Programmed cell death 1 (PD-1) and programmed cell death-ligand 1(PD-L1) inhibitors have captured our attention as new therapeutic options for several tumor types. Nonetheless, the differences in efficacy between PD-1/PD-L1 inhibitors and conventional treatments (chemotherapy or targeted therapy) in pretreated advanced cancer patients remain unclear.A systematic literature search was conducted to identify phase III randomized controlled trials (RCTs)-based investigations of PD-1/PD-L1 inhibitors (atezolizumab) against pretreated advanced cancer. We evaluated these trials for inclusion, assessed each study’s risk of bias and selected relevant data for analysis.P < 0.001) and median month difference . The progression-free survival HRs favored PD-1/PD-L1 inhibitors over conventional treatment , whereas median month difference was just the opposite .The eligibility criteria were met by 5,093 patients from 8 phase III RCTs. PD-1/PD-L1 inhibitors significantly extended overall survival relative to the conventional treatment, expressed as hazard ratio [HR] , were associated with improvement in overall survival (2.83 months) but not progression-free survival. These findings will be important in considering PD-1/PD-L1 inhibitors in the treatment of pretreated advanced cancer and have implications for future study design. Cancer, especially advanced cancer, is still a pressing worldwide health issue . AlthougImmune checkpoint inhibitors, especially programmed cell death 1(PD-1) and programmed cell death-ligand 1(PD-L1) have captured our attention as new therapeutic options for patients with selected advanced cancer for which no effective treatment yet existed . PD-1 reUntil now, several randomized controlled trials (RCTs), which compared the efficacies of PD-1/PD-L1 inhibitors to conventional treatments (chemotherapy or targeted therapy) against various pretreated advanced cancer, have been conducted –23. HoweClinicalTrials.gov databases was performed from their inceptions to February 20, 2017. The search was conducted using the following keywords: “nivolumab,” “pembrolizumab” or “atezolizumab.” We also searched the American Society of Clinical Oncology conference proceedings, the European Society for Medical Oncology conference proceedings and Google Scholar to ensure that no eligible studies were overlooked. After the titles and abstracts were screened by two independent reviewers (H. H and Q. Z), the full texts from potentially relevant studies were retrieved to confirm the eligibility criteria. We also reviewed reference lists of original articles, review articles, and relevant books. When a duplicate publication of the same trial was found, the study with the most complete, recent, and updated report was included. All procedures were performed per the preferred reporting items for systematic reviews and meta-analyses statement , which estimated the percentage of total variation across studies due to heterogeneity rather than chance, with I2 >50% indicating significant heterogeneity. The sources for heterogeneity were explored by conducting predefined subgroup analyses and meta-regression (in cases where the percentage of never smokers was given): treatment type (PD-1 inhibitors vs PD-L1 inhibitors), tumor type (NSCLC vs others), and PD-L1-positive status (unselected vs selected). A two-tailed P value of less than 0.05 was considered statistically significant. Funnel plots were performed to assess the potential publication bias. The Duval and Tweedie trim-and-fill test and the classic fail-safe N test were applied to define the extent of publication bias [A general variance-based method was used to estimate the summary HR and their 95% CIs, which were calculated to assess the benefit with respect to OS and PFS. When median survival times or median months of PFS were available, median differences were generated and combined. Random effects models were used to compute all the outcome measures investigated , 31 and ion bias . When puion bias  and Eggeion bias  were useAs shown in Figure 7 trials used PD-1 inhibitors including 5 trials with nivolumab –20, 2 wiAll trials recruited patients with a performance status of 0 through 2 in the Eastern Cooperative Oncology Group (ECOG), apart from 1 trial , which iAll trials used commonly recommended second- or later-line therapeutic regimen as the control group  and median month difference . Moreover, there was no publication bias for OS expressed as HR and median month difference , whereas PD-1/PD-L1 inhibitors suggested shorter median PFS expressed in months  . However, a significant statistical heterogeneity was noted in the analysis of median month difference in PFS (I2 = 65.3%). No or minimal publication bias for the outcome PFS expressed as HR and median month difference was noted  Figure . No signas noted . The DuvP values for subgroup difference: type of treatment, 0.35; and type of tumor, 0.58, respectively). However, the subgroup result of PD-L1-positive status appeared to be discordant: trials recruiting selected PD-L1-positive patients showed no significant difference in median month difference of PFS between PD-1/PD-L1 inhibitors with control group . On the other hand, in trials recruiting patients with unselected PD-L1 expression, PD-1/PD-L1 inhibitors demonstrated a shorter median PFS  compared with control treatment. PD-L1-positive status can partly explain the heterogeneity between the trials, but the subgroup difference did not yet reach the level of statistical significance (P = 0.11). In a meta-regression analysis regarding smoking status, there was no correlation between the percentages of never smokers and the standardized mean difference for PFS (P = 0.29); Subgroup analyses were carried out to explore the heterogeneity in the terms of median month difference for PFS because the significant heterogeneity existed within included studies Table . There wThe result of risk of bias assessment is provided in Figure We performed the meta-analysis with a focus on investigating the efficacy difference between PD-1/PD-L1 inhibitors and conventional therapies in pretreated advanced cancer patients. This study included all published 8 well-conducted, good-quality, phase 3 RCTs incorporating 5,093 patients with pretreated advanced cancer. Considering PD-1/PD-L1 inhibitors blockade PD-1/PD-L1 pathways by activating an immune response directed against cancer, it exhibit antitumor activity in the form of a cytostatic, rather than a cytotoxic effect . Thus, PPresent pooled analysis suggested that PD-1/PD-L1 inhibitors prolong OS (2.83 months) compared with conventional treatments, however, the corresponding reduction of median PFS with PD-1/PD-L1 inhibitors were 0.69 months. The apparent discordance between PFS and OS have been commonly noted , 22, 23,PD-1 inhibitors block the PD-1 interaction with both PD-L1 and PD-L2. However, PD-L1 inhibitors block interactions between PD-L1 and both PD-1 and CD80 whereas the latter interaction may down-modulate T-cell responses . MoreoveEfforts to find the appropriate biomarker to identify which patients would benefit most from PD-1/PD-L1 inhibitors is a crucial personalized-medicine approach to increase response rates and many different predictive biomarkers are evaluated. Of these, the relationship between PD-L1 expression on tumor cells and outcome to PD-1/PD-L1 inhibitors has been extensively investigated but are still inconclusive, which could partly be due to the function of complex interactions between tumors and the immune system , 42, 49.PD-1/PD-L1 inhibitors have shown promising activity against many tumor types, whereas only a subset of patients can respond to such mono-therapy , 10. TheTwo meta-analyses have tried to explore the efficacy of PD-1/PD-L1 inhibitors. One meta-analysis included only 3 trials of PD-1/PD-L1 inhibitors  and the Inclusion of well-conducted, good quality, phase 3 RCTs are the strengths of our study. The lack of heterogeneity in the assessment of OS represents a well selection of studies. Furthermore, subgroup analyses were all pre-planned and limited to a minimum. Inclusion of large number of patients and recent studies are other strengths. However, our study encountered several limitations relevant to this meta-analysis. First, the analysis was based on extracted data and not on individual patient data. Therefore, the interpretations of the results needed to be carefully performed, especially for the specific relationships in the subgroup analysis. Further studies using the individual patient data will be necessary to assess outcomes, such as the primary outcomes and subgroup interactions. Second, this meta-analysis involved the different treatment schedules between the trials. In two trials, patients received pembrolizumab per the following schedules: 2 mg/kg, 10 mg/kg  or 200 mIn conclusion, our meta-analysis identified that in selected pretreated advanced cancer patients, OS was significantly longer (2.83 months) with PD-1/PD-L1 inhibitors than with conventional treatments, whereas median PFS was just the opposite (−0.69 months). These findings provide important and clinically useful information with respect to PD-1/PD-L1 inhibitors as novel treatment strategies to improve patient overall survival, as well as could be useful for the design of future studies.


