to rate their bladder “pain”, “pressure”, “discomfort”, and urinary “urgency” sensation on separate numeric rating scales. We then compared their mean ratings and correlations, to discover group differences between IC/BPS and OAB patients. Our objective is to better understand the sensation of bladder “pressure” and “discomfort”, and how they are similar or distinct from “pain”  and “urgency”  in the two clinical populations. This study may provide further clarity on the distinguishing factors between bladder pain, pressure, discomfort and urgency symptoms.Patients who presented to urology clinic and were diagnosed clinically with IC/BPS or OAB by one urologist (HHL) consented to participate in this questionnaire study between late 2012 and early 2014 (the timing of the enrollment was related to funding). The clinical diagnosis of IC/BPS and OAB was consistent with the AUA IC/BPS Guideline  respectiThink about the pain associated with your bladder. On average, how would you rate these symptoms in the past 4 weeks? Think about the pressure associated with your bladder. On average, how would you rate these symptoms in the past 4 weeks? Think about the discomfort associated with your bladder. On average, how would you rate these symptoms in the past 4 weeks? Think about the urgency to urinate associated with your bladder. On average, how would you rate these symptoms in the past 4 weeks? Patients completed the following questions to rate their bladder pain, pressure, discomfort, and urinary urgency on separate 0–10 numeric rating scales (NRS):We intentionally chose not to define these descriptors for the patients because we wanted the patients to interpret their bladder sensations for themselves when rating them.In addition, patients were also asked to identify their single most bothersome symptom over the past 4 weeks among the followings: (1) pain, pressure, discomfort in the pubic or bladder area, (2) pain, pressure, discomfort in the area between your perineum  or vaginal area , (3) pain, discomfort during or after sexual activity, (4) strong need to urinate with little or no warning, (5) frequent urination during the day, (6) frequent urination at night, (7) sense of not emptying your bladder completely, (8) other. For the most bothersome symptom question, we did not separate out pain from pressure or discomfort. The exact wordings of these questions were identical to the SYM-Q questionnaire administrated in the MAPP Study.As mentioned earlier, the clinical diagnosis of IC/BPS and OAB was based on patient chief complaint, history, physical examination findings, and laboratory and diagnostic workup as indicated in accordance to the AUA Guidelines on IC/BPS and OAB , 5. The Additional questionnaires used to characterize the cohorts included: (1) genitourinary pain index (GUPI) , (2) intp < 0.05. Since formal comparison of pain, pressure, and discomfort ratings have never been done in these two populations, a priori power calculation was not performed.For comparisons between IC/BPS and OAB cohorts, Wilcoxon sum-rank tests and chi-square tests were used to compare continuous and categorical data respectively. Paired t-tests were used to compare within group variables of interest. Spearman’s rank coefficient was used to assess correlation between quantitative variables. All analyses were performed using R version 4.1.0 with statistical significance set at p = 0.003, < 0.001, 0.002, respectively), while OAB participants had significantly higher scores on OAB-q, ICIQ-UI, and ICIQ-OAB .There were no statistical differences in age, sex, and race between the IC/BPS and OAB cohorts. Both men and women were represented, and the cohorts were predominantly white , see Table Among IC/BPS patients (n = 27), their mean numeric ratings of pain, pressure, discomfort, and urinary urgency were almost identical . There was a significantly bigger difference in ratings between bladder pain and urgency (urgency minus pain = 4.1 ± 3.2) than between bladder pressure and urgency  and between bladder discomfort and urgency  among OAB patients.Among OAB patients (n = 51), their mean numeric ratings of pain, pressure, and discomfort  were significantly lower than urgency , see Table Among OAB patients, the correlations between pain and urgency , and between pressure and urgency were weak 0.21 and 0.26). The correlation between discomfort and urgency was moderate in OAB (r = 0.45). The scatterplots are shown in Fig.  and 0.26p < 0.001). Frequency during the day was identified as the most bothersome symptom in 41.2% of OAB patients and 18.5% in IC/BPS patients (p < 0.05). In addition, 25.5% of OAB patients reported urinary frequency at night as the most bothersome symptom.The most bothersome symptom for over half of IC/BPS patients (51.9%) was pain, pressure or discomfort localized in the bladder and pubic region rather than the perineal area in men or vaginal area in women (7.4%). While a small percentage of OAB patients (9.8%) did complain of pain in the bladder and pubic area , the most bothersome symptoms were urinary urgency and daytime frequency (41.2% for both). Approximately 41.2% of OAB patients reported urgency as their most bothersome symptom in contrast to ~ 4% in IC/BPS patients  IC/BPS patients also reported non-zero urinary urgency or frequency along with pain.Among OAB patients, 50 of 51 (98%) reported non-zero urinary urgency, 24 (47.1%) reported non-zero bladder pain, 36 (70.6%) reported non-zero pressure, and 39 (76.5%) reported non-zero discomfort.In this study, we compared the bladder pain, pressure, discomfort, and urgency sensation between IC/BPS and OAB patients, in order to better understand how bladder “pressure” and “discomfort” are similar or distinct from “pain”  and “urgency”  in the two clinical populations.In our cohort of IC/BPS patients, there was no patient who reported any pressure or discomfort without pain. Every single patient reported pain, pressure, AND discomfort. Thus we were unable to compare IC/BPS patients with pressure or discomfort alone versus those with pain. Their mean numeric ratings of pain, pressure, or discomfort were nearly identical. In addition, the three-way correlations between pain, pressure, or discomfort were very strong . In fact, the correlation between pain and discomfort ratings was extremely high (r = 0.95), almost approaching a straight line Fig. c. This sAmong OAB patients, the correlations between urgency and pain, and between urgency and pressure were very weak (0.21 and 0.26 respectively). The correlation between urgency and discomfort was moderate in OAB (r = 0.45). This suggest that OAB patients can distinguish urinary urgency from pain and pressure, but this distinction is less clear cut between urgency and discomfort symptoms. It is conceivable that “urgency” may be confused with “discomfort” by some OAB patients, or at least the distinction between these terms may vary among patients. This observation is consistent with a qualitative study which showed that OAB patients described their urgency as “pressure” or “discomfort [Pain, on the other hand, is a much better descriptor distinguishing IC/BPS from OAB. Even though a subset of OAB patients reported non-zero bladder pain, their NRS of pain was mild (2.0 ± 2.6 out of 10) and was significantly lower than those reported by IC/BPS. More importantly, it was their most bothersome symptom that distinguished OAB from IC/BPS. The most bothersome symptom of IC/BPS patients is pain in the bladder/pubic area. The most bothersome symptoms of OAB patients are urinary urgency and frequency. These findings clearly confirmed “pain” and “urgency” as cardinal and most bothersome symptom of IC/BPS and OAB, respectively.The report of non-zero  bladder pain in OAB might raise the questions whether we could have misdiagnosed IC/BPS for OAB, and vice versa. However, this possibility was low since 100% of our IC/BPS patients reported pain while 98% of OAB our patients reported urgency. The distribution of their single most bothersome symptoms also aligned well with the accepted case definitions of IC/BPS and OAB. Based on the current evidence, it is possible that the subgroup of OAB patients with pain may have different underlying pathophysiology  from OAB patients without pain –16. ThisPotential limitations of the study included the small sample size, and that our cohort was predominantly white in the United States. It is possible that patients in other ethnic groups, cultures, or in other countries might interpret these bladder sensations differently from our cohort. Future studies with larger sample size and a more diverse demographics or ethnic makeup should be conducted to further examine our findings.In future studies, it would also be beneficial to examine whether the discomfort was related to “fear of pain” (which will point to IC/BPS) versus “fear of leakage” (which will point to OAB).IC/BPS patients interpreted bladder pain, pressure, or discomfort as the similar concepts and rated their intensity similarly. It is unclear whether the descriptors pressure or discomfort provide additional information beyond the pain descriptor in IC/BPS. Bladder discomfort may also be confused with urgency in OAB. We should re-examine the descriptors pressure or discomfort in the IC/BPS case definition.Additional file 1. Supplemental Table 1. Characteristics and demographics.


