Complex real-world challenges are often solved through teamwork. Of special interest are ad hoc teams assembled to complete some task. Many popular multiplayer online battle arena (MOBA) video-games adopt this team formation strategy and thus provide a natural environment to study ad hoc teams. Our work examines data from a popular MOBA game, League of Legends, to understand the evolution of individual performance within ad hoc teams. Our analysis of player performance in successive matches of a gaming session demonstrates that a player’s success deteriorates over the course of the session, but this effect is mitigated by the player’s experience. We also find no significant long-term improvement in the individual performance of most players. Modelling the short-term performance dynamics allows us to accurately predict when players choose to continue to play or end the session. Our findings suggest possible directions for individualized incentives aimed at steering the player’s behaviour and improving team performance. People are often brought together in temporary ad hoc teams to achieve a common goal before moving on to the next problem, likely with a different team. An example of such ad hoc teams can be found in multiplayer online battle arena (MOBA) games. In this popular genre of games, two teams are assembled and face each other, with individuals collaborating with strangers to complete a series of complex, fast-paced tasks  to win the game.Previous studies  showed tHowever, the performance of individuals within teams, and of the teams themselves, may evolve over time, as individuals improve and perfect their skills or learn how to work with others on a given shared task. Understanding how individual and team performance change over time can then provide suitable insights on how to assemble successful teams. To this aim, we study the performance of players in League of Legends (LoL), a popular MOBA game.RQ1 Do players improve over time, as they acquire skills and experience through teamwork?RQ2 Are there notable changes in individual performance during the course of a single team-playing session?RQ3 If performance does change over a session, does experience mitigate its variation?RQ4 What factors predict a player’s choice to continue playing or end a given session?Data from MOBA games like LoL enable us to explore the following four research questions:The data we study contain records of nearly 242 000 solo-queue matches played by 16 665 of the most active LoL players. After segmenting matches by sessions—periods of game play activity without an extended break—we track the player’s performance over the course of the session. We measure performance at two levels: the overall team’s performance and the individual player’s performance. The former is defined as the fraction of matches during a session won by the player’s team. The latter is defined on the basis of three main players’ actions during the game: the number of kills (K), the number of assists (A) and the number of deaths (D). We compute the kill–death–assist (KDA) ratio of the player, which is a value commonly used by players to compare their performance. Interestingly, both measures show that performance generally declines over the course of a single game playing session. This is surprising for two reasons: first, players in solo-queue matches do not choose their teammates in the game (we indeed consider this type of match to avoid the possible influence of playing with friends); second, the game is designed to match opposing teams’ skills and yield an equal probability of winning to each team. However, we systematically observe that the team to which a player is assigned wins on average fewer matches if that player had already played other matches without taking a break. While similar short-term performance deterioration was observed in the context of different online activities, such as commenting on Reddit , this is2.2.1.League of Legends is a multiplayer online game that combines elements of role-playing, real-time strategy and tower defence game genres. A single match consists of a strategic, fast-paced battle between two teams composed of five people, who are usually strangers. A team wins by destroying the opposing team’s nexus, a large structure fortified by defensive towers. While the destruction of the enemy nexus is the main goal, teams also aim to fulfill subgoals, which may be necessary for or conducive to victory; individual players also strive to achieve personal goals, such as a high kill/death ratio.1 With the aim of studying individual performance, we collected information of solo-queue matches, in which players cannot select their teammates. These specific matches allow us to avoid any influence that playing with friends might have on the final performance of players. We additionally require that each player in the dataset has at least 10 matches for two main reasons. First, we want to avoid