Heightened attentional bias to emotional information is one of the main characteristics of disorders related to emotion dysregulation such as anxiety, depression, and substance abuse. Although reappraisal, an emotion regulation strategy, is known to effectively modulate subjective experience of emotions, it remains unknown whether reappraisal can alter attentional biases to emotional information. In the current research, we investigated the influence of instruction-induced state reappraisal (Study 1) and trait reappraisal (Study 2) on attentional biases to happy and angry faces. In Study 1, healthy young women were recruited and randomly assigned to one of the three groups: up-, down-, and no-regulation. Participants were instructed to reappraise their emotions to increase and decrease emotional experience while viewing an emotionally negative film clip. Attentional bias was assessed with a dot-probe task with pictures of angry and happy facial expressions. In Study 2, a separate group of healthy young men and women participated. Participants’ trait reappraisal and suppression as well as state and trait anxiety were assessed. A dot-probe task was completed by all participants. Statistical tests in Study 1 revealed that participants who reappraised to decrease negative emotions while viewing an emotionally negative film clip had reduced attentional bias to subsequently presented angry faces compared to participants who reappraised to increase negative emotions. Multiple regression analyses in Study 2 revealed that trait reappraisal predicted slower orienting toward angry faces, whereas state anxiety predicted slower disengagement from angry faces. Interestingly, trait suppression predicted slower disengagement from happy faces. Taken together, these results suggest that both instruction-induced state reappraisal and trait reappraisal are linked to reduced attentional bias to negative information and contribute to better understanding of how everyday emotion regulation styles contribute to attentional processing of emotional information. Individuals have a biased tendency to attend more to emotionally meaningful versus ordinary information . AlthougIn fact, dysfunctional attentional biases to emotional information have been linked to various emotion dysregulation disorders including anxiety, depression, and substance abuse. For example, patients with anxiety show biased attention to threat-related information, and substance abusers also attentional biases to substance-related information . AttentiGiven the critical association between emotion dysregulation and increased attentional bias , one mayIn addition, the momentary effect of reappraisal may accumulate over time if reappraisal is habitually used for daily emotional events, and this could produce a systematic difference in attentional responses to emotional information. Indeed, individual differences in trait-level reappraisal have been associated with daily positive and negative emotional experiences . Neural In the current research, we investigated the effects of momentary, instruction-induced reappraisal and habitual reappraisal on attentional biases in two studies. Attentional bias was assessed with a dot-probe task , which iIn Study 1, we investigated the effect of instruction-induced reappraisal on attentional bias to subsequently presented faces. Healthy young women were recruited and randomly assigned to one of the three reappraisal groups: up-regulation, down-regulation, and no-regulation. First, emotional states were induced by having participants watch a film clip showing a female victim of physical violence. While watching the film, participants were instructed to use reappraisal to increase or decrease emotions, or did not reappraise their emotions. This was followed by a dot-probe task with angry, happy and neutral faces. For Study 1, only women participants were recruited because naturally occurring initial emotional reactions to a female victim may differ across men and women. Our main hypothesis was that reappraisal to increase negative emotions would lead to a greater attentional bias to angry faces, whereas reappraisal to decrease negative emotions would lead to a reduced attentional bias to angry faces. We also examined attentional biases to happy faces. A previous study reported no changes in attentional bias to positive words after negative mood induction . As partIn Study 2, we further tested whether habitual daily uses of reappraisal and suppression predict attentional biases to angry and happy faces. Healthy young men and women performed a dot-probe task as in Study 1. Individual differences in reappraisal and suppression were assessed with the Korean version of the Emotion Regulation Questionnaire ERQ; . State aFifty-seven healthy young women (mean age = 22.30 ± 2.65 years) were recruited through campus flyers. The sample size was determined based on pilot studies and previous studies demonstrating the effect of reappraisal using between group design . All volFor the dot-probe task, we selected faces with angry , happy , and neutral  expressions from a standardized database of face photographs . ValenceAfter signing a consent form, participants completed the Beck Anxiety Inventory BAI;  and EmotNext, participants received emotion regulation instructions according to group assignment . ParticiNext, participants watched a 5-min film segment selected from the movie “One on One (2014)” directed by Kim Ki-Duk. This film segment depicted a man’s violent behavior against his girlfriend. An independent group of 16 participants (nine men and seven women) rated the film segment as having an emotional valence of 2.1  and emotional arousal of 5.2 . Immediately after watching the film, participants again rated their mood on the VAS (VAS-post). Participants also rated how successful they were in following the reappraisal instructions on a VAS . Finally, participants performed the dot-probe task. There were eight practice trials composed of face pairs that were not used in the main task before the experimental trials. After completing the task, participants were debriefed and compensated for their time.A total of 1.1% of the total data were excluded due to incorrect responses (0.4% of the data) and outlier responses, defined as trials with reaction times less than 200 ms or greater than three standard deviations above the individual mean (0.7% of the data). Attentional bias scores were calculated by subtracting mean reaction times to dot probes that replaced emotional faces from mean reaction times to dot probes that replaced neutral faces. Positive attentional bias scores indicate greater attention to emotional relative to neutral faces, and negative attentional bias scores indicate avoidance of emotional faces. Attentional bias was calculated separately for happy and angry faces. We further assessed two subcomponents of attentional bias: orienting and disengagement. Orienting was calculated by subtracting mean reaction times to probes replacing emotional faces in emotion-neutral pairs from mean reaction times to probes replacing neutral faces in neutral–neutral pairs. Greater positive scores indicate faster orienting to emotional faces. Disengagement was calculated by subtracting mean reaction times to probes replacing neutral faces in neutral-neutral pairs from mean reaction times to probes appearing in the location of the neutral expression in emotion-neutral pairs. Greater positive scores indicate slower disengagement from emotional expressions. Statistical analyses were performed using SPSS 19 .Table 1. There were no groups differences in age , reappraisal , suppression , and BAI  scores (Table 1).Descriptive statistics for each group are presented in F = 8.37, p = 0.001] and time  and the group × time interaction, , were significant. Overall, negative mood increased post-film  compared with pre-film . Overall the up-regulation group reported greater negative mood  compared with the down-regulation group . To further examine the interaction effect, follow-up t-tests were conducted. Results showed no group differences in negative mood before the film (ts < 1). However, a significant group effect was found post-film, F = 26.33, p < 0.001. The down-regulation group reported lower negative mood than the up-  and no-regulation groups . The up-regulation group reported greater negative mood than the no-regulation group, . A one-way ANOVA on self-ratings of regulation success with group as a factor revealed no group differences, F = 1.44, p = 0.246.Negative mood scores were calculated by summing all VAS scores (depressed-happy scores were reverse-coded). A two-way ANOVA with time (pre versus post) as a within-subject factor and group as a between-subject factor was conducted. The main effects of group, . Follow-up t-tests showed that the increase in negative mood scores was greater in the up-regulation group than no-regulation group  and down-regulation groups . The increase in negative mood was significantly smaller for the down-regulation group compared to no-regulation group .To specifically examine whether mood change after watching the film differed across groups, we calculated mood change scores by subtracting pre-film scores from post-film scores. A one-way