Error in Figure/Table LegendIn the published article, there was an error in the legend for Figure 3 The comparison and overlap of infected pathogens between mNGS and laboratory culture in all 205 patients with CAP. (A) Bacteria levels; (B) fungi level; and (C) virus level.In the published article, there was an error in the legend for Figure 4 Infected pathogens detected by mNGS in severe and non-severe patients with CAP. (A) bacteria levels; (B) fungi level; and (C) virus level.In the published article, there was an error in the legend for Figure 6 Infected pathogens detected by mNGS in immunocompetent and immunocompromised patients with severe pneumonia. (A) Bacteria levels; (B) fungi level; and (C) virus level.Error in Figure/TableOrientia tsutsugamushi was divided in Part C “Protozoa”, which should be moved to Part A “Bacteria”. The corrected In the published article, there was an error in Orientia tsutsugamushi was divided in Part C “Protozoa”, which should be moved to Part A “Bacteria”. The corrected In the published article, there was an error in Orientia tsutsugamushi was divided in Part C “Protozoa”, which should be moved to Part A “Bacteria”. The corrected In the published article, there was an error in Incorrect Supplementary MaterialOrientia tsutsugamushi was divided in Part C “Protozoa”, which should be moved to Part A “Bacteria”.In the published article, there was an error in Supplementary Figure 2. Supplementary Figure 2. The comparison and overlap of infected pathogens between metagenomic next-generation sequencing (mNGS) and laboratory culture in 186 patients with CAP whose sample type was consistent between two methods. (A) Bacteria levels; (B) Fungi level; (C) Virus level.Orientia tsutsugamushi was divided in Part of “Protozoa”, which should be moved to Part of “Bacteria”.In the published article, there was an error in Supplementary Table 2. Supplementary Table 2. Comparison of pathogens detected by mNGS between non-severe CAP and SCAP groups.Orientia tsutsugamushi was divided in Part of “Protozoa”, which should be moved to Part of “Bacteria”.In the published article, there was an error in Supplementary Table 3. Supplementary Table 3. Comparison of pathogens detected by mNGS between immunocompetent and immunocompromised patients with SCAP.Text CorrectionOrientia tsutsugamushi was wrongly be categorized as Protozoa, which should be a bacterium. So, the number of detected bacteria and protozoan should be corrected.In the published article, there was an error. 3 Results, 3.4 Pathogens’ profile of all CAP patients according to detection methods, Paragraph 1. This sentence previously stated:A correction has been made to “The detected pathogens were divided into four kingdoms, namely, bacteria, fungi, protozoa, and viruses. A total of 56 bacteria , Chlamydia psittaci , Burkholderia cenocepacia (n=2), and Chlamydia abortus (n=2).”“A total of 22 bacteria were only detected by mNGS, including The corrected sentence appears below:Mycobacterium abscessus , Chlamydia psittaci , Burkholderia cenocepacia (n=2), and Chlamydia abortus (n=2).”“A total of 23 bacteria were only detected by mNGS, including Orientia tsutsugamushi was wrongly be categorized as Protozoa, which should be a bacterium. So, result of protozoa should be deleted.In the published article, there was an error. 3 Results, 3.4.2 Profile of fungi and protozoa, sub-title and Paragraph 1. This paragraph previously stated:A correction has been made to “3.4.2 Profile of fungi and protozoaCandida albicans  detected in 40 patients was the most frequent fungus, and 27 of cases were only detected by mNGS. Pneumocystis jirovecii (P. jirovecii) was the second common fungus detected in 12 cases by mNGS only. One case was positive of Orientia tsutsugamushi, which was detected by mNGS only  detected in 40 patients was the most frequent fungus, and 27 of cases were only detected by mNGS. Pneumocystis jirovecii (P. jirovecii) was the second common fungus detected in 12 cases by mNGS only.”In the fungi level (Orientia tsutsugamushi was wrongly be categorized as Protozoa, which should be a bacterium. So, the number of detected bacteria should be corrected.In the published article, there was an error. 3 Results, 3.5 Comparison of pathogens detected by metagenomic next-generation sequencing between severe and non-severe community-acquired pneumonia patients, Paragraph 1. These sentences previously stated:A correction has been made to “A total of 44 bacteria were identified by mNGS in 205 CAP patients. There were 14 bacteria found in both severe and non-severe CAP patients, 5 bacteria were found only in infected non-severe patients, and 25 bacteria were only detected in SCAP patients (The corrected sentence appears below:“A total of 45 bacteria were identified by mNGS in 205 CAP patients. There were 14 bacteria found in both severe and non-severe CAP patients, 5 bacteria were found only in infected non-severe patients, and 26 bacteria were only detected in SCAP patients (Orientia tsutsugamushi was wrongly be categorized as Protozoa, which should be a bacterium. So, the number of detected bacteria should be corrected.In the published article, there was an error. 3 Results, 3.6 Comparison of pathogens between immunocompromised and immunocompetent patients with severe community-acquired pneumonia, Paragraph 2. These sentences previously stated:A correction has been made to “A total of 39 bacteria were identified by mNGS from 144 SCAP patients. Among them, 28 bacteria were detected in immunocompetent cases only, 3 bacteria were detected in immunocompromised cases only, and 8 bacteria were found in both immunocompetent and immunocompromised groups.”The corrected sentence appears below:“A total of 40 bacteria were identified by mNGS from 144 SCAP patients. Among them, 29 bacteria were detected in immunocompetent cases only, 3 bacteria were detected in immunocompromised cases only, and 8 bacteria were found in both immunocompetent and immunocompromised groups.”Orientia tsutsugamushi was wrongly be categorized as Protozoa, which should be a bacterium. So, the word “Protozoa” should be deleted in Conclusions.In the published article, there was an error. Conclusions. These sentences previously stated:A correction has been made to P. jirovecii, chlamydia, and protozoa.”“mNGS is superior in detecting MTB, NTM, viruses, The corrected sentence appears below:P. jirovecii, and chlamydia.”“mNGS is superior in detecting MTB, NTM, viruses, The authors apologize for these errors and state that this do not change the scientific conclusions of the article in any way. The original article has been updated.


Three years after the outbreak of the coronavirus disease (COVID-19) pandemic, turnover among frontline nurses has increased. The participants of this study were nurses at two general hospitals in Ishikawa, Japan, receiving COVID-19 patients. An original self-report questionnaire was created based on previous research. The questionnaire was distributed to 400 nurses, and responses were received from 227 nurses (response rate: 56.8%). The factors influencing turnover intention at the facilities were having less time to relax  and wanting to receive counseling . As a strategy to prevent turnover, nurse managers should provide opportunities for nurses to receive counseling during normal working hours and pay particular attention to changes in nurses’ daily lives, such as changes in the time available for relaxation. The first case of coronavirus disease COVID-19) was reported in Wuhan, China, in December 2019 9 was rep,4,5. FroThe COVID-19 pandemic has had a substantial effect on the physical and mental health of healthcare workers who treat patients ,9. HospiThe COVID-19 pandemic has increased the demand for nurses and increased their workload. Additionally, there has been a substantial increase in the intention to resign among nurses dealing with patients on the COVID-19 frontline ,19,20,21As the largest group of healthcare providers who spend the most time with patients, nurses have been most affected by the pandemic . The gloContinuing research on turnover in nurses has identified complex relationships among organizational and individual factors . HoweverOur manuscript is compliant with the “Strengthening the Reporting of Observational studies in Epidemiology” (STROBE) guidelines .We conducted a web-based online cross-sectional survey of 400 nurses working at two medical institutions in Ishikawa Prefecture, Japan. These two medical institutions accept patients with COVID-19 infection at the request of Ishikawa Prefecture.This research used survey items from our previous study, “Factors Affecting Nurses’ Internal Transfer Intentions after the Introduction of COVID-19-Related Family Visiting Restrictions ”. In JapWe asked participants about their ages , sex, and years of experience.Questions regarding visitor guidance, assisting with visits using the patient’s smartphone, assisting the physician with online explanations to family members, delegating shopping, delegating phone calls, transporting patients who transferred to another hospital or to a nursing home, and transferring the patient to the hospital entrance upon discharge were responded to using “increased significantly”, “increased”, “no change”, “decreased”, or “decreased significantly”.Questions regarding working hours, number of days off, and sleeping hours were responded to using “increased significantly”, “increased”, “no change”, “decreased”, or “decreased significantly”.Questions regarding unhealthy diet, sleeping less, waking up more in the middle of the night, smoking more, and having less time to relax were responded to using “strongly agree”, “agree”, “disagree”, or “strongly disagree”.Participants were asked if they needed more nurses, more nursing assistants, appreciation, respect, benefits, counseling, workload reduction, infection prevention education, and childcare support. Possible responses for each item were “strongly agree”, “agree”, “disagree”, or “strongly disagree”.Nurses working in Japanese hospitals accepting COVID-19 patients were asked about their intention to resign; possible responses were “yes” or “no”.The analysis was based on reports examining burnout and turnover intention among Romanian nurses during the COVID-19 pandemic . The chrPandemic-related changes in nurses’ work content, working hours, number of days off, and sleeping hours were categorized as “increased” for “increased significantly/increased”, and other responses were categorized as “others”.To analyze pandemic-related changes in nurses’ daily lives and participants’ perception of the support they needed for working during the COVID-19 pandemic, responses of “strongly agree/agree” were categorized as “agree”, and responses of “disagree/strongly disagree” were categorized as “disagree”.