biases related to players that try the game a few times and never play again. Second, we will focus our analysis on performance evolution in gaming sessions (as described in the following). Thus, we need each player to play at least few sessions in their history. The final dataset [We collected data about LoL by using the LoL’s Riot Games API. dataset  consists2.2.RQ2–RQ3, we will need to identify sessions of continuous player activity. Time series of a player’s matches can be decomposed into gaming sessions, i.e. periods of activity without an extended break. The sessions can be identified by examining time intervals between consecutive matches. Cases where this interval exceeds some predefined threshold are used to separate matches into different sessions [n as the temporally ordered sequence of n matches, with no more than a 15-minute break between matches. The break length, corresponding to the median of the distribution of break times between matches, is computed over the most active players of our dataset (i.e. players having at least 10 matches in their history).To address sessions ,6. Here,To check the robustness of our findings regarding individual performance and verify that they are not due to chance, we also carry out an analysis of randomized session data, i.e. sessions where the order of matches for individual players was randomly shuffled according to the strategy depicted by 2.3.RQ4, in our analysis we will present a prediction task that will leverage the three methods described as follows.To address Random forest is an ensemble-based learning method for classification and prediction that operates by constructing a multitude of decision trees at training time and outputs the class that is the mode of the classes or mean prediction of the individual trees [al trees . Random al trees . In partal trees .Gradient boosting is a machine learning technique which produces a prediction model in the form of an ensemble of weak prediction models, typically decisions trees. Gradient boosting produces competitive, highly robust, interpretable procedures for both regression and classification [fication .Adaptive boosting is a machine learning meta-algorithm which produces a prediction model combining weak learners  into a weighted sum that represents the final output of the boosted classifier [adaptive means that subsequent weak learners are adjusted in favour of those instances misclassified by previous classifiers. Even if such an approach is sensitive to noisy data and outliers, as long as the performance of each weak classifier is slightly better than random guessing, the final predictive model can be proved to converge to a strong learner [assifier ,15. The  learner .player id, match position in a session and match duration; (ii) player’s actions, such as kills, deaths and assists; and finally, (iii) player’s performance measures, such as the KDA and the binary information about whether the player wins in the match or not, etc.Moreover, for each classification method, we learn three models, in which we incrementally add different sets of features: (i) match metadata, such as 3.In this paper, we study the performance of a set of LoL players who played at least 10 solo-queue matches. We require at least 10 matches to consider players who engaged in the game long enough to play a few sessions in their history, and avoid the bias that might occur when considering players that try the game a few times and quit. Importantly, we only select solo-queue matches, in which players cannot decide their team, or part of their team, thus avoiding possible influences of friends in the game.Our dataset is then composed of about 242 000 matches played by 16 665 different players. In the following, we will address the research questions previously defined, and we will provide some insights of the possible mechanisms underlying our observations.3.1.RQ1), thus we compute long-term performance of players by taking into account their entire history in the dataset, i.e. the total number of matches of each player. Here, we consider two measures of performance. First, we define a team performance measure, which is computed as the fraction of wins. Second, we define an individual performance measure, namely the kill–death–assist ratio KDA, defined as k is the number of kills, a is the number of assists and d is the number of deaths of a player in a given match. a,b) and KDA  for each player as they play more matches. As we can observe, there is no long-term team’s performance improvement with experience (ρ=0.02). The longer the users play, the more the performance related to their teams reverts to the mean—which is approximately 0.5 . A possible explanation might be related to the design of the game. In fact, players are given Elo-like ratings—a method used to calculate the relative skill of players in competitor-versus-competitor games such as chess—and these ratings are used to assemble teams