ANOVA on mood change scores with group as a factor revealed a significant main effect . Follow-up t-tests showed that the attentional bias to angry faces was reduced in the down-regulation group  compared to the up-regulation group , t(36) = 2.43, p = 0.020, but not to the no-regulation group , t(36) = 0.85, p > 0.250. There was a marginally significant increase in attentional bias to angry faces in the up- compared to no-regulation group, t(36) = 1.72, p = 0.094 . A one-way ANOVA with group as a factor did not reveal any group differences in orienting to or disengagement from angry faces, F = 0.80, p = 0.455, and F = 2.20, p = 0.120, respectively.A one-way ANOVA on attentional biases to angry faces with group as a factor revealed a significant effect of group, . No significant group effects were observed for orienting to happy faces, F = 0.59, p = 0.556, and disengagement from them, F = 1.15, p = 0.325.A one-way ANOVA on attentional biases to happy faces with group as a factor revealed no group differences, [Forty-seven right-handed college students  were recruited through campus flyers. The sample size was determined based on pilot studies and previous studies . ParticiThe dot-probe task was similar to Study 1, except that the faces were selected from a different database and all conditions had the same number of trials. A total of 16 angry , 16 happy , and 64 neutral  faces were selected from a standardized database of face photographs . Valencer = -0.19, p = 0.197).After signing the consent form, participants completed Spielberger State-Trait Anxiety Inventory STAI; . This sct-tests were performed comparing attentional bias scores against zero. We also performed multiple regression analysis to identify variables predictive for each angry and happy bias index . As predictor variables, we entered trait reappraisal, trait suppression, trait anxiety (STAI-T), state anxiety (STAI-S), as well as individual variables such as age and gender. Statistical analyses were conducted using SPSS 19 .As in Study 1, trials with incorrect responses (0.7% of the data) and response outliers (1.2% of the data) were excluded from the analyses. Attentional bias, orienting, and disengagement scores were calculated separately for angry and happy faces. One-sample t-tests revealed a statistically significant bias toward angry faces, t(46) = 3.89, p < 0.001, but no significant bias toward happy faces, t(46) < 1, p > 0.250, which is consistent with what we found from Study 1. Multiple regression analyses revealed that overall attentional bias toward angry faces were predicted by trait reappraisal  and state anxiety . Interestingly, reappraisal negatively predicted orienting to angry faces  and state anxiety positively predicted disengagement from angry faces . On the other hand, overall attentional bias toward happy faces was predicted by suppression . However, no predictive factors were observed for orienting. A marginal level of relationship between suppression and happy disengagement was found .One-sample In this research, we investigated whether reappraisal modulated attentional biases to angry and happy faces in two studies. In Study 1, we found that self-reported negative mood, induced by a short film clip with unpleasant content, diminished in participants who reappraised to reduce negative emotions while watching the film clip as compared to those who reappraised to increase negative emotions and those who naturally viewed the film without reappraisal. More interestingly, participants who reappraised to reduce emotions also showed smaller attentional biases to subsequently presented angry faces compared to those who reappraised to increase emotions. There were no group differences in attentional biases to happy faces. In Study 2, we found that individuals more prone to using reappraisal in regulating everyday emotions were slower in orienting toward angry faces, whereas those more prone to using suppression were slower in disengaging from happy faces. Together, these results suggest that instructed and habitual patterns of emotion regulation are associated with attentional processes toward emotional information.Across Studies 1 and 2, we found that both instruction-induced, state reappraisal and trait reappraisal contribute to attentional responses to angry faces. Reappraisal to reduce negative emotions during film viewing induced less vigilant attention to subsequently presented angry faces compared with reappraisal to increase negative emotions. Notably, participants were not instructed to reappraise emotions during the dot-probe task. Their explicit goal was to respond to the dot-probe as quickly as possible. Therefore, it appears that altered emotional mood states due to reappraisal while viewing the film clip were transferred to the experimental task and modulated attentional processes to subsequently presented angry faces. Alternatively, participants may have continued to reappraise during the dot-probe task, which could have directly influenced overt attention. However, a previous report showed that people tend to direct overt attention to the emotion source when they have an explicit reappraisal goal . TherefoAttentional bias did not differ between the down- and no-regulation groups. Therefore, it remains unclear whether this reduced attentional bias was truly due to decreased attentional bias in the down-regulation group or increased attentional bias in the up-regulation group. There was a trend toward a significant increase in attentional bias in the up- versus no-regulation group. Although the interpretation of these results is limited, the current findings still have important implications for clinical applications. Given that patients with emotional disorders have heightened attentional biases to task-irrelevant emotional information, our findings suggest that reappraisal may be useful for reducing a maladaptive level of attentional bias. More studies are needed to precisely elucidate the role of appraisal on attentional bias.It has been repeatedly demonstrated that reappraisal has acute effects on affective states; however, its long-term cumulative effect still has to be determined . The resInterestingly, multiple regression analyses in Study 2 revealed that attentional biases to happy faces were predicted by trait suppression. More specifically, individuals with greater tendency to suppress emotions tended to disengage more slowly from happy faces even if they were task-irrelevant. However, the underlying mechanisms of the association between habitual suppression of emotions and attentional bias toward happy faces are still unclear. Although it remains speculative, longer attention time to dwell on happy faces signaling acceptance and safety might be instrumental in accomplishing an internally driven goal of managing emotions. Habitual suppression has been associated with heightened anxiety and increased sympathetic activation , which cIt is notable that no measurable overall attentional bias to happy faces was observed. This lack of attentional bias to positive expressions is interesting given that quick and elaborate processing of positive information is as important and evolutionarily adaptive as quick and elaborate processing of negative information . A previAlthough the results of the current research are revealing, several limitations are also identified. First, in Study 1 we used a short film clip depicting a female victim of violence to induce negative emotions and found differences in attentional biases to angry faces following reappraisal. However, we did not observe differences in happy faces. It would be interesting and worthwhile to see whether reappraisal of positive emotions would result in changes in attentional biases to happy faces. Second, Study 1 tested only female participants in order to control sources of variability such as initial emotional responses to women victim and types of reappraisal strategies. A bigger scale of study with both male and female participants is warranted for better understanding of factors influencing attentional biases to emotional information. Third, in Study 1 we did not assess initial baseline levels of attentional biases before the exposure to the film clip. Repeated assessment of attentional biases before and after treatment may have the potential to increase demand characteristics bias. Despite that understanding, a baseline level of attentional bias measure for each subject would have allowed to draw stronger conclusions in relation to the influence of instructed reappraisal on attentional biases. Finally, in Study 2 we did not evaluate current mood states although we assessed current state anxiety. The inclusion of both current positive and negative affect as predictive factors in our analyses might have been more revealing, especially regarding the relationship between positive affect and attentional biases to happy faces.To conclude, the main finding of the current research revealed that both instruction-induced state reappraisal and trait reappraisal are linked to reduced attentional bias and slower orientating to task-irrelevant angry faces. These results contribute to our understanding of how reappraisal regulates negative emotions and provides insight relevant to the