χ2 Fisher direct probability tests on the association between turnover intention and participant attributes and years of experience; pandemic-related changes in work content, working hours, number of days off, sleeping hours, daily lives, and perceived support needed for nurses working during the COVID-19 pandemic.We conducted To evaluate the effects on turnover intention of participant attributes and years of experience, pandemic-related changes in work content, working hours, number of days off, sleeping hours, daily lives and perceived support needed during the COVID-19 pandemic, we performed a univariate analysis with turnover intention as the dependent variable and sex, age, and years of experience as covariates. The responses “Difficulty falling asleep”, “I have experienced increased night waking”, “Less time to relax”, and “I want to receive counseling” were shown to have a significant probability value of <5%. Thus, these were forced into the model and a stepwise binomial logistic regression analysis was conducted.The variables used for analysis were entered after checking for multicollinearity (variance inflation factor ≥10), and the statistical significance level was 5%. All data entry, tabulation, and statistical analysis were performed using SPSS Ver27 from IBM Corp. .This study was conducted with the consent of the Academic Research Ethics Review Board of the first author’s university (No. I673) and was conducted in accordance with the 1995 Declaration of Helsinki . Nursing administrators at two hospitals were requested to cooperate with the study by telephone and email. A letter of informed consent was distributed to the participants via email, informing them of the purpose and importance of the study, the survey methodology, that participation was voluntary, that participants’ responses were anonymous, so that no individuals would be identified by answering the survey, and that completion of the questionnaire implied their consent.Of the total sample of 400 nurses, 227 responded to the survey (56.8% response rate). Of these, 134 (59.0%) responded to all the survey items and their data were included in the analyses. Four (3.0%) participants were male, 130 (97.0%) were female, with 27 (20.1%) in their 20 s and 107 (79.9%) ≥30 years old. The median (range of) years of experience was 15 years (1–40): <15 years, 63 participants (47.0%); ≥15 years, 71 participants (53.0%) .n = 107, 79.9%), “Visitor guidance”, “Transferring the patient to the hospital entrance upon discharge” , and “Delegating shopping” .In terms of changes in nurses’ work content after the COVID-19 pandemic, “increased” was the most common response for “Delegating phone calls” , “Sleeping hours” , and “Number of days off” .Regarding negative pandemic-related changes in working hours, number of days off, and sleeping hours, “agree” was the most common response for “Working hours” , “I have experienced increased night waking” , and “Difficulty falling asleep” .In terms of pandemic-related changes in nurses’ daily lives, “agree” was the most common response to “Less time to relax” , “Need more nursing assistants” , and “Wants to have a pay raise” .In terms of support needed for nurses working during the COVID-19 pandemic, “agree” was the most common response to “Need more nurses” (The number of respondents who answered “Yes” to the question about turnover intention was 85 (63.4%) .n = 62, 57.9%) (p = 0.009); having ≥15 years of experience  (p = 0.004); and those agreeing to “Difficulty falling asleep”  (p < 0.001), “I have experienced increased night waking”  (p = 0.001), “Less time to relax”  (p < 0.001), “Wants to have a pay raise”  (p = 0.010), “Want to receive counseling”  (p = 0.007), and “Want to reduce my workload”  (p = 0.001)  .Binomial= 0.001) .The purpose of our study was to identify factors associated with turnover intention among nurses working in hospitals accepting COVID-19 patients during the pandemic. Of the participants, 63.4% expressed a turnover intention because of the COVID-19 pandemic. Nurses who indicated they had less time to relax and expressed a wish to receive counseling were more likely to express turnover intention than those with different responses. Break length and the frequency of social breaks are not strong predictors of nurse turnover . The impPerceived reduced relaxation time outside of work may be a predictor of turnover intention in frontline nurses. Intensive care unit nurses in charge of COVID-19 patients should relax outside of work hours to reduce perceived stress . Nurses There are several study limitations. Participants worked in a small number of hospitals in a limited geographic area, and were drawn from only general hospitals. In addition, the data available for analysis were limited: only 33.5% of the 227 participants (response rate: 56.8%) were included in the analysis. Participants in this study were asked to complete the survey through their director of nursing. The timing of the survey may have meant that some participants were intending to leave their jobs but unable to respond to this effect. This was a cross-sectional study motivated by the increased turnover rate of nurses caring for COVID-19 patients on the frontline of the COVID-19 pandemic. Therefore, it may have included nurses who intended to resign prior to the COVID-19 pandemic. A recent report on nurses’ turnover intentions has shown that work–family  conflictTo prevent turnover among frontline nurses during pandemics, it is important to be attentive to their psychological distress and provide them with access to counseling, as noted in previous studies. In addition, attention should be paid to whether nurses have reduced time for relaxation outside of work hours. Increases were most frequently reported for the workload activities of delegating phone calls, visitor guidance, and transferring patients to the hospital entrance upon discharge. Although these tasks include aspects that can be handled by nursing assistants, our findings suggest that their performance had become an additional work burden for nurses. During the COVID-19 pandemic, nursing administrators were encouraged to manage increased workloads by reallocating tasks to nurses and nursing assistants. Nursing managers are recommended to implement measures such as establishing a system where counseling is available, encouraging more relaxing activities outside of work hours, and interviewing nurses about whether they are relaxing. On the basis of our findings, we recommend that nursing managers pay attention to pandemic-related changes in nurses’ workloads and increased work burden, and create an environment where nurses can better focus on their work. In addition, nursing managers should monitor nurses’ psychological distress more closely and take prompt mitigation measures, such as shortening exposure time to COVID-19 patients and implementing job rotation. Future research should investigate the issues of increased workload, burden, and psychological distress in frontline nurses. Research is also needed to examine the effect of an organized counseling system and relaxation time for nurses on preventing nurse turnover.


Φ) simulation and analyze photosystem energy distribution. Five experiments were conducted to measure chlorophyll fluorescence parameters under different photothermal and natural drought conditions. The response model of Φ to photosynthetically active radiation (PAR), temperature (T), and the relative water content of the substrate (rSWC) was established for both cultivars. The results showed that the decreasing rate of Φ was higher at lower temperatures than at higher temperatures, with increasing PAR under well-watered conditions. The drought-stress indexes (εD) of both cultivars increased after rSWC decreased to the critical values of 40% and 29% for ‘ROC22’ and ‘ROC16’, respectively, indicating that the photosystem of ‘ROC22’ reacted more quickly than that of ‘ROC16’ to water deficit. An earlier response and higher capability of nonphotochemical quenching (NPQ) accompanied the slower and slighter increments of the yield for other energy losses (ΦNO) for ‘ROC22’  compared with ‘ROC16’ , indicating that a rapid decrease in water consumption and an increase in energy dissipation involved in delaying the photosystem injury could contribute to drought tolerance for sugarcane. In addition, the rSWC of ‘ROC16’ was lower than that of ‘ROC22’ throughout the drought treatment, suggesting that high water consumption might be adverse to drought tolerance of sugarcane. This model could be applied for drought-tolerance assessment or drought-stress diagnosis for sugarcane cultivars.Selections of drought-tolerant cultivars and drought-stress diagnosis are important for sugarcane production under seasonal drought, which becomes a crucial factor causing sugarcane yield reduction. The main objective of this study was to investigate the differential drought-response strategies of drought-resistant (‘ROC22’) and -susceptible (‘ROC16’) sugarcane cultivars via photosynthetic quantum efficiency ( Sugarcane is a major crop for sugar and bio-energy worldwide and essential for agricultural productivity and economic growth . In ChinPn), stomatal conductance (gs), PSII quantum efficiency (ΦPSII), and leaf relative water content; however, the genotypes exhibited increased nonphotochemical quenching (NPQ), malondialdehyde (MDA), proline, superoxide dismutase (SOD),and ascorbate peroxidase (APX) contents, which were higher in drought-resistant than in drought-susceptible genotypes Φ = (Fm′ − Fs)/Fm′Fv/Fm = (Fm − Fo)/FmNPQ = Fm/Fm′ − 1ΦNO = 1/[NPQ + 1 + qL(Fm/Fo − 1)], where qL = (Fo′/Fs)(Fm′ − Fs)/(Fm′ − Fo′)Φ was equivalent to and symbolized as ΔF/Fm′, ΦPSII, ΦII, or Φ2 in different studies, with the same calculation result [Parameter n result ,54,55,63VWC) was measured at a depth of 0~16 cm in each bucket via AZS-100  at 8:00 a.m., with 5 repetitions during the drought treatment. The rSWC was calculated with the formula rSWC = VWC/VWCS  were adopted to measure Φ under different air temperatures (T) using artificial light in field conditions in different seasons, calculated using the formulae transformed from the calibration model proposed to evaluate the response of Φ to photothermal backgrounds [ΦTo, ΦbL, and ΦbH represent the optimum Φ under To and the basic Φ values under low T and high T at limited PAR. Tmin, To, and Tmax represent the minimal, optimal, and maximal temperatures for sugarcane photosynthesis and vegetative growth, with values of 15 °C, 30 °C, and 40 °C, respectively, according to previous studies [ΦbL, ΦbH, ΦTo, and PAR was established as follows ΦbL=Fv/Fm measured from Exp1~3 was used for Equation (4)’s fitting, and aL, bL, aH, and bH are empirical coefficients fitted from Equations (2) and (3). The parameter Ε, fitted from Equation (4), was merely related to different sugarcane cultivars.The mean value of the Φ, Fv/Fm, PAR, T, and rSWC measured under drought and used the values to describe the relationship between ε and rSWC .We inserted Equations (2)–(4) into Equation (1) to determine the and rSWC a:(5)εD={ΦbL and ΦbH was assumed to be the same as that of ΦTo, and these values were calculated as:ΦbLD and ΦbHD represent ΦbL and ΦbH, respectively, under drought stress.The effect of drought stress on r2) and relative root mean-squared error (rRMSE) were adopted to analyze the conformity and accuracy between the predicted and the measured values and were calculated as follows:x, y, OBSi, SIMi, M, and n are the measured value, predicted value, average measured variables, average predicted variables, observed value, simulated value, average observed value, and sample size for Equations (8) and (9), respectively.The coefficient of determination  of the parameters and empirical coefficients are listed in The simulation and energy distribution analyses exhibited an opposition between water consumption and drought tolerance. ‘ROC22’ (drought-resistant) decreased its water consumption and activated a photoprotective mechanism to delay the drought-induced physiological injury, while ‘ROC16’ (drought-susceptible) maintained relatively high water consumption to guarantee photochemical reactions but was severely injured with aggravation of drought.