of players with comparable skills. In other words, if a player’s skill improves he/she will be paired up against players with similar skill level, and analogously if the skill level decreases. Thus, the likelihood to win each match is not significantly better than 50%. We noted the same effect when studying the KDA ratio, whose values revert to the mean score of 2.7 .First, we examine how performance changes with experience  explores short-term performance over the course of one session. In contrast to long-term performance, player’s performance, measured by both the fraction of matches the player’s team won and the player’s KDA of each match, degrades measurably over the course of a single session. Figure 3a(i),b(ii) provide a comparison between the performance achieved by players in sessions of different length (number of matches going from 1 to 5). We can observe that both types of performance at the end of a session are lower than at the beginning of that session. Moreover, the longer the session, the larger the performance decline: for sessions with three or more matches, the win rate and the KDA value, respectively, deteriorate by more than 10% and 8% between the first and the last matches in the session. Such short-term performance deterioration is not present in the randomized data (figure 3a(ii),b(ii)), suggesting the presence of a real effect and not simply a byproduct of data heterogeneity.Our second question (Performance declines over the course of a session according to both measures (win rate and KDA). The only difference is the initial improvement during longer game playing sessions: this pattern might reflect a ‘warm-up’ period. This pattern is stronger for the team’s performance measure (win rate) than for the player’s performance measure (KDA). The decline in team’s performance suggests that the teams a player is assigned to later in the session do not perform as well as the teams the player is assigned to earlier in the session. On the other hand, deterioration is also observed in individual performance. This phenomenon might be associated with some cognitive effect, such as mental fatigue, boredom or attention decline (we report relevant research in this area in §4).3.3.RQ3), we studied how deterioration is linked to players’ experience. To this end, we ranked players by the number of matches they played and compared highly experienced players (those in the 95th percentile or above) with the less experienced players (those below the 5th percentile by number of matches played). a) and the less experienced ones (b). Performance of the latter group of players declines far more than that of the experienced players. Comparison to randomized data suggests that these trends are not due to chance.Does experience mitigate performance declines? To answer our third research question . The difference is still statistically significant even when only the player’s first 20 sessions are taken into account , indicating that highly experienced players are different from other players already at the beginning of their tenure. These players not only play more games during a session, they also play for longer. Boxplots in figure 5b show that the duration of sessions (in seconds) of the highly versus less experienced players are also significantly different . Although the reason why the more experienced players are able to play longer is still unknown, its net effect is to partially shield these players from the effects of performance deterioration.This suggests that player experience mitigates the mechanisms that lead to short-term deterioration of performance. For example, experienced players may use their available cognitive resources more efficiently and stretch them over more games. Analysis provides some support for the hypothesis that highly experienced players tend to engage in longer gaming sessions compared to the less experienced players. Boxplots in figure 53.4.RQ4), we focus on player engagement. In particular, we examine what characteristics predict if some players engage with short gaming sessions while others go on to have longer sessions. We formulate this problem as a prediction task. Specifically, given a player’s history, described by a set of match-related features, our goal is to predict whether a given match will be the player’s last in the session. We chose three different sets of features to characterize players: features describing matches, game actions and performance. Match features  include:match: current match’s position in the current session;— match duration: duration (in seconds) of the current match;— cumulated match duration: duration (in seconds) of the current session.— mean match duration: average match duration in the current session;— sessions: total number of sessions played until now;— player id: the unique identification of each player;— experience: total number of matches played until current match.