development of treatment strategies for emotional disorders.SAK and SHK conceived and designed the study. SAK conducted the study and collected the data. SAK and SHK analyzed the data. SAK, HK, and SHK interpreted the data. SAK drafted the first version of the manuscript. HK and SHK revised the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Several detection techniques, including fluorescent, electrochemical, and electrical methods, have been extensively investigated to selectively recognize K+ ions. In this work, a highly sensitive and selective biosensor based on single-layer graphene has been developed for K+ ion detection under Van der Pauw measurement configuration. With pre-immobilization of guanine-rich DNA on the graphene surface, the graphene devices exhibit a very low limit of detection (≈1 nM) with a dynamic range of 1 nM–10 μM and excellent K+ ion specificity against other alkali cations, such as Na+ ions. The origin of K+ ion selectivity can be attributed to the fact that the formation of guanine-quadruplexes from guanine-rich DNA has a strong affinity for capturing K+ ions. The graphene-based biosensors with improved sensing performance for K+ ion recognition can be applied to health monitoring and early disease diagnosis.Potassium (K Graphene-based biosensors have attracted much research interest recently.Potassium  of a thin semiconductor, by placing it in a magnetic field with a four-point electrode configuration [+ ion biosensors exhibit a high sensitivity as low as 1 nM (10−9 M) and an excellent selectivity (≈4 times) for effectively distinguishing between potassium and sodium ions. The results presented here provide helpful guidance for the design of high-performance ion-selective biosensors for use in healthcare and medical monitoring applications.In this contribution, we fabricated the Hall effect biosensors made of CVD-grown single-layer graphene for detecting Kguration . The car4 and H2 (15:15 sccm) was then flowed into the system to form graphene layers on the copper surface at 1050 °C for reaction of 20 min, followed by cooling the furnace down naturally to room temperature.The graphene films were grown using a thermal CVD with the employment of a 25 µm-thick copper foil as the catalyst  with 25 µm in thickness. The pristine copper foil was cleaned in acetone by ultrasonication for 10 min to remove surface-adsorbed organic impurities. In order to grow graphene, copper foil was set in a tube furnace system, which was heated from room temperature to 1050 °C at a heating rate of 17.5 °C/min with 40 sccm hydrogen flow. When the furnace temperature reached 1050 °C, the copper foil was annealed for an additional 40 min for full reduction of native surface oxide layer. A gas mixture of CH2 layer. The copper foil with graphene layers on the surface was first cut into a sheet with a size of 8 mm × 8 mm, and then a ≈200 nm-thick PMMA layer was deposited on the graphene surface by spin-coating at a spin speed of 500 rpm (10 s) and 4000 rpm (1 min), respectively. The copper foil was then etched away by immersing the sample in 0.1 M ammonium persulfate ((NH4)2S2O8) aqueous solution at 60 °C for 5 h. After complete removal of copper, the PMMA/graphene layer was cleaned with deionized water 3 times and fished onto the SiO2/Si substrate. The sample was dried at 60 °C for 30 min on the hot plate, followed by immersing in acetone at 60 °C for 6 min to dissolve the PMMA. Finally, a post-annealing treatment was performed at 450 °C to improve the interface between graphene and the substrate, as well as guarantee the surface cleanness by decomposition of PMMA micro-residues.In order to fabricate the biosensor, the as-prepared graphene films need to be detached from copper foil and then placed on the silicon substrate coated with a 300 nm insulating SiOThe morphology, quality, and surface chemical compositions of graphene films were identified using optical microscope , atomic force microscopy , Raman spectrometer with 532 nm excitation wavelength of He–Ne laser , and X-ray photoelectron spectroscopy , respectively. The transmittance of the sample in the visible light region was determined by UV-Vis spectroscopy . The electrical signals of graphene biosensors for the detection of alkali ions were recorded by Hall effect measurement system .2/Si substrate is very weak. The characteristic peaks of graphene can be found in its Raman spectrum (−1) and a strong 2D-band (≈2700 cm−1) [−1), the high I2D/IG ratio (1.98), and the narrow full-width at half-maximum of 2D-band (≈38 cm−1) demonstrate the nature of high-quality single-layer graphene prepared by the catalytic CVD method. The organic contaminants would be introduced to the graphene surface during the transfer process and also degrade sensor performance [The layer number and quality of graphene films were identified as presented in spectrum b with a 00 cm−1) ,32. The formance . As the formance ,34. The formance ,36. Figu+ ions, a flexible single-stranded DNA with guanine-rich sequences (5′-GGTTGGTGTGGTTGG-3′) was immobilized on the graphene surface as a probe, which could fold into a tetraplex structure (guanine-quadruplexes) with K+ ions due to the formation of intramolecular hydrogen bonding [n-doping and charged impurity scattering from the immobilized DNA strands, respectively [R) is determined by the product of carrier concentration (n) and mobility (μ) based on the Van der Pauw principle: R + ion detection without the interference from the background buffer ions and DNA probes.It is well known that the basal plane of graphene exhibits a strong interaction with foreign molecules, such as antigens, DNAs, proteins, etc., leading to the achievement of a very low detection limit of the biosensors based on graphene . However bonding . Before ectively ,40,41. I∝ 1/(μn) . We foun+ ion specificity of probe DNA-immobilized graphene device, a control experiment using Na+ ions was implemented and the sensor measurements using the graphene without functionalization were also performed for the comparison. The solutions containing K+ and Na+ ions with the desired concentrations were prepared by dissolving KCl and NaCl in 1× TE buffer, respectively. The device after incubation with DNA probes was rinsed with 1 mL 1× TE buffer for 3 times to remove weakly bound DNAs, and the electrical properties of graphene were recorded. The unfunctionalized graphene was also immersed in 1× TE buffer for 15 h, followed by the same rinsing process. A 100 μL drop with 1 nM K+ ions was individually placed on the surface of graphene with and without DNA immobilization for 6 h, and the electrical properties were recorded again after washing with 1× TE buffer. The standard deviation of each data point in + ions to demonstrate the selective detection of our sensors. As shown in + ion recognition, even the graphene surface has been modified with guanine-rich DNA strand. In + and Na+ ions in the case of probe DNA-immobilized graphene, suggesting that the monitoring of carrier concentration of the Hall effect device can effectively distinguish K+ ions from other alkali ions. In order to examine the K+ ion recognition was investigated, as presented in 13 to 1.30 × 1013/cm2) when the K+ ion is the target. In contrast, it decreases only 4.7% (from 1.90 × 1013 to 1.81 × 1013/cm2) for sensing Na+ ions, demonstrating that our graphene sensing platform based on the Van der Pauw measurements provides a high sensitivity and distinct selectivity for the detection of K+ ions. Compared to other graphene-based devices [+ ions was reported using mechanically exfoliated graphene FETs [+ ions (diameter: ≈2.7 Å), a bipyramidal antiprismatic arrangement can be implemented accordingly [+ ions was much larger than other interference ions, even the added concentration of interference ions (10 nM) is 10 times higher than that of K+ ions (1 nM). The signal deviation for K+ ions is 3.2 and 16.3 times higher than that of Na+ and other cations , respectively. The results confirm that the proposed graphene biosensor not only shows a high sensitivity, but also has a pronounced selectivity towards K+ ions.The performance of graphene biosensors functionalized with DNA probes for selective K devices ,44,45, aene FETs . The higene FETs ,47,48,49+ ions. We demonstrated that among the electrical properties determined from Van der Pauw measurements, the change of carrier concentration of the device is extraordinarily sensitive to the added K+ ion concentration, and less dependent on that of Na+ ions. The K+ ion specificity is attributed to the spatial matching between K+ ions and guanine-quadruplexes, which is a tetraplex structure folded from guanine-rich DNA strands immobilized on the graphene surface. As a result, our devices exhibit a low detection limit (≈1 nM), wide dynamic range (1 nM–10 μM), and remarkable K+ ion selectivity against Na+ and other cations. The proposed sensing platform is feasible and effective in monitoring potassium ions in chemical and biological environments.In summary, the Hall effect biosensor fabricated with single-layer CVD graphene shows great sensitivity for highly selective detection of K