Its cyclic stability was also scrutinized via galvanostatic charging/discharging and electrochemical impedance spectroscopy, indicating excellent long-term stability. In addition, we manufactured an asymmetric supercapacitor device, which offered a high energy density value of 18.1 Wh kg−1 at a power density of 2609.2 W kg−1 (at 1 A g−1 in 2.0 mol L−1 KOH electrolyte). Based on our findings, we believe that higher performances observed for spinel Zn-ferrites nanocorals could be explained by their unique crystal structure and electronic configuration based on crystal field stabilization energy, which provides an electrostatic repulsion between the d electrons and the p orbitals of the surrounding oxygen anions, creating a level of energy that determines their final supercapacitance then evidenced, which is a very interesting property that could be explored for the production of clean energy storage devices.Spinel ferrites are versatile, low-cost, and abundant metal oxides with remarkable electronic and magnetic properties, which find several applications. Among them, they have been considered part of the next generation of electrochemical energy storage materials due to their variable oxidation states, low environmental toxicity, and possible synthesis through simple green chemical processing. However, most traditional procedures lead to the formation of poorly controlled materials . Thus, we report herein a cellulose nanofibers-mediated green procedure to prepare controlled highly porous nanocorals comprised of spinel Zn-ferrites. Then, they presented remarkable applications as electrodes in supercapacitors, which were thoroughly and critically discussed. The spinel Zn-ferrites nanocorals supercapacitor showed a much higher maximum specific capacitance (2031.81 F g Although the depletion of fossil fuel reserves is worrisome due to our natural resources’ energy dependence, the climate change caused by their increased utilization has created a critical necessity for developing efficient, renewable, and clean energy production and storage processes ,2. Howev2O4 —and their composites have been implemented as potential materials for SCs applications due to their low-cost production features [2+ and Fe3+ ions in their spinel lattice [Among them, the spinel ferrites—MFefeatures ,12. For features ,14,15,16features ,19,20,21Interestingly, many reports have described synthesis procedures based on chemical co-precipitation ,23, micr2/g) [Additionally, alternative green synthesis protocols have attracted relevant attention as new regulators for the design of controlled nanomaterials. Among them, biomass-based methods, such as those based on applying cellulose, chitosan, and starch, are advantageous due to their abundance and availability as polymers. Such biopolymers can efficiently act as regulators during nanostructures’ synthesis . In this2/g) , and the2/g) ,40, buil2/g) . Additio2/g) .2+ adsorption from aqueous solutions [Some authors have recently proposed using nanocellulose fibers as a template for synthesizing oxide nanocrystals through prior metals adsorption and subsequent heat treatment under an oxidant atmosphere . In thisolutions ,42.2+ and Fe3+ cations from aqueous solutions, followed by a controlled thermal oxidation under synthetic air atmosphere, to produce the desired metallic oxide nanomaterials, and concomitantly chemically react with nanocellulose, which is converted to gaseous molecules. In all cases, particles with uniform size, composition, and crystal structure have been observed. Additionally, the use of TCNF offered benefits for the easy manufacture of low-cost materials that resulted in enhanced electrode performance, which can be a promising alternative for supercapacitors engineering. Our studies provided a facile method to prepare well-defined nanostructures that do not need expensive reagents or sometimes prohibitive laboratory needs. Moreover, results demonstrated that regarding the spinel-containing sample, a supercapacitance behavior was observed (2031.81 F g−1 at a charge/discharge current density of 1 A g−1 in a 2.0 mol L−1 KOH electrolyte), which was absent in both pure Fe2O3 and ZnO samples produced with the same synthesis protocol. For these samples, much lower capacitance values were measured, which have proven to be equal to 184 F g−1 for Fe2O3 and 24.39 F g−1 for ZnO. We also manufactured an asymmetric supercapacitor device, which offered a high energy density value of 18.1 Wh kg−1 at a power density of 2609.2 W kg−1 (at 1 A g−1 in 2.0 mol L−1 KOH electrolyte).In this study, it is reported a controlled and eco-friendly synthesis of zinc and iron-containing oxide nanocorals, including a Zn-ferrite spinel, mediated by TEMPO-oxidized cellulose nanofibers (TCNF), displaying high surface area and multiple hydroxyl carboxylate groups, for suitable adsorption of Zn2ZnO4), ZnO, and Fe2O3 nanocorals were prepared by an eco-friendly procedure using inexpensive reactants . The syntheses were mediated by the use of TCNF  as templates for suitable adsorption of Zn2+ and Fe3+ cations from an aqueous solution, followed by thermal treatment at 500 °C in the air (muffle furnace) for 2 h. Low-magnification SEM images indicate a porous morphology of all the samples. The high-resolution SEM images show a multiply linked structure formed by uniform and well-defined nanoparticles with sizes in the range of 20–30 nm, as shown in 2O3 nanoparticles composing the nanocorals showed that all samples are polycrystalline. This observation was confirmed by the selected area electron diffraction (SAED) patterns, as shown in the insets of 2O3. The value shown in 2O4. STEM-EDS elemental mapping results  of cubic structure with the most prominent peaks located at Bragg angles equal to 2θ = 18.1°, 30.0°, 35.3°, 42.9°, 53.2°, 56.7°, 62.3°, 73.7°, and 89.3°, respectively, related to the hkl coordinates (111), (022), (113), (004), (224), (115), (044), (335), and (553) (JCPDS No. 96–901–2442), respectively. The XRD results also indicated the presence of lower intense peaks related to a residual amount of a rhombohedral hematite structure, which is located at Bragg angles equal to 33.2°, 48.8°, 54.1°, and 64.0°, respectively, corresponding to lattice indexes equal to (111), (222), (333), and (444) (JCPDS), respectively. Regarding the synthesized ZnO nanocorals (o 00-036-1451). Finally, the Fe2O3 nanocorals , displaying five active Raman modes (A1g + Eg + 3F2g) [−1, respectively. The motion of oxygen in tetrahedral AO4 groups occurred at the modes above 600 cm−1; hence, the mode at 654 cm−1 can be assigned to the A1g symmetry, and the other low-frequency modes correspond to both Eg (mode at 285 cm−1) and F2g , which represent the characteristics of the octahedral sites (BO6). The Raman spectrum for ZnO nanocorals shows two intense peaks in the ZnO located at 101 and 439 cm−1, resulting from two degenerate E2 phonon modes of the Zn and O sublattice vibration; specifically, E2 low and E2 high, respectively [−1 is related to a second-order mode, characteristic of ZnO nanostructures. Finally, the Raman spectrum for Fe2O3 nanocorals  . These fectively . The peanocorals A presenton modes . The pea2O3 nanocorals were also researched by FTIR , while the band centered at υ2 = 470 cm−1 could be assigned to the octahedral metal stretching vibration (Mocta−O). On the other hand, The FTIR spectra of ZnO nanocorals  for the Fe2O3, ZnO, and Zn-ferrite nanocorals samples shown in 2O3 and ZnO different structures  curves at different scan rates , which offers a quasilinear relationship, indicating that the electrode reactions are diffusion-controlled with OH− diffusion from the electrolyte to the electrodes’ surface involved [involved .2+ ions are generally larger than the Fe3+ ion , the M2+ ions occupy the octahedral sites. This logic justifies the behavior of most ferrites, being inverse spinels.It is important to note that previous reports showed that spinel crystal structures of ferrites could be classified as (i) “normal spinel” and (ii) “inverse spinel”, which occur more commonly. More specifically, Ni-, Co-, Mg-, and Cu-ferrites are listed as inverse spinels. In this case, Ni, Co, Mg, and Cu elements do not entirely take over the octahedral sites but are distributed between tetrahedral and octahedral sites. Generally, two hypotheses can be proposed to justify the ferrites’ preference to adopt inverse or normal spinel: the ion-size effect and the crystal field stabilization energy (CFSE). In the first rationale, the smaller cations occupy the smaller interstitial sites , and the larger ions occupy the larger sites . Since the MOn the other hand, the normal spinel ferrites (Mn- and Zn-ferrites) may be justified by electronic configuration based on CFSE. The CFSE originates from the spatial arrangement of cation’s d orbitals and their environment. An electrostatic repulsion between the d electrons and the p orbitals of the surrounding oxygen anions creates a level of energy that determines the system’s stability. We believe this unique crystal structure and the expected electrostatic repulsion between the d electrons and the p orbitals at the spinel crystal structure could strongly affect their activities, leading to superior SCs performances relative to their monometallic counterparts. It is important to note that during Rietveld’s analysis of the Zn-ferrite sample, the structure considered for the spinel phase was of normal type and stoichiometric, corroborating the CFSE arguments cited before.