— To address our last question  in the game include:KDA: kill–death–assist (KDA) ratio of a player in the current match;— cumulated KDA: KDA ratio of a player in the current session;— mean KDA: average KDA a player achieved per match in the current session;— win: binary variable indicating whether the player won or lost the current match;— session win rate: fraction of wins in the current session;— current win rate: fraction of wins until the current match in the current session.— Finally, we characterize players’ performance  through the following features:positive outcome if that match is the last match of the player’s session, and a negative outcome if the player keeps playing after that match. Our dataset is mildly unbalanced, containing 145 169 positive labels and 261 037 negative ones. This is consistent with the presence of several sessions of length greater than 1 (i.e. with at least two matches). In machine learning, standard evaluation metrics that do not account for uneven class distribution can be misleading. To address this challenge, we perform two different predictive tasks: (i) we use the full  dataset to evaluate the performance of three prediction models by means of the area under the receiving operator characteristic curve (AUC), providing an evaluation for the true and false positive rates of the model predictions (where AUC=1 represents a perfect test); (ii) we under-sample the original data to obtain a balanced dataset and evaluate the performance of our prediction models through standard metrics such as precision , recall , accuracy  and F1 .We label each match in the dataset as a In both prediction tasks, we compare the performance of three ensemble-based prediction models: random forest (RF), gradient boosting (GB) and adaptive boosting (AB). To find the best combination of hyper-parameters, for each classifier we perform a 10-fold cross-validated grid search over the hyper-parameters’ space. To prove robustness of results, we report mean scores and standard deviations obtained via Monte Carlo cross validation. Here, we use 90% of the data samples to train and the remaining 10% to test our models.model nesting.For each classification algorithm , we learn three distinct predictive models in which we cumulatively add the different sets of features: (1) we only consider match metadata ; (2) we additionally take into account the action features ; and finally (3) we add the features related to performance . This procedure is commonly called AUC=0.976±0.001), followed by RF (AUC=0.968±0.001 over 512 different decision trees), and AB (AUC=0.914±0.002). The most significant features, whose Gini index (i.e. a score indicating the relevance of each specific feature in the prediction task) is reported in session win rate (feature importance=0.163), current win rate (feature importance=0.286) and match (feature importance=0.087). The importance of the match index in the session, which is an indicator of how much time players have already spent in the game, in predicting behaviour suggests that people have a finite budget—whether of time or cognitive resources—for game play. At the same time, the overall team performance (current and session win rate) also decreases during the session. The perception of decreasing win rate, combined with exhaustion of a finite budget, may lead to the player’s decision to quit the game.In the first prediction task , the best performance is obtained by model 3 where all the 22 features are used (i.e. MATCH + ACTIONS + PERFORMANCE). As shown in accuracy=0.960±0.001), followed by GB (accuracy=0.957±0.001) and AB (accuracy=0.878±0.003). Consistently with the results provided in the first prediction task, the features identified by the RF classifier as most predictive are: match (feature importance=0.364), current win rate (feature importance=0.335) and session win rate (feature importance=0.111).In the second prediction task , the highest accuracy is again achieved by model 3 (MATCH + ACTIONS + PERFORMANCE). The best results, shown in 4.4.1.et al. [Various recent studies explored human performance and activity in online games. Several authors investigated aspects of team performance ,4,5,16, et al. , an exteOther aspects of social and group phenomena in virtual environments were covered in the review by Sivunen & Hakonen . In thiset al. [The ‘optimal’ composition of temporary teams also attracted a lot of research: Kim et al. ,5 studieet al.  once agaet al. [Finally, the topic of individual performance in online games has been studied in different platforms. Shen et al.  suggesteet al.  and showet al. [Other works looking at individual performance analyse first-person shooter games: Microsoft researchers studied the performance trajectories of Halo players, as well as the effect that taking prolonged breaks from playing has on their skills . Analysiet al.  studied To the best of our knowledge, ours is the first study to focus on individual performance within temporary