Background. The natural history of idiopathic pulmonary fibrosis (IPF) is very complex and unpredictable. Some patients will experience acute exacerbation (AE) and fatal outcomes. Methods. The study included 30 AE-IPF patients, 32 stable IPF (S-IPF) patients, and 12 healthy controls. We measured the plasma concentrations of leptin and KL-6. Simple correlation was used to assess associations between leptin and other variables. Plasma leptin levels were compared between AE-IPF and S-IPF subjects, decedents, and survivors. Kaplan-Meier curves were used to display survival and Cox proportional hazards regression was used to examine risk factors for survival. Results. In subjects with AE-IPF, plasma leptin was significantly greater than in subjects with S-IPF (p = 0.0003) or healthy controls (p < 0.0001). Plasma leptin was correlated with BMI, KL-6, LDH, CRP, and PaO2/FiO2 . Plasma leptin was significantly greater in 33 decedents than in the 23 survivors (p = 0.007). Multivariate Cox regression analysis showed leptin (>13.79 ng/mL) was an independent predictor of survival (p = 0.004). Conclusions. Leptin could be a promising plasma biomarker of AE-IPF occurrence and predictor of survival in IPF patients. The natural history of idiopathic pulmonary fibrosis (IPF) is characterized by decreasing pulmonary function over time. Some patients may experience acute exacerbations (AE)—sudden worsening of pulmonary function, increased oxygen requirements, new opacities on chest imaging, and a diffuse alveolar damage (DAD) pattern of lung injury without an identifiable etiology . EstimatLeptin was first discovered and cloned by Zhang et al. in 1994 . Leptin β1) ..p < 0.00 Cox model that included leptin and controlled for two other clinically important predictors, leptin was the independent predictor of time-to-death (In a multivariableto-death .p < 0.001) (p = 0.004)  and 13.79 ng/mL , respectively. The area under the ROC curve for leptin in distinguishing AE-IPF from S-IPF was 0.761   and for = 0.004) .p = 0.000; 0.729, p = 0.003) was a better biomarker of IPF acute exacerbation and survival than CRP   and 1(B) in Supplementary Material available online at http://dx.doi.org/10.1155/2016/6940480).CRP is also significantly elevated in patients with AE-IPF than S-IPF shown in . By compIn this study, we assessed plasma leptin concentrations in patients with IPF and in healthy controls and found the following: leptin concentration was higher in subjects with AE-IPF than those with S-IPF and higher in subjects who died than in those who survived to vital status ascertainment. Survival was significantly shorter for patients with leptin levels above, compared with those below, 13.79 ng/mL, and leptin was an independent predictor of survival when controlling for age and oxygenation.The causes and mechanism of acute exacerbation in patients with IPF remain unclear, although it is well known that patients with AE-IPF have significant morbidity and high mortality , 4. Idenβ1 signaling in lung fibroblasts; it does so by inhibiting PPARγ. In addition, leptin signaling is required for bleomycin-induced lung fibrosis [Leptin is a proinflammatory cytokine and plays an important role in the pathogenesis of ARDS, liver, and lung fibrosis , 18, 23.fibrosis . Additiofibrosis , 27. We The mortality of AE-IPF subjects was significantly higher than S-IPF subjects, consistent with the published reports –4. In adThis study has limitations. First, the number of patients and normal controls enrolled was small, and the sample was entirely Asian (Chinese). These may substantially limit the ability to generalize results to the IPF universe, including the 13.79 ng/mL cut-off value. We have no data on the within-subject variability of leptin; levels from the same patient in different clinical circumstances  would be informative and provide data to further support the utility of leptin as a biomarker of disease. A prospective, multicenter, and multinational study of a larger patient cohort would be useful to help provide additional data on leptin.In summary, we found that leptin was elevated in AE-IPF and that high plasma leptin concentrations are associated with poor survival. Additional research is needed to confirm and extend these results, to determine whether and how leptin plays a role in the pathogenesis of AE, and to delineate the utility of plasma leptin as a biomarker of AE-IPF occurrence and predictor of survival in IPF patients.Plasma leptin level  was a better marker of IPF exacerbation and survival than CRP .


This value is in excellent agreement with reported macroscopic values and our atomistic simulations. This implies mechanical properties of graphene scale down to a few-nanometre length. The simplicity of our method provides a unique opportunity to investigate the local variability of nanomechanical properties in layered materials.The interest in mechanical properties of two-dimensional materials has emerged in light of new device concepts taking advantage of flexing, adhesion and friction. Here we demonstrate an effective method to measure adhesion of graphene atop highly ordered pyrolytic graphite, utilizing atomic-scale ‘blisters' created in the top layer by neon atom intercalates. Detailed analysis of scanning tunnelling microscopy images is used to reconstruct atomic positions and the strain map within the deformed graphene layer, and demonstrate the tip-induced subsurface translation of neon atoms. We invoke an analytical model, originally devised for graphene macroscopic deformations, to determine the graphite adhesion energy of 0.221±0.011 J m Analysis of the mechanical properties of two-dimensional materials is important for device development. Here, the authors report a microscopic method for measuring the adhesion of graphene on top of highly ordered pyrolytic graphite, which exploits atomic-scale blisters formed upon neon atom intercalation. We intercalate neon atoms into graphite, which in turn deform enclosing graphene sheets to create atomic-scale blisters on the surface. Detailed experimental analysis of these atomic blisters leads to a direct estimate of adhesion energy of ∼0.221 J mh of ∼0.14 nm and full-width at half-maximum of ∼0.93 nm, respectively  charge transfer between the intercalated Ne atoms and surrounding carbon atoms of the graphite, further asserting that we can treat Ne intercalates as essentially neutral atoms.Representative scanning tunnelling microscopy (STM) images of graphene blisters on highly ordered pyrolytic graphite (HOPG) are shown in ectively . For refectively . The vanectively , we esti eV refs , 22. Ourx–y–z coordinate map of atom centres extracted from et al.et al.To map out atomic positions and their local neighbourhoods, we employed image processing as illustrated in et al.h>10 nm). The nonlinear plate model is generally more accurate and suitable for all sized bubbles. Within this model, the adhesion energy (Γ) of the graphene blister having a characteristic radius a and a height h, 10μ represents a function of the Poisson ratio ν:Here h<0.3 nm and h<<a. In such cases the first term in The linear plate model is an approximation of the nonlinear plate model for small sized bubbles generally having h and radius a. Although this linear plate model was derived for a small blister, it's validity for atomic scale is not clear a priori. In this study, we test this model extension by using the simplified version of the linear plate model given in h and the radius a of the atomic blister, we fit the STM line-profile with a Gaussian function:which is simply a function of the blister's height as indicated by the red curve in a and the height h, and obtained the adhesion energy via −2  unit, E2D is the 2D Young's modulus of graphene, D is the bending stiffness, a and h are radius and height of the blister; while μ represents the function of the Poisson ratio ν given in E2D=0.323 TPa nm and ν=0.179, we estimate a pressure of 5.4 GPa within the nonlinear plate model (an estimate for the membrane model is 6.3 GPa). For reference, the pressure of the macroscopic blisters determined in the work of Yue et al.where −2. Notably the adhesion energy does decrease with the increase in the number of intercalated Ne atoms. These results suggest that the local adhesion of graphene layers can be substantially influenced by an increase in the number of intercalated neon atoms, particularly if they are localized in the same region. It is also expected that further increase in concentration of intercalated neon atoms can induce local exfoliation of the top graphene layer.Our approach is further validated by atomistic modelling using DFT calculations. Our experiments further indicate the feasibility of tip-induced manipulation of subsurface Ne atoms. et al.One may question the applicability of STM to measure topographic height. In fact, it is well-known that the atomic corrugation of graphite in STM images depends on the tip conditions3031323334353632333233z-height in this case to make the atomic corrugation in −2 was obtained out of 32 total measurements of the rescaled blister . Nc-AFM detects short-range forces, and the nc-AFM image provides direction information about the corresponding tip–surface interactionIn conclusion, we demonstrate a direct method to measure the adhesion energy of graphene on graphite, by intercalating inert gas atoms between