−1. All the materials present only one plateau, consistent with the CV curves. The Zn-ferrite nanocorals showed a much higher maximum specific capacitance (2031.81 F g−1 at a current density of 1 A g−1) than Fe3O4 and ZnO counterparts . The following specific capacities were obtained in the first discharging curve for the Zn-ferrite nanocorals; a decrease with the increase in current density was observed, as shown in −1 (2 A g−1), 1627.3 F g−1 (4 A g−1), 1540.9 F g−1 (6 A g−1), 1472.72 F g−1 (8 A g−1), and 1431.81 F g−1 (10 A g−1). The Coulombic efficiencies achieved were 95.4% (1 A g−1), 90.45% (2 A g−1), 90.40% (4 A g−1), 90.41% (6 A g−1), 95.3% (8 A g−1), and 96.92% (10 A g−1). Clearly, the Zn-ferrite nanocorals presented the best performance, prompting us to study it further.In this sense, to evaluate whether the described characteristic will affect the performance of the materials, GCD curves for the electrodes are displayed in −1. According to −1 at the final cycle. Such phenomena could be explained due to irreversible redox reactions that may take place, damage, or loss of active sites. In addition, mechanical stress from electrolyte ions intercalation/deintercalation to counterbalance the overall charge can happen, affecting the efficiency of the material [−1 for the Zn-ferrite nanocorals are presented as an insert in Consequently, the Zn-ferrite nanocorals’ cycling stability was obtained by performing 1000 charge–discharge cycles with a current density of 10 A gmaterial . Initial−1 KOH electrolyte before, during (after 500 cycles), and after the 1000 cycles , 13.7 F g−1 (2 A g−1), 11 F g−1 (4 A g−1), and 5.5 F g−1 (8 A g−1), with Coulombic efficiencies higher than 90%, which can be better followed by −1 at a power density of 2609.2 W kg−1 (at 1 A g−1 in 2.0 mol L−1 KOH electrolyte). We also performed 500 cycles, in which the ASC system showed a capacitance gain after 220 cycles, with stabilization up to the end of the cycles . In this configuration, the prepared material was used as the anode, while the cathode was activated carbon. As a starting experiment, the operating voltage window of the ASC was assessed by CV A, in whie cycles D.N-oxy) 98%, ZnSO4·7H2O (≥99%), Fe2(SO4)3·7H2O (97%), and α-cellulose (99%) were purchased from Sigma-Aldrich and used without further purification. All solutions were prepared with deionized water . The morphology of the samples has been studied by Scanning electron microscopy (SEM) with JEOL JSM 7100F microscope , operated at 15 kV; and by high-resolution transmission microscopy (HRTEM) with JEOL JEM 2100F microscope , operated at 200 kV. Elemental mappings have been completed with energy-dispersive X-ray spectroscopy (EDS) for all the samples in STEM mode. The samples for microscopy studies were prepared by drop-casting isopropanol suspensions of the materials over silicon wafers  or carbon-coated copper grids , followed by drying under ambient conditions. Textural features were defined using nitrogen adsorption isotherms, recorded at −196 °C in a Micromeritics  Gemini III 2375 surface area analyzer. Typically, 100 mg of each sample was degassed for 3 h at 150 °C prior to each analysis. We used the BET method to obtain the specific surface areas of the samples from the adsorption isotherms created in a relative pressure range of 0.07 < P/Po < 0.3. The X-ray diffraction of the samples was obtained with an X’Pert PRO diffractometer , with an angle range between 10° and 95°, with a step of 0.02° during 6 s acquisition time. Fourier-Transform Infrared (FT-IR) spectra were attained using a Vertex 70 spectrometer V , with a resolution of 5 cm−1 in the region of 500–4000 cm−1. Raman measurements were carried out by a Horiba/Jobin-Yvon T64000 spectrometer  equipped with a liquid N2-cooled CCD detector at a resolution of 2 cm−1 and excitation from a 532 nm wavelength solid-state laser (LAS-532-100-HREV) operating at 14 mW on the sample surface.Analytical-grade NaBr ≥ 99%, NaClO 12 wt%, TEMPO , and 6.1 mL of 12 wt.% aqueous solution of NaClO were added to 100 mL of deionized water and were kept under stirring for 150 min. During the reaction, drops of 0.5 M NaOH were gradually added to maintain a pH between 10 and 11 [In a typical procedure for the synthesis of TCNF, 1 g of α-cellulose, 0.1 g of NaBr, 0.016 g of TEMPO 3·7H2O in a concentration of 90 g L−1 was employed. Finally, for the Fe2ZnO4 sample, an aqueous mixture containing both precursors in the same initial concentrations was used. After 60 min of contact time between 1 g of TCNF and each aqueous solution, the solid phase was vacuum filtered, and finally thermally treated under the presence of atmospheric air in a muffle furnace at 500 °C for 120 min, according to For synthesizing the nanostructured oxides, the previously obtained TCNF suspension was mixed under constant stirring with the desired metal-containing aqueous solution. In the case of the ZnO sample, an aqueous solution containing ZnSO−1 HCl for 60 min, and subsequently washing it with isopropanol/water. Then, a suspension  was made by mixing the sample, Super P, and polyvinylidene fluoride (PVDF) in N-methyl-2-pyrrolidone (NMP) at a weight ratio of 8:1:1. Following, 20 µL of each ink was transferred to the Nickel foam (previously cleaned) and dried overnight. After determining the mass of the active material of the foam (we weight the dried foam before and after the ink addition), Nickel wire was pressed on the edge of it, and the electrode was immersed in a 2.0 mol L−1 potassium hydroxide solution for 12 h previously to its utilization.The electrode preparation was started by cleaning the Nickel foam by immersing it in 3 mol L−1) of the studies was calculated according to Equation (1) [The specific capacitance  :(1)Cs=I I (A) is related to the discharging current, ∆t (s) is the discharging time, m (g) represents the mass of material used for the working electrode obtaining, and ∆V (V) is the potential window.The Coulombic efficiency (η) was calculated using Equation (2) :(2)η=tdtd and tc are the discharging and charging times, respectively.In the equation, t−1. The achieved Specific Capacitance from GCD analysis in a 3-electrode system was used to balance the charge/mass ratio between the cathode/anode. This charge/mass balance between the two electrodes follows the q+ = q− relationship to obtain the optimal performance system, where:The Asymmetric Supercapacitor Cell (ASC) was assembled using Nickel foam loading Zn-ferrite nanocorals as the cathode and AC  loading on Nickel foam as the anode; the electrolyte was KOH 2 mol Ls is specific capacitance, ∆V is the potential window of charge–discharge behavior, and m is the mass of the active electrode material. The active material weight loaded on the cathode for the ASC to obtain Q+ = Q− was obtained using Equation (4) [+ and m− refer to the weights of the materials loaded on cathode and anode electrodes, respectively. Equations (5) and (6) were used to obtain the energy density (ED) and the power density (PD) [s is the specific capacitance (F g−1) obtained in the GCD tests for the hybrid supercapacitor, ∆V (V) is the potential range between the cathode and anode, and ∆t (s) is the time of discharge of the device. All the tests were carried out at room temperature.The charge stored (q) by each electrode depended on the following Equation (3) :(3)q=m·Ction (4) :(4)m+m−=ity (PD) :(5)ED=12−1 potassium hydroxide media as an electrolyte, in which the working electrode (comprised of the prepared samples) was introduced into a single-compartment glass cell with Pt gauze as the auxiliary electrode and Ag/AgCl (KCl saturated) as the reference electrode. The Cyclic Voltammetry (CV) essays were recorded with the N2-saturated electrolyte, with different scan rates (5–80 mV s−1), in a potential range of −0.2 V to 0.5 V. The Galvanostatic Charge–discharge (GCD) tests were conducted under different current densities . Electrochemical impedance spectroscopy (EIS) measurements were met by applying an AC voltage of 5 mV amplitude with a frequency range from 0.01 Hz to 100 kHz at open circuit potential.The electrochemical measurements were performed using an Autolab PGSTAT 302 N potentiostat  with NOVA 2.0 processing software. The working electrode comprised Nickel foam  modified with the samples, in which a Nickel foil  was pressed for conduction issues. The electrochemical measurements were performed in a three-electrode system using 2.0 mol L2+ and Fe3+ cations. Such normal spinel structure could strongly affect the nanocorals’ performance, leading to superior supercapacitor performances relative to their monometallic counterparts, which was confirmed. The nitrogen adsorption–desorption isotherms of the material showed a high specific surface area and the obtaining of porous nanosystems, which is also highly desired for storage applications. Thus, a high specific capacity of 2031.81 F g−1 at a current density of 1 A g−1 was obtained, with good rate performance and excellent electrochemical stability (95.4%). We performed 1000 cycles, and even considering a decrease in the specific capacity, the material, after the hundredth cycle, kept stabilization up to the thousandth cycle.A simple spinel Zn-ferrite nanocorals cellulose-mediate preparation was demonstrated. TEM, SEM, and HRTEM analyses of the Zn-ferrite nanocorals revealed the formation of multiple linked nanoparticles that grew as part of a spontaneous particle attachment process. Additionally, XRD and Raman studies showed that we could achieve a normal spinel structure, which probably was achieved due to the use of TCNF as physical templates for suitable adsorption of Zn2+ ions for other ions of different metals, aiming to evaluate their effect on the storage process.We believe we have stepped forward to understand the utilization of biomass-derived spinel material for storage applications. As a personal opinion, although lithium cells have been widely used for power backup purposes, supercapacitors do not wear out or leak and can be recharged quickly. Even considering their lower storage ability compared to batteries, future studies are promising and aim to overcome such drawbacks. Bearing in mind our remarkable results, we intend to perform new experiments varying the metal precursors’ concentrations to achieve improved performances. Additionally, we intend to change Zn