teams, to analyse the effect of performance deterioration over the short term, and to determine its interplay with engagement.4.2.Video-games represent a natural setting to study human behaviour. Prior to this study, several works have been devoted to analysing the behaviour and activity of players in multiplayer games. In particular, behavioural dynamics of team-based online games have been extensively studied in role-playing games like World of Warcraft ,27, in bThe earlier studies focused on massively multiplayer online games like World of Warcraft, which exhibit both a strong component of individual game-play (e.g. solo quests aimed at increasing one’s character level and skills) as well as collaborative instances (e.g. raid bosses). First Nardi & Harris , and Baret al. [Later studies focused on MOBAs: Kuo et al. ,28 inveset al. ,30. Concet al. .Despite the fact that our work does not focus on the analysis of engagement in team-based online games, the results we found could be leveraged to design incentives to increase players’ engagement over time and used to prevent players from quitting the game.4.3.vigilance tasks—i.e. tasks which require monitoring visual displays or auditory systems for infrequent signals—performance was shown to decrease over time, with concomitant increases in perceived mental effort [Performance deterioration following a period of sustained engagement has been demonstrated in a variety of contexts, such as student performance , drivingl effort . For exal effort .Factors leading to a deteriorating performance are still debated –40. HoweMoreover, mental fatigue is detrimental to individuals’ judgements and decisions, including those of experts—e.g. judges are more likely to deny a prisoner’s request as they advance through the sequence of cases without breaks on a given day , and eviShort-term deterioration of individual performance was previously observed in other online platforms. It has been shown that the quality of comments posted by users on Reddit social platform , the ans5.In this paper, we addressed four research questions concerned with modelling individual performance within temporary teams. To this aim, we studied players of a team-based online game, League of Legends, and measured performance at the level of the team, as the fraction of matches the player’s team won, and at the individual level, by computing the KDA ratio of the player at the end of each match. In the long term, we observed that there is no evident performance  improvement with experience and that both measures of performance are around their mean value. This observation might be linked to the game design: the team composition balancing strategy limits individual performance variance and thus reduces individual contributions to their team performance.In the short term, i.e. over the course of a single game-playing session, our performance measures showed a strong deterioration pattern: the longer a player’s session is the more performance decreases, with metrics decreasing on average by 8–10% between the beginning and end of a session. Our findings are consistent with observations made on different online platforms and social networks, where performance deterioration was observed over the course of sessions. We found, however, that experience modulates short-term performance changes, potentially reducing the effects of performance depletion. Player experience  appeared indeed to mitigate some of the effects of performance deterioration: the more experienced players showed less performance decline over the course of a game session than the less experienced ones. Other factors that were not investigated in the present work can influence performance in team-based games: the presence of friends in the team could trigger higher collaborative behaviour, players’ performance in the MOBA game can be also affected by the role the players are impersonating, and the composition of the team can have an effect on players decisions during the game.We have shown, through the analysis of performance in the short term, that players tend to quit the game session after a certain number of matches in which their performance declines. We also investigated the factors that are predictive of a player quitting a game session. To this aim, we designed a prediction task in which we defined three sets of features. Each of these sets describes a specific aspect of the game. We took into account features related to matches, players’ actions and performance. We found that the features that best predict whether the player will quit the session are those associated with the match histories . These findings are consistent with the hypothesis that players have a finite ‘cognitive budget’ for playing, which they deplete with game-play. While our work does not address the origins of depletion—whether through growing boredom or cognitive fatigue—we have shown that this phenomenon has different effects