graphene sheets. Measurements are based on the shape and heights of the graphene blisters at the atomic scale and analysed using analytical methods (linear plate models) and DFT calculations. Our results are in good agreement with recently reported direct measurements of adhesion energy for graphite. Local topographic analysis also provides detailed atomic information related to the graphene blisters, such as local strain and the mobility of subsurface noble gas atoms. We envision that this methodology can be applied to other layered materials to estimate the adhesion energy and the related elastic mechanical properties at atomic level. Moreover, intercalation of noble gas atoms may provide a feasible pathway to create electronically interesting ‘Gaussian' impurities39−10 mbar. The nc-AFM measurements were carried out in the same system with a SPECS Kolibri sensor. HOPG was freshly cleaved by the scotch tape method and quickly transferred into ultrahigh vacuum. The HOPG was subsequently annealed up to 500 °C for ∼20 min. The blisters and associated defects on HOPG were created by briefly sputtering the sample with Ne+ ions . These sputtering conditions are necessary for the reproducibility of atomic sized blisters. Lighter neon was chosen over argon to minimize creation of point defects by sputtering. STM images were taken after cooling the sample to both liquid nitrogen (77 K) and liquid helium (4.3 K) temperatures. The STM profile measurements over different blisters taken at 77 and 4.3 K yield consistent results, suggesting in this temperature range adhesion energy shows little change, as may be expected  positions were determined with subpixel precision using a combination of image processing and fitting routines. After the atomic positions were established, a survey of the six nearest neighbours was conducted to obtain the data in An HOPG sample (Grade SPI-1) was purchased from SPI Supplies. The experiments were conducted by using the SPECS Joule-Thomson (JT) cryogenic STM/AFM in an ultrahigh vacuum chamber with a base pressure ≤1 × 10cted see . Atomic-ab initio simulation package (VASP)434649−1 k-point separation was used for STM image calculations.The adsorption properties of Ne atoms in the gap between two parallel graphene layers were investigated based on DFT calculations using Vienna The data that support the findings of this study are available from the corresponding author upon request.How to cite this article: Wang, J. et al. Atomic intercalation to measure adhesion of graphene on graphite. Nat. Commun.7, 13263 doi: 10.1038/ncomms13263 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1-6, Supplementary Notes 1-3 and Supplementary References.


There are many reasons why microbial community composition is difficult to model. For example, the high diversity and high rate of change of these communities make it challenging to identify causes of community turnover. Furthermore, the processes that shape community composition can be either deterministic, which cause communities to converge upon similar compositions, or stochastic, which increase variability in community composition. However, modeling microbial community composition is possible only if microbes show repeatable responses to extrinsic forcing. In this study, we hypothesized that environmental stress acts as a deterministic force that shapes microbial community composition. Other studies have investigated if disturbances can alter microbial community composition, but relatively few studies ask about the repeatability of the effects of disturbances. Mechanistic models implicitly assume that communities show consistent responses to stressors; here, we define and quantify microbial variability to test this assumption. A central pursuit of microbial ecology is to accurately model changes in microbial community composition in response to environmental factors. This goal requires a thorough understanding of the drivers of variability in microbial populations. However, most microbial ecology studies focus on the effects of environmental factors on mean population abundances, rather than on population variability. Here, we imposed several experimental disturbances upon periphyton communities and analyzed the variability of populations within disturbed communities compared with those in undisturbed communities. We analyzed both the bacterial and the diatom communities in the periphyton under nine different disturbance regimes, including regimes that contained multiple disturbances. We found several similarities in the responses of the two communities to disturbance; all significant treatment effects showed that populations became less variable as the result of environmental disturbances. Furthermore, multiple disturbances to these communities were often interactive, meaning that the effects of two disturbances could not have been predicted from studying single disturbances in isolation. These results suggest that environmental factors had repeatable effects on populations within microbial communities, thereby creating communities that were more similar as a result of disturbances. These experiments add to the predictive framework of microbial ecology by quantifying variability in microbial populations and by demonstrating that disturbances can place consistent constraints on the abundance of microbial populations. Although models will never be fully predictive due to stochastic forces, these results indicate that environmental stressors may increase the ability of models to capture microbial community dynamics because of their consistent effects on microbial populations.IMPORTANCE There are many reasons why microbial community composition is difficult to model. For example, the high diversity and high rate of change of these communities make it challenging to identify causes of community turnover. Furthermore, the processes that shape community composition can be either deterministic, which cause communities to converge upon similar compositions, or stochastic, which increase variability in community composition. However, modeling microbial community composition is possible only if microbes show repeatable responses to extrinsic forcing. In this study, we hypothesized that environmental stress acts as a deterministic force that shapes microbial community composition. Other studies have investigated if disturbances can alter microbial community composition, but relatively few studies ask about the repeatability of the effects of disturbances. Mechanistic models implicitly assume that communities show consistent responses to stressors; here, we define and quantify microbial variability to test this assumption.Author Video: An author video summary of this article is available. Many central questions in ecology focus on the sources of variability in populations. Accuracy of predictions is highly valued in ecological studies, and population size is necessarily more predictable when populations are less variable , 2. StilSome variability naturally exists in all populations. Disturbances could act either to increase or to decrease this level of variability , 7. The Predicting the composition of microbial communities using environmental disturbances is a major objective of microbial ecology . Here, w16–To address the strong interest in understanding drivers of variability in microbial community composition, some studies have analyzed the range of compositions observed in bacterial communities following novel disturbances. These experiments have found consistent changes in community composition due to the disturbance , 21. ThuIn this study, we imposed several disturbance regimes upon periphyton communities in order to examine the effects of disturbances on the variability of populations within the periphyton. Our goal was to determine whether disturbances have repeatable effects on periphyton communities. After initially growing 108 periphyton communities on Plexiglas slides in a common environment, we then randomized each of these replicate communities into one of nine treatments, each corresponding to a different disturbance regime. To generate these nine treatments, we subjected periphyton communities to one of three possible conditions  at two time points. These two disturbances were selected because they are both potential consequences of the high-wind events that occur in our study system . This 3 We quantified the variability of populations between communities within each treatment using the coefficient of variation (CV) of each taxon. The coefficient of variation is calculated as the standard deviation of the populations divided by the mean abundance of the populations and therefore has the advantage of accounting for variance-mean scaling . We tranPeriphyton colonized Plexiglas slides suspended in a shallow  eutrophDiatom taxa on slides were enumerated by light microscopy to measure the abundance of each taxon within the periphyton biofilm. We used linear mixed models to analyze how the variability of taxa in the disturbed treatments compared to the variability of taxa in the undisturbed treatment, AA. The fixed effects in the model were 4 binary variables corresponding to whether the community received the depth disturbance at T1, the scouring disturbance at