These results suggest that ‘Shuofeng’ and ‘Chester’ are the top two blackberry cultivars planted in Nanjing, with the best growth and comprehensive quality. Moreover, a total of 119 alleles were detected with an average number of 6 alleles per locus. The polymorphism information content (PIC) was 0.374~0.844, with an average of 0.739, indicating a high genetic diversity among the 23 blackberry cultivars and strains. This study provides insight into the plant growth, fruit characteristics and genetic diversity of the 23 blackberry cultivars and strains, and is thus conducive to the protection and utilization of blackberry cultivars and strains.To screen and evaluate excellent blackberry cultivars and strains, 17 indexes of plant growth and fruit horticultural and nutritional characteristics were measured, 20 simple sequence repeat (SSR) markers were analyzed, the fingerprints of 23 blackberry cultivars and strains were constructed, and the processing characteristics of 10 excellent cultivars and strains were evaluated. The results showed that ‘Chester’ and ‘Shuofeng’ had the highest plant yield (6.5 kg per plant), of which the ‘Chester’ fruit also had the highest hardness (2.78 kg/cm Rubus spp.) is native to North America and Europe and was introduced to China by the Institute of Botany, Jiangsu Province, and the Chinese Academy of Sciences  in 1986. After more than 30 years of cultivation and promotion, it has become a characteristic agricultural industry in China, which has made great contributions to the development of low mountain and hilly areas and increased farmers’ incomes [Blackberry  and third generation DNA-based molecular marker systems represented by single nucleotide polymorphism (SNP) have been used in the identification of bus spp. ,23, genebus spp. , and frubus spp. ,26. Due bus spp. ,28. Morei et al.  used ISSIn this study, 23 blackberry cultivars and strains were selected as the research materials. The 23 blackberry cultivars and strains included 14 introduced cultivars, 7 newly bred cultivars, and 2 hybrid strains. Both the newly bred cultivars and strains had no thorns, while 7 of the 14 introduced cultivars were thorny . To provThrough comparative analysis, it was found that the number of basal branches of the 23 blackberry cultivars and strains ranged from 1 to 4, with an average of 2.4 . The culThe investigation of the ripening characteristics of the different blackberry cultivars and strains showed that 11 of the 23 cultivars and strains were early-maturing cultivars (including 6 thornless cultivars), 6 were medium-maturing cultivars (including 5 thornless cultivars), and 6 were late-maturing cultivars and strains (including 2 thornless cultivars and 2 thornless hybrid strains). The maturity duration also varied significantly among cultivars and strains, with the shortest being approximately 20 days such as ‘Black Butte’, ‘Young’, and ‘Zaohei’, and the longest being 45 days such as ‘Kiowa’. The maturity duration of ‘Chester’ and ‘Wanfeng’ was 40 and 35 days, respectively, next to ‘Kiowa’, while that of the remaining 12 cultivars and strains were 30 days. The intervarietal variation coefficient of maturity duration was 20.2%.Plant yield is influenced by growth statistics and maturity duration. Among the 23 cultivars and strains, ‘Black Butte’ had the smallest plant yield (1.3 kg), ‘Chester’ and ‘Shuofeng’ had the highest plant yield of approximately 6.5 kg, and ‘Kiowa’ and ‘Shuofeng 2’ had a high plant yield of more than 6.0 kg. The coefficient of variation of plant yield was the highest, up to 34.4%, among all the growth and maturity indexes .Blackberry fruits are aggregated fruits composed of several small fruits with longitudinal diameters close to or slightly longer than the transverse diameter. The transverse and longitudinal diameters of the fruits of the 23 cultivars and strains were in the range of 15.83–24.83 cm and 19.72–31.62 cm, respectively . The str2 , ‘Shuofeng 2’ (187.7 mg/100 g FW), and ‘Shuofeng’ (171.4 mg/100 g FW), while ‘Young’ had the lowest total anthocyanin content with only 53.95 mg/100 g FW. The three hybrid strains and cultivars with the highest total polyphenol content were ‘10-5n-2’ (3.24 mg/g FW), ‘Shuofeng’ (3.11 mg/g FW), and ‘Chester’ (2.90 mg/g FW). However, ‘Ningzhi 2’ had the lowest total polyphenol content of only 1.82 mg/g FW. Overall, among the five nutritional indexes, the solid-to-acid ratio had the highest coefficient of variation (43.96%).Through correlation analysis of the 17 indexes of blackberry, it was found that among the 8 growth and maturity indexes, the maturity duration was positively correlated with the basal branch diameter and effective branch number a. The yiThe 23 blackberry cultivars and strains were divided into 3 groups by hierarchical cluster analysis b. Group Principal component analysis was performed on 17 indicators representing the plant growth and maturity characteristics and fruit appearance and nutritional characteristics of the 23 blackberry cultivars and strains, which were reduced to five principal components, representing a total contribution rate of 79.8% . Among tAmong the 20 pairs of polymorphic primers, most of the primers amplified 4–8 regions, with relatively rich polymorphism . A totalCluster analysis of the 23 blackberry cultivars and strains using UPGMA b showed On the basis of phenotypic characteristics and genetic diversity analysis, 10 representative blackberry cultivars and strains were selected to investigate fruit processing characteristics. Juice of the different cultivars and strains was prepared by the enzymatic hydrolysis centrifugation method. The differences in juice yield, juice pH, and dry matter content of the 10 blackberry cultivars and strains were compared. Due to the characteristics of the small fruit aggregation of blackberry fruits, their juice mainly exists in the small fruits, so the number of small fruits and the proportion of small fruit weight also affect the processing characteristics; thus, these two indexes were also investigated . As showAmong the 10 representative blackberry cultivars and strains, ‘Kiowa’ had the highest number of small fruits (up to 106.8), reaching twice that with the lowest number of small fruits, ‘Chester’ and ’10-5n-2’. ’10-5n-2’ had the highest proportion of small fruits which reached 94.7%, and ‘Wanfeng’ and ‘Chester’ also had high proportions of small fruits, approximately 93~94%, while ‘Zaohei’ had the lowest proportion of small fruits, at only 82.6%. The dry matter contents of the 10 blackberry cultivars and strains ranged from 11.0% to 17.2% , with th‘Hull’ and ‘Chester’ were the first two thornless cultivars of the 23 blackberry cultivars and strains to be planted in the Nanjing area, Jiangsu, China. ‘Kiowa’ had the largest fruit and good growth adaptability, but it was not widely planted because of its thorns. In recent years, the research group has designed multiple hybrid combinations using ‘Kiowa’ as the male or female parent, with the breeding goal focused on having no thorns, high yield, and large fruits. Ultimately, three new cultivars, ‘Shuofeng’ (♀ Kiowa × ♂ Hull), ‘Shuofeng 2’ (♀ Hull × ♂ Kiowa), and ‘Wanfeng’ (♀ Hull × ♂ Kiowa), which have good growth adaptability and high-quality fruits, were obtained. At present, ‘Shuofeng’ is being promoted.When examining the growth characteristics of blackberry cultivars and strains, it is difficult to ignore the thorns of the plant, as all field management, fruit harvesting, and sample collection work can be inconvenient due to the presence of thorns . Among tGenerally, fruit size is an appearance characteristic that will be of primary concern in the fresh fruit consumer market. However, for soft and juicy blackberry fruits, hardness is also an important index because blackberries are aggregate berries that lack a complete protective cuticle . High haMany consumers, including Chinese consumers, have the impression that blackberry fruits taste sour . HoweverThe main antioxidant components of blackberries are anthocyanins and polyphenols ,44,45, wAmong the 20 pairs of SSR primers selected for the identification of blackberry cultivars and strains in this study, 19 pairs had PIC values exceeding 0.62, indicating good polymorphism and the ability to identify the genetic characteristics of blackberry cultivars and strains accurately and clearly. Three pairs of primers with good polymorphism can effectively distinguish 22 blackberry cultivars and strains, laying a foundation for subsequent resource protection and utilization. From the genetic clustering map of SSR analysis, it can be seen that there is a similar relationship between the hybrid strains and their parents, such as ‘Ningzhi 1’ and ‘Boysen’, ‘Ningzhi 2’ and ‘Triple Crown’, ‘Ningzhi 3’ and ‘Arapaho’, and ‘Zaohei’ and ‘Kiowa’, which is consistent with previous research results ,15. HoweRubus spp.) cultivars and strains were planted in the Blackberry Germplasm Repository  of Nanjing Lishui Baima, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences. The annual average temperature of the planting area in 2020 was 17.1 °C, and the annual rainfall was 1294.1 mm. The soil of the resource garden is acidic clay with a pH of approximately 6.0, containing 18.67 g/kg organic matter, 1.25 g/kg total nitrogen, 4.83 mg/kg available phosphorus, and 94.21 mg/kg available potassium. The plants were irrigated under a drip irrigation system. The plant spacing was 1.5 m in rows and 2.5 m between rows, and bird nets were used from June to August during the fruit maturity period.Twenty-three blackberry , 4 newly bred cultivars (‘Ningzhi 1’ (bud mutation from ‘Boysen’), ‘Ningzhi 2’ (♀ ‘Triple Crown’ × ♂ ‘Navaho’), ‘Ningzhi 3’ (♀ ‘Arapaho’ × ♂ ‘Hull’), and ‘Shuofeng’ (♀ ‘Kiowa’ × ♂ ‘Hull’)), and 5 hybrid strains (‘Zaohei’ (♀ ‘Kiowa’ × ♂ ‘Arapaho’), ‘Wanfeng’(♀ ‘Hull’ × ♂ ‘Kiowa’), ‘Shuofeng 2’ (♀ ‘Hull’ × ♂ ‘Kiowa’), ‘10-5n-2’ (♀ ‘Chester’ × ♂ ‘Kiowa’), and ‘7-7-4’ (♀ ‘Triple Crown’ × ♂ ‘Chester’)).Reagents included Folin phenol purchased from Sigma–Aldrich and Klerzyme 150 pectinase purchased from DSM (China) Co., Ltd. . The instruments included a PAL-1 Brix meter , KM-1 hardness tester , Philips HR2166 food processing machine (Philips (China) investment Co., Ltd., Shanghai, China), and ZD-2 automatic potentiometric titrator .The 4th and 5th year after planting is the most stable period of growth performance, which can reflect the adaptability of the cultivars and strains. Therefore, 4- and 5-year-old plants were selected for the investigation of the 23 cultivars and strains. Referring to the method of Lyu , after pIn the full fruit period, 20 fully mature fruits were randomly picked from 4- or 5-year-old plants, and the single fruit weight, transverse diameter, longitudinal diameter, and fruit hardness were measured. The fruits were weighed with an electronic scale with a sensitivity of 0.01 g. The longitudinal and transverse diameters of the fruit were measured using a digital Vernier caliper with a sensitivity of 0.01 mm . The fruIn the full fruit period, approximately 600 g of the fully ripe fruits were hand-harvested once from 4- or 5-year-old plants, frozen into single frozen fruits at −20 °C, and placed in a sealed bag at −20 °C until use. The soluble solids content of 20 single fruits was determined using a Brix meter. The total acid content was determined by acid–base titration, according to GB 12456-2021 Determination of total acid in fruits, and was calculated using citric acid. The ratio of solids to acid was the ratio of soluble solids content to total acid content. The total anthocyanin content was determined by the pH differential method . The tot−1, which was stored at 20 °C for future use.The apical young leaves of annual nutrition branches of the 23 blackberry cultivars and strains were collected, wrapped with tin foil, quick-frozen in liquid nitrogen, and stored in a −80 °C refrigerator. The blackberry DNA was extracted using the Plant Genome DNA Extraction Kit (Tiangen Company), and the specific operation steps are shown in the instructions. Then, the quality and concentration of DNA were detected by 1% agarose gel electrophoresis and a NanoDrop-1000 spectrophotometer , and the final concentration was adjusted to 50 ng/μLRubus SSR [According to published articles related to ubus SSR ,54 and tubus SSR , 20 pairubus SSR ,56.g r for 5 min, the supernatant juice was weighed, and the juice yield (%) was calculated. The pH value of fruit juice was determined by titrator. Ten fully mature fruits were taken, and tweezers were used to peel the small fruits off one by one from the fruit axis. The number of small fruits was recorded, the weight of the small fruits and the whole fruit was recorded, and the proportion of small fruits was calculated. Five to seven grams of fruit pulp was placed in a water box and dried at 105 °C for more than 8 h until the sample reached a constant weight. The dry matter content (%) of the fruit was calculated. Each sample was repeated 3 times.Five hundred grams of the fully ripe fruits were hand-harvested once from 4- or 5-year-old plants in the full fruit period, frozen into single frozen fruits at −18 °C, and kept at −18 °C until use. The fruit juice yield determination was slightly improved with reference to the method of Wang . ApproxiOrigin 2022  software was used to perform hierarchical cluster analysis and principal component analysis. The data before clustering and principal component analysis were standardized by Z scores. The Ward square Euclidean distance clustering method was used for hierarchical cluster analysis, and the correlation coefficient matrix method was used for principal component analysis. In addition, NTSYS-pc Ver.2.10e  software was used to perform UPGMA-based clustering analysis on the SSR marker information of the 23 blackberry cultivars and strains, and PIC Calc 0.6 software was used to calculate the polymorphism information content (PIC) values of each primer.The plant growth and maturity statistics, fruit appearance, nutrition, processing characteristics, and genetic diversity of 23 blackberry cultivars and strains were investigated in the blackberry germplasm repository in Nanjing. The results showed that the variations in plant yield and solid–acid ratio were the largest among the 7 growth indexes and 10 fruit appearance and nutrition indexes. According to phenotypic characteristics, 21 blackberry cultivars and 2 strains can be divided into 3 phenotypes: excellent adaptability, medium adaptability, and poor adaptability. Principal component analysis reduced 17 indexes to 5 principal components: growth, acidity, fruit appearance, sweetness and hardness. In addition, the fingerprints of the 23 blackberry cultivars and strains were constructed using 3 SSR primers, laying a foundation for subsequent resource protection and utilization.


Major bacterial groups show strong metabolic specialization in organic topsoils. α-/β-/γ-Proteobacteria specialized in the acquisition of small, soluble compounds, whereas Acidobacteria, Actinobacteria, and other detritosphere groups specialized in the degradation of plant-derived polymers. α-/β-/γ-Proteobacteria dominated the expression of transporters for common root exudates and limiting nitrogenous compounds, supporting an ecological model of dependence upon plants for carbon and competition with plants for nitrogen. Detritosphere groups specialized in distinct substrates, with Acidobacteria producing the most enzymes for hemicellulose depolymerization. Acidobacteria was the most active group across the three plant ecotypes sampled—the largely nonvascular, lower biomass intertussock and the largely vascular, higher biomass tussock and shrub. Functional partitioning among bacterial groups was stable between plant ecotypes, but certain functions associated with α-/β-/γ-Proteobacteria were more strongly expressed in higher biomass ecotypes. We show that refined metaproteomic approaches can elucidate soil microbial ecology as well as biogeochemical trajectories of major carbon stocks.Microbial activity in Arctic soils controls the cycling of significant stores of organic carbon and nutrients. We studied The Arctic is warming twice as fast as the rest of the planet, and Arctic soils currently store twice as much carbon as the entire atmosphere—two facts that make understanding how Arctic soil microbial communities are responding to climate change particularly urgent. Greening of vegetation cover across the Arctic landscape is one of the most prominent climate-driven shifts in Arctic terrestrial ecology, with potentially profound effects on biogeochemical cycling by the soil microbiome. Here we use metaproteomics to document microbial metabolic functions that drive soil carbon and nutrient cycling processes in an Arctic tundra landscape. We identify functional roles among bacterial taxonomic groups that are largely stable across vegetation types, with certain functions strongly expressed by rhizosphere groups reflecting a community metabolic response to greening. Arctic soils contain large quantities of organic carbon susceptible to respiration in a warming climate. Though Arctic soils comprise only 16% of soil surface area , 3. Low Greening of vegetation cover across the Arctic landscape—notably the shift from tussock tundra to taller, higher-biomass woody shrubs—is one of the most prominent climate-driven shifts in Arctic terrestrial ecology, and its consequences for the soil microbial community remain unclear -24. Loin situ measurement of most reaction intermediates linking plant inputs to efflux gases, such as CO2, via microbial metabolisms  are abundant around near-surface runoff channels, with nonvascular and herbaceous plants growing in the understory. Shrubs have a higher density of fine roots distributed at shallower depths in the organic layer than tussocks (Cores of permafrost-affected soil were collected at the end of the lower Arctic growing season (10–12 August 2014) in the vicinity of Toolik Field Station, Alaska, USA (TFS) . Cores wve layer , 56. Tusve layer . Woody stussocks , 58. Samtussocks , 60. Avetussocks . Cores t10.6084/m9.figshare.c.6485806). Protein extraction was based on the method of Chourey et al.  are used to measure the relative expression levels of the function by the suite of taxa. These scores and the overall relative abundances of proteins, as determined from spectral counts, factor into the metric, Φ, introduced below, which is used to compare the protein expression profiles of taxonomic groups and how a single taxonomic group’s expression profile differs between samples. ProteinExpress uses both new algorithms and existing bioinformatic tools, which are implemented by the software in a pipeline . Proteink-mer length 27, maximum length 87, and step size 10)  . Using tsize 10) , anotherPseudomonas and Rhizobiales  . Ea. Ea77). 