on experienced and inexperienced players.individualized incentive strategies could be designed to identify different classes of performers, and reward them dynamically and differently based on personalized, relative assessments of performance. This would allow to overcome the issues related to long-term performance and game design, by guaranteeing a satisfactory game experience for both experienced and inexperienced players. Moreover, incentives that enhance players’ engagement in the game could be used in combination with our predictions to prevent a player’s choice to quit the session, or frustration that may drive them to quit the game. Our future efforts will thus be devoted to further the research in the science of individualized incentives.By leveraging our findings,


PR genes 1 (NPR1) protein. Recent studies demonstrate the ability of the circadian clock to predict various potential attackers, and of redox signaling to determine appropriate defense against pathogen infection. Interaction of the circadian clock with redox rhythm promotes the balance between immunity and growth. We review here a variety of recent evidence for the intricate relationship between circadian clock and plant immune response, with a focus on the roles of redox rhythm and NPR1 in the circadian clock and plant immunity.The circadian clock in plants synchronizes biological processes that display cyclic 24-h oscillation based on metabolic and physiological reactions. This clock is a precise timekeeping system, that helps anticipate diurnal changes; e.g., expression levels of clock-related genes move in synchrony with changes in pathogen infection and help prepare appropriate defense responses in advance. Salicylic acid (SA) is a plant hormone and immune signal involved in systemic acquired resistance (SAR)-mediated defense responses. SA signaling induces cellular redox changes, and degradation and rhythmic nuclear translocation of the non-expresser of  Arabidopsis are associated with physiological rhythms driven by the circadian clock .,74.CCA1 CCA1-ox)  47]. Re. ReCCA1  mutants  75]; th; thCCA1 response . DefenseCCA1, LHY, and TOC1 form the core loop of transcription-translation feedback loops that regulate daily timekeeping. CCA1 and LHY have transcript and protein levels that peak in the morning, TOC1 peaks in early evening and acts as a repressor that directly binds to CCA1 and LHY promoters. ~40% of TOC1 target genes have an early morning phase regulated by the circadian clock. LHY and CCA1 have both been shown to regulate TOC1 negatively. (2) CCA1 and LHY activate expression of PRR family genes through direct association with their promoters. PRR family genes also suppress CCA1 and LHY expression by binding to their promoters. (3) CCA1 and LHY regulate GRP7, a key constituent of a slave oscillator that modulates stomatal activity related to plant immune response, is involved in regulation of plant immunity. Stomatal opening at dawn and closure at noon. (4) Morning-phased LHY and CCA1 positively regulate resistance against oomycete pathogen Hpa and bacterial pathogen P. syringae, whereas evening-phased TOC1 negatively regulates resistance against bacteria. Spore formation of oomycete pathogen occurs mainly at night, and spores, therefore, germinate at dawn. (5) Maximal JA accumulation occurs around midday, whereas SA peaks around midnight, cytosolic NPR1 suppressed JA signaling. (6) NPR1 plays essential roles in binding of SA. NPR1 oligomers become monomers under pathogen infection, and this process triggers SA accumulation. In the absence of SA or pathogen challenge, NPR1 is degraded by the proteasome and its inhibitory effect on effector-triggered cell death and anti-pathogen defense is eliminated. (7) Under anti-pathogen defense response or SA induction, NPR1 is converted into monomers, which can be combined with TGA2 transcription factor to promote PR gene expression, and are released (transferred) from cytoplasmic sites to the nucleus. GSNO facilitates Cys156 disulfide bonding and promotes NPR1 oligomer formation. Both TRX-3h and TRX-5h catalyze monomerization of NPR1 and prevent its repolymerization.(1) CCA1, overexpression of which enhances resistance in Arabidopsis defense response . In. In33]. 8.31 nM) ,88,111. 