T1, the depth disturbance at T2, or the scouring disturbance at T2. A random effect for taxon was included, under the assumption that taxa have differing amounts of inherent population variability. The response variable in the linear mixed models was the square root CV of the taxon populations, measured in density per square centimeter. A lower CV corresponded to lower variability of a taxon between communities in the same treatment. The estimated treatment effects from the mixed model represent the mean differences in square root CVs between a given treatment and the undisturbed treatment, AA.P values were obtained from the linear mixed model for the diatom taxa. The scouring disturbance reduced the square root CV of the diatom communities by a mean of 0.155 at T1 (P = 0.0343) and by 0.158 at T2 (P = 0.0307). The altered-depth disturbance reduced the square root CV by 0.156 at T1 (P = 0.0330) and by 0.230 at T2 (P = 0.0016). Thus, densities of diatom taxa became more consistent as a result of experiencing one disturbance, regardless of whether the disturbance was applied at T1 or T2.The treatment that had the highest mean taxon square root CV was the undisturbed treatment, AA. Thus, taxon populations in the AA treatment were the most variable of any treatment. The four treatments that received one disturbance, SA, AS, DA, and AD, all had significantly lower square root CVs than the AA treatment . These tP = 0.0458 and P = 0.0363, respectively). In these treatments, which received the same type of disturbance at T1 and T2, the taxa were more variable than would be expected from the independent effects of the disturbances at T1 and T2. However, there was no significant interaction between disturbances in communities that experienced different types of disturbances at T1 and T2 (corresponding to treatments DS and SD). Therefore, communities that received different disturbances at the two time points continued to become less variable as a result of experiencing another disturbance, whereas communities that experienced the same disturbance twice did not become as consistent.In both the DD and SS treatments, there were significant positive interactions between the disturbances at T1 and T2  to determine if the community composition shifted as a result of the disturbances. We compared the undisturbed treatment, AA, to the disturbed treatments to identify whether the disturbed communities separated from the AA treatment in community composition. Strong separation of disturbed and undisturbed communities would indicate novel community development in the disturbed treatments. The PCA for the diatom communities captured 97.2% of community variability in the first two axes. The first axis, responsible for 92.8% of variability, represented the tradeoff between communities dominated by The communities in the undisturbed treatment, AA, occupied a large area of the PCA space see . CommuniP values reported for the bacterial communities were obtained from this analysis.Bacterial community composition in the periphyton was determined using a PCR-based DNA fingerprinting method called automated ribosomal intergenic spacer analysis (ARISA) . This meThe analyses for the bacterial communities used the residuals from detrended taxon CVs as the response variable. Detrending was performed because the CVs of the OTUs showed a strong relationship with mean OTU abundances, whereby OTUs with higher mean abundances had lower CVs. To remove the effect of mean abundance, we fitted an exponential model of the OTU CVs as a function of log(OTU mean abundances) . We thenFor the bacterial communities, AA had the median level of variability out of the nine treatments. However, no treatment was significantly more variable than the AA treatment. No single disturbance at either T1 or T2 had significant effects on the variability of OTUs . Thus, tAs with the diatom communities, we compared the bacterial community composition of the AA treatment to those of the disturbed treatments using a PCA. We evaluated whether the communities in the AA treatment separated from the communities in disturbed treatments to determine if there were consistent compositional differences between the undisturbed and disturbed communities. The PCA for the bacterial communities captured 51.2% of community variability in the first two axes. The first axis accounted for 27.6% of community variability, and the second axis accounted for 23.6% of the variability. The loadings on these two axes were primarily from the most abundant OTUs across all treatments.The communities in the undisturbed treatment, AA, occurred in close proximity to disturbed communities in PCA space . FurtherP = 0.540, P = 0.554, and P = 0.754 for Sorensen, Euclidean, and Bray-Curtis metrics, respectively).We used three different dissimilarity metrics  in Mantel tests to evaluate whether differences in the diatom communities were related to differences in the bacterial communities. There was no significant relationship between the bacterial and diatom communities for any of the three metrics used  disturbances decrease the variability of populations within diatom and bacterial communities and that (ii) multiple disturbances have interactive effects. For the diatom communities, every treatment that experienced a single disturbance had a significantly lower square root CV than did the AA treatment. This consistent result shows that communities that were disturbed once became less variable than communities that were undisturbed. However, double disturbances did not necessarily cause the communities to become increasingly more consistent. Both the SS and the DD treatments had significant positive interactions, showing that these communities were more variable than would be expected based on the independent effects of the single disturbances. However, the treatments that experienced different disturbances at the two time points (SD and DS) continued to become less variable as a result of the second disturbance. Thus, for the diatom communities, different disturbances continued to increase the consistency of the diatom communities, although the same recurring disturbance appeared to be saturating in effect. Therefore, the interactions between sequential disturbances were important to understanding community dynamics in treatments that were disturbed twice.For the bacterial communities, no single disturbance had a significant effect on the variability of OTUs within the communities. However, three of the four treatments that experienced two disturbances had interactive effects, all of which led to lower population CVs. Thus, multiple disturbances to the bacterial communities generally created communities that were less variable than communities that were disturbed once. The strong interactions indicate that multiple disturbances had novel effects on the communities, such that the communities that experienced two disturbances demonstrated much-different responses than the communities that experienced only one disturbance. Additionally, it appears that high levels of disturbance were necessary to generate changes in the bacterial communities, because the only treatments to show significant effects were disturbed twice. This was not surprising, as pelagic lake bacterial communities were previously found to be highly resilient to disturbances  and therDespite differences in how the diatoms and the bacteria responded to individual treatments, there were several broad similarities between the diatom and bacterial responses to disturbances. For instance, none of the disturbed treatments in either the diatom or bacterial communities showed significantly greater variability than the undisturbed treatment, AA. Additionally, in both the diatom and the bacterial communities, at least two of the three least variable treatments were highly disturbed, having experienced two disturbances . These rThe differences in the diatom and bacterial responses to individual disturbance treatments may be due to additional drivers of population variability within these two communities. For example, the strength and number of species interactions in a community can also be an important determinant of population variability . AlthougGomphonema species have been found to be tolerant of turbulent conditions, showing high abundances in water currents , whereas the altered-depth disturbance was a sustained perturbation (press disturbance). The similar responses of the communities to these two disturbances are in line with a recent review showing that a high proportion of microbial communities are sensitive to both press and pulse disturbances; of the experiments reviewed, 92 of 112 microbial communities showed a change in composition or function in response to a pulse disturbance, and 141 of 178 communities changed in response to a press disturbance . Thus, tIn addition to the experimental disturbances, there are many other possible factors that could have influenced the variability of populations within the periphyton. For instance, the periphyton communities experienced environmental variability throughout the duration of the experiment due to natural weather conditions and small-scale variability in environmental forces. Acknowledging this environmental stochasticity, we intentionally implemented experimental disturbances that were more extreme than the natural variability that we observed during this time period. Additionally, we did not account for colonization of diatoms or bacteria after disturbances were implemented, which may have generated additional variability in these communities. However, because the Plexiglas slides were rerandomized between disturbances, we expect systematic bias from immigration to be minimal between treatments. Furthermore, the diatom and bacterial data sets are complementary in their strengths; the diatom data were obtained through direct counts, meaning that there is high accuracy in identification, although only a subsample of the community was measured. Conversely, nearly the entire bacterial community was sampled but with some degree of bias from using ARISA . Thus, bPrediction of microbial communities is an oft-cited goal of microbial ecology. However, predictive models can be accurate only if the process that they are describing is inherently repeatable. For instance, statistical models will produce a good fit to microbial community composition data only if these microbial communities show consistent responses to environmental drivers. The results of these experiments indicate that microbial communities do show repeatability in their response to environmental stress, because communities became more similar to one another after experiencing the same disturbance. This finding could be tested in other systems by examining whether predictive models of bacterial community composition  lake inDisturbances were implemented at two time points. At the first time point, day 20, we randomly assigned 36 of the 108 slides to each of the following conditions: ambient (no disturbance), altered-depth disturbance (relocation to an 0.5-m depth), or water-scouring disturbance. Weekly water column profiles showed that Secchi depth during the summer of 2013 varied between 1.5 m and 3.3 m, with 11 to 33% of surface photosynthetically active radiation (PAR) reaching 3-m depth. We chose these two disturbances  because they mimic natural disturbances to periphyton communities in the lake due to the high-wind events that are common at our study site , 47. DurSimilarly, on day 25, we again randomized the slides into three groups and manipulated the slides with the disturbances described above, incubating the slides for another 5 days. On day 30 of the experiment, we retrieved the Plexiglas slides from the lake and froze the slides at −20°C until further processing. Additional details about the experimental manipulations are provided in 10.1128/mSystems.00013-16.3Figure S1 Figure S1, PDF file, 0.1 MB.At the start of the experiment, the 108 Plexiglas slides were distributed across 6 identical metal racks. All 6 racks were deployed side by side on buoy lines in Lake Myvatn and were suspended 0.3 m from the sediment surface. After 20 days of periphyton colonization (T1), slides were again randomized before experiencing the first disturbance. Again, on day 25, slides were randomized onto new racks corresponding to the disturbance experienced at T2. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Slides were removed briefly from the freezer to obtain diatom counts on a microscope before being frozen again. Diatoms were identified to the lowest taxonomic resolution possible, which was genus or species. A minimum of 500 individuals per slide were identified by counting half-transects across slides. The mean number of individuals identified per sample was 1,063, for a total of 114,843 individuals across the 108 Plexiglas slides. We then transported the slides to Madison, WI, USA, for analysis of the bacterial communities in the periphyton using automated ribosomal intergenic spacer analysis (ARISA) , 49. BriNitzschia holsatica, Cymbella spp., Synedra spp., Gomphonema spp., Rhoicosphenia spp., Cocconeis spp., colonial Fragilaria spp., and singular Fragilaria spp.). Together, these 8 taxa accounted for 99.4% of all individuals counted. We standardized all data to densities of each taxon per square centimeter. For each of the nine treatments, we calculated the coefficient of variation (CV) for each of the 8 taxa across the 12 slides in that treatment. We chose the CV as the indicator of population variability because it did not change in response to the mean abundance of the taxa and because it showed homogeneity of variance between treatments. Additionally, the CV integrates across all 12 slides within a treatment and mitigates the effects of any single anomalous communities. We transformed the CVs by taking their square root because the distribution of CVs was slightly skewed toward larger values.Because some taxa were rare and therefore were inconsistently present in samples, we analyzed only the 8 most common diatom taxa  were binary vectors corresponding to whether or not the taxon was in a treatment that received the depth disturbance at T1, the scouring disturbance at T1, the depth disturbance at T2, or the scouring disturbance at T2 (equation 1). We also included all interactions between these four predictor variables to assess the interactive effects of multiple disturbances. We recognized that the taxa may have different inherent levels of population variability, and so we included a random intercept by taxon, denoted by αtaxon. This term assumes that the square root CVs of taxa are normally distributed but estimates only the distribution from which the square root CVs are drawn, rather than an effect for each taxon.We analyzed square root CVs using a linear mixed effects model. The four predictor variables  against the mean abundance of that population. We found that the population CV was not biased by the mean population abundance. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.5Figure S3 Cocconeis spp., Rhoicosphenia spp., Cymbella spp., Synedra spp., Gomphonema spp., Nitzschia holsatica, single Fragilaria spp., and colonial Fragilaria spp. Download Figure S3, PDF file, 0.3 MB.We plotted the estimated random effect from the mixed model against the log of the mean abundance of each diatom taxon. We found no relationship between the fitted random effects and the mean abundances of the diatom taxa. Abbreviations refer to Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.6Figure S4 Figure S4, PDF file, 0.3 MB.Mixed model residuals were not biased by treatment and were approximately normally distributed. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.7Figure S5 Figure S5, PDF file, 0.4 MB.A principal component analysis for the diatom communities shows that the AA treatment polygon overlaps strongly with every other treatment polygon. The AA treatment polygon (red) spans a large portion of the first axis (PC 1), as well as the entire length of the second axis (PC 2). Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.8Figure S6 Figure S6, PDF file, 0.5 MB.A principal component analysis for the bacterial communities shows that the AA treatment polygon (red) overlaps strongly with every other treatment polygon. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the We performed principal component analyses (PCAs) on the diatom and the bacterial communities. Our main goal for these analyses was to evaluate whether the composition of the disturbed communities consistently differed from the composition of undisturbed communities. Because there was a wide range in the mean densities of diatoms on the slides, we transformed the diatom counts into relative abundances before running this analysis. Again, we used only the 8 most common taxa in the diatom PCA. Similarly, in the PCA of the bacterial communities, we removed all OTUs present in fewer than 30 samples.To assess whether there were correlations between the diatom communities and the bacterial communities, we performed Mantel tests on the 106 slides for which we had data on both the diatom and bacterial communities. We used these tests to determine if changes to either the diatom or the bacterial community on a slide could predict changes in the other community.10.1128/mSystems.00013-16.1Text S1 Text S1, DOCX file, 0.1 MB.We provide additional details about the design and data collection of our experiment. We performed diagnostics on the mixed models used to statistically analyze our results so as to verify the appropriateness of these models. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.2Text S2 Text S2, PDF file, 0.1 MB.Approximations for community proportion CVs. We performed simulations to demonstrate that the pattern seen in the CVs of the bacterial data set is an expected result of relativizing the data. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.9Table S1 Table S1, DOCX file, 0.1 MB.Definitions of community properties and their relationships to population variability. Download Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00013-16.10Table S2 P value, which was the fraction of the null distribution of mixed model coefficients that was lower than the observed mixed model coefficient. *, P < 0.05; **, P < 0.01. Download Table S2, DOCX file, 0.1 MB.For each treatment, we calculated a pseudo-Copyright © 2016 Herren et al.2016Herren et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the