6485806) . PSMs me6485806) . The one10.6084/m9.figshare.c.6485806)  to find homologous proteins , 80. Of 6485806) , 82. At  to find doi.org/10.6084/m9.figshare.c.6485806). For example, “CITA” + “Citrate synthase” was assigned to the “TCA Cycle” Functional Group. Some Functional Groups, such as “TCA Cycle,” are equivalent to biochemical pathways. Others, such as “Ribosome,” “Transposase,” and “Ribose Transport,” are comprised of functions not necessarily related through a common pathway. When needed, eggNOG-mapper gene family and functional descriptions were cross-referenced to databases  to assign Functional Groups. For example, sequences with a description of “Hydrolase, family 38” lacking an eggNOG gene family assignment were assigned to “Mannose Cleavage,” as CAZy records that enzymes assigned to Family 38 on the basis of sequence homology are only known to be active on α-linked mannose residues  :N is the peptide index; ND is the number of spectra matched to peptide N; LN is the average length of protein subject sequences returned from BLASTp searches of PSM-protein candidates containing peptide N (see above); and n is the total number of peptides identified in the dataset. Spectrum count is normalized to protein length, as longer proteins generate more tryptic peptides and are thereby more likely to be observed. The average standard deviation of relative subject sequence lengths from sets of PSM-protein candidates was 2.0%. Metaprotein and Functional Group NSAF values were calculated by summing the NSAF values of peptides associated with the given metaprotein or Functional Group.where S’. S’ was normalized to the range, , by subtracting the minimum S’ among the bins (S’min) and dividing by S’max–S’min. Normalization allows comparison of relative levels of production of a peptide among the taxonomic groups in the sample—which must be well-represented by the bins—with minimum and maximum production by groups corresponding to values of 0 and 1, respectively. The normalized value of S’ was multiplied by the NSAF value of the peptide in the dataset, producing φN, a metric representing both peptide abundance and taxonomic similarity.We developed a metric, Φ, to estimate relative metaprotein expression levels within and between taxa. Φ takes into account both the relatedness of a metaprotein to a particular bin of contigs (serving as a measure of the likeliness of expression by organisms in the bin) and its NSAF, which represents the level of metaprotein expression irrespective of the bin. To determine Φ, each peptide’s PSM-protein candidates were aligned to each bin by BLASTp. The single candidate that produced the alignment with the lowest E-value was selected, only considering alignments with E-values <0.01. Selection of this candidate ensures the strongest possible nonrandom association between a peptide and each taxonomic bin. The strength of each association was measured by the alignment’s bit score, N was extended to metaproteins and Functional Groups (Table S4 at doi.org/10.6084/m9.figshare.c.6485806) by summing φN values of PSMs categorized as a certain metaprotein or Functional Group, P.φbin, which scales φP to the maximum value across bins , to compare patterns of functional partitioning between taxonomic groups. We used ΦP, which scales φP to the maximum value across metaproteins or Functional Groups , to compare a taxonomic group’s protein expression profile between samples. In either case, if the maximum value of φ is 0, Φ is assigned a value of 0. Φbin and ΦP lie in the range , with at least one value across the set of bins or functions, respectively, equal to 1.We used Φbin and ΦP values. For analysis of between-ecotype variance of a given taxon’s functional profile, each data point in the multivariate analysis is a taxonomic group with a vector of Φbin values over all Functional Groups. For analysis of between-ecotype variance of a given function’s distribution between taxa, each data point is a Functional Group with a vector of ΦP values over all taxonomic groups.To understand differences in the functional profiles of taxonomic groups between plant ecotypes, linear discriminant analysis and principal component analysis were conducted with ΦWe identified peptide sequences for 3,142–16,235 MS2 spectra (PSMs) from each of the 15 organic and 3 mineral soil samples, of which 1,079–5,003 (27.1–36.9%) were functionally annotated . Metaprobin  Φbin values averaged across all organic soil samples, irrespective of plant ecotype. A breakpoint in the reduction of the sum of squared errors with the addition of clusters exists at k = 3 clusters (Table S5 at doi.org/10.6084/m9.figshare.c.6485806), which showed consistent expression patterns between taxonomic bins. Protein expression of Functional Groups in Cluster 1 was largely attributed to α-/β-/γ-Proteobacteria; proteins in Cluster 2 were expressed most strongly by Acidobacteria, but also at high levels by α-/β-/γ-Proteobacteria; and functions in Cluster 3 were dominated by Acidobacteria. Other taxa, including Actinobacteria, Bacteroidetes, and Myxococcales (δ-Proteobacteria), produced fewer proteins for most functions.Our calculated metric, Φbin see , was useMany core cellular functions, including DNA replication and repair, transcription, translation, and cell division, fall into Cluster 2—being most highly expressed by Acidobacteria and secondarily by α-/β-/γ-Proteobacteria —suggestibin,Ribosome values, as a proxy for overall cellular activity; We also examined the expression of Functional Groups when normalized to that of ribosomal proteins  and formaldehyde oxidation pathways. The nondetection of methane monooxygenase, low representation of Archaea in nucleotide datasets, and identification of only three metaproteins with strong alignments to Archaea in RefSeq indicate that biogenic methane was not a significant substrate. Rhizobiales produced the highest levels of monosaccharide transporters but low levels of enzymes for polysaccharide depolymerization, glycolysis, and the catabolism of nonglucose monosaccharides. Rhizobiales dominated the expression of enzymes for polyhydroxyalkanoate synthesis, a C storage mechanism with higher overall abundance than the other identified C storage pathways of trehalose and glycogen synthesis.2 fixation. Intracellular N cycling functions, such as amino acid synthesis and catabolism pathways, were much more evenly expressed between taxonomic groups. The abundant ammonia metabolism Functional Group, which largely consists of the glutamine synthetase metaprotein, was more strongly expressed by Acidobacteria.Proteobacteria—particularly Rhizobiales—dominated the production of N transporters . ProteinPhosphate acquisition proteins, including phosphate transporters and phosphatases, were higher in overall abundance than polyphosphate and phosphonate acquisition proteins, and were relatively evenly expressed across groups. Iron is known to be an important substrate in microbial iron mats that float on submerged soils around Toolik . In our bin and ΦFunctional Group values reveal that organic soil samples cluster by plant ecotype when considering the functional profiles of taxonomic groups , xylose+arabinose transport (P = 0.019), sugar alcohol transport (P = 0.014), and succinoglycan synthesis (P = 0.014) (10.6084/m9.figshare.c.6485806). These functions were more highly represented in tussock than intertussock samples and were most strongly expressed by Rhizobiales. Linear discriminant analysis of the NSAF data  shows that organic soil samples distinctly cluster by plant ecotype, whereas the three mineral soil samples, one from each plant ecotype, form a separate cluster  in overall relative abundance between environments (shrub is ignored due to the fewer number of metaproteomic datasets than tussock and intertussock): ribose transport ( cluster .Sphagnum peat (We profiled microbial protein expression in Arctic Alaskan soils using a metaproteomic approach that accounts for sequence microdiversity, finding that major heterotrophic functions are consistently partitioned among broad taxonomic groups . While tnum peat , AcidobaIn these Toolik-area soils, Actinobacteria dominated cellulase production, in contrast to the Swedish soils in which Acidobacteria predominated, while Actinobacteria had high genomic potential for cellulases but negligible production of transcripts and proteins . Our resα-/β-/γ-Proteobacteria specialized in the acquisition of small, soluble compounds, including monosaccharides, despite expressing very low levels of glycosidases. These taxa appear to be adapted primarily to the rhizosphere or other soil hot spots receiving pulses of labile carbon substrates or nutrients . At the 15N dilution experiments (α-/β-/γ-Proteobacteria appeared to compete strongly with plants for scarce N in Toolik-area soils. By the time we sampled at the end of the growing season, bioavailable N—which is mainly in organic forms—has been found to fall to nearly undetectable levels in soils around Toolik . Two meceriments  and metaeriments . In our eriments . High N eriments , 98, 108eriments . We founBacterial protein relative abundances differed between plant ecotypes in organic soils , yet funPatterns of functional partitioning that we found among major bacterial groups seem likely to persist as plant ecotypes continue to shift in response to Arctic warming. These patterns may also apply over large areas of the Arctic, as suggested by the occurrence of nearly identical metagenome-assembled genomes hundreds of kilometers apart . Buildin