8.31 nM) . NPR1 ma8.31 nM) . Surpris8.31 nM) .These findings, taken together, demonstrate the essential role of NPR1 as a receptor of SA, and also negative NPR3 and NPR4 controlling protein levels of NPR1 in plant immunity .Alternaria solani and Botrytis cinerea are infected, the SA pathway will be manipulated through NPR1 and two JA-dependent defense genes expression including PROTEINASE INHIBITORS I and II will be suppressed for disease symptom development [NPR1 as a key regulatory factor in the cross-talk between SA and JA signaling. Interaction between NPR1 and transcription factor TGA1 roles in the SA signaling, nuclear localization of NPR1 is not required for a plant defense network between SA and JA signaling, and therefore, the network is modulated by the NPR1 stays in cytosol . Once thelopment .In brief, both JA and SA are of vital importance in plant immunity and thus coordinates the plant defense as well as growth regulation through various mechanisms.PR1 transcription by combining with TGA factors following the transfer to the nucleus [NPR1 is a key regulator of the SA-signaling network. SA induces degradation of NPR1, and NPR1 activates  nucleus , as detaNPR1 shows defects in SA-mediated pathogen resistance on Arabidopsis mutants when it was first discovered; studies during the past two decades have clarified the role of NPR1 as a key transcriptional regulator affecting a wide variety of host genes involved in immune responses and biocompatibility [A. thaliana. The conserved function of NPR1 has also been demonstrated in crop species such as rice, wheat, tomato, and apple [Arabidopsis NPR1 (AtNPR1) homologues [Plant evolution involves continuous improvement and sophistication of immune systems. As has been talked above (2.2), tibility ,109,114.nd apple . Overexpmologues  in thesemologues ,118,119.PR genes is required for binding activity of NPR1 and TGA transcription factors to promoter elements [PR gene promoter begins transcription and expression to initiate hypersensitive responses. NPR1 clearly functions as a key positive regulator in SA signaling, as well as a negative regulator in JA signaling [Two-hybrid screening studies of a yeast model have shown that NPR1 interacts with TGA transcription factors through its ankyrin repeat domain , and alselements ,122. TGAelements ,123,124.ignaling .PR gene expression, and are released (transferred) from cytoplasmic sites to the nucleus :LUC). LHY expression peaked at dawn, while TOC1 expression was delayed by SA treatment, because LHY is an antagonist of TOC1 in the clock. NPR1 will strengthen the circadian clock when the redox rhythm is disrupted by challenges, through regulation of both morning-phased LHY and evening-phased TOC1 [NPR1 monomer level has recently been shown to peak at night, suggesting that rhythmic translocation of NPR1 to nucleus, controlled by rhythmic oscillation of endogenous SA, may regulate circadian clock genes. NPR1 antagonistics both SA and t plants . NPR1 maenous SA . NPR1 isnduction . Reductinduction ,129. Othsed TOC1 .Neurospora crassa suggested regulation of cellular redox state by the circadian clock [Arabidopsis [Redox rhythm is closely associated with plant immunity, and both are related to the circadian clock. Early studies of the fungus an clock . Daily tbidopsis ,3,4. Nic2O2) and reactive nitrogen species  have closely related roles in plant immunity [The alteration of cellular redox state is one of the earliest occurring responses in a challenged cell . ROS  involved in plant defense, Alvina et. al., pointed that CCA1 is a master regulator of ROS homeostasis  is reflected in oscillations of both oxidation state of ROS scavenger peroxiredoxins and NADP/NADPH levels, both of which lack transcription/translation activities ,96. Redou et al.  found thTOC1 in an NPR1-dependent manner [TOC1. NPR1 monomer level peaks at night and displays a circadian oscillation pattern. Basal expression of TOC1 was reduced in both an NPR1 mutant and in an SA mutant [TOC1 expression in the SA mutant but not in the NPR1 mutant, regardless of treatment duration [Because the redox rhythm is regulated by NPR1, circadian redox rhythm in plants is coupled with a genetic clock. This coupling is mediated by cellular redox changes induced by endogenous SA level, which induce degradation and rhythmic nuclear translocation of NPR1 ,70. Endot manner . NPR1 isA mutant . Supplemduration .Arabidopsis, stomatal closure is induced by NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOGUE D (RBOHD) [Redox signaling in plants helps determine the most suitable defense against pathogen infection, and the circadian clock helps predict various potential/ imminent attackers ,141,142. (RBOHD) ,144. SA  (RBOHD) . Express (RBOHD) ,148,149. (RBOHD) ,151,152. (RBOHD) .Reduced glutathione (GSH) and oxidized glutathione (GSSG) form an essential antioxidant buffer system. Reduction of the GSH/GSSG ratio during pathogen infection leads to increased oxidative stress. NPR1 reduction does not lead to an immediate increase of the GSH/GSSG ratio . GSH is,+ and altered their proportions, thereby triggering expression of defense-related genes. ROS have a toxic  effect on invading bacteria, but can also injure host cells. NPR1 may be the core element that links together the circadian clock and redox rhythm. Plant defense systems are associated with the circadian clock for regulation of daily physical activity via a complex network system. Defense responses create an environment conducive to plant survival, but often lead to growth inhibition, thus setting up a perpetual competition for resource allocation. Our understanding of plant immunity and the circadian clock has undergone major, sequential progress during the past decade, and research in both areas is well advanced. On the other hand, SA is the last of the major plant hormones whose receptors have an intricate control of SA responses. Future studies will further elucidate the mechanism of how SA receptors regulate plant defense responses and the core elements that coordinate the circadian clock with plant immunity.We have summarized here a variety of evidence for connections between circadian clock, plant immunity, and redox rhythm. Cellular redox state is regulated by circadian clock. SA treatment of plants disrupted oscillations of NADPH/ NADP


Foxp1 leads to an increase of brown adipose activity and browning program of white adipose tissues. The Foxp1-deficient mice show an augmented energy expenditure and are protected from diet-induced obesity and insulin resistance. Consistently, overexpression of Foxp1 in adipocytes impairs adaptive thermogenesis and promotes diet-induced obesity. A robust change in abundance of the β3-adrenergic receptor (β3-AR) is observed in brown/beige adipocytes from both lines of mice. Molecularly, Foxp1 directly represses β3-AR transcription and regulates its desensitization behavior. Taken together, our findings reveal Foxp1 as a master transcriptional repressor of brown/beige adipocyte differentiation and thermogenesis, and provide an important clue for its targeting and treatment of obesity.β-Adrenergic receptor (β-AR) signaling is a pathway controlling adaptive thermogenesis in brown or beige adipocytes. Here we investigate the biological roles of the transcription factor Foxp1 in brown/beige adipocyte differentiation and thermogenesis. Adipose-specific deletion of  Beta3-adrenergic receptor (b3-AR) signaling in response to cold activates adipose tissue thermogenesis. Here the authors identify the transcription factor FoxP1 as a direct negative regulator of b3-AR expression and show that loss of FoxP1 leads to enhanced development of thermogenic adipose tissue. WAT acts to store energy in the form of triglycerides, whereas BAT and beige adipose tissues dissipate heat against cold or excessive diet3. Brown adipocytes uniquely express uncoupling protein-1 (UCP1), which is the functional marker of brown adipocytes and fuels oxidation and heat generation in the inner membrane of mitochondria. Brown adipocytes originate from Myf5+ mesodermal progenitor cells, common progenitors of skeletal muscle4. Beige and brown adipocytes share similar morphological features as well as expression of UCP1. Both beige and brown adipocytes can be induced within WAT depots following cold exposure or β-adrenergic stimulation8. These processes are termed browning of white adipocytes. In the context of the global obesity epidemic, mechanisms that activate brown or beige adipocytes may have significant clinical implications for metabolic disorders9.Energy homeostasis in mammals mainly is controlled by energy intake and expenditure. There are three types of adipose tissues: white adipose tissues (WAT), brown adipose tissues (BAT), and brown-like (beige) adipose tissue. Each are composed of morphologically and anatomically distinct adipocyte populations10. Upon cold exposure, SNS releases adrenergic signals, such as noradrenaline or catecholamines, which activate the downstream pathway through binding adrenergic receptors in adipose tissues. A variable portfolio of α/β adrenergic receptors is then assembled within various mammalian tissues. Compared to the β1/2 adrenergic receptors (β1/2-AR), the β3 adrenergic receptor (β3-AR) is relatively enriched in adipose tissues required for cold or diet-induced thermogenesis12. Adrenergic signaling triggers energy expenditure chronically via BAT thermogenesis14, and acutely via WAT fueling16. β3-AR is primarily responsible for beige adipocyte induction in pre-existing white adipocytes under adrenergic agonists stimulation, whereas β1-AR mediates beige adipocyte generation after cold exposure17. The role of β3-AR agonists in beige induction and activation has also been demonstrated in human adipose tissues18. Consequently, β3-AR agonists can activate both typical brown and beige adipocytes through SNS, and thus, provide promising opportunities for anti-obesity treatments19.Activated brown or beige adipocytes are induced to dissipate heat as adaptive thermogenesis under sympathetic nervous system (SNS) controlβ3-AR gene may be more susceptible to obesity22. Consistent with this finding, mice devoid of the β3-AR gene are prone to deposit more fat than control mice23. In ob/ob mice, β3-AR expression is dramatically impaired in adipocytes24. In fact, β3-AR has a unique expression dynamic in adipocytes termed desensitization. That is, β3-AR displays a short-term decline in mRNA abundance upon exposure to β3-AR agonists28. This is distinct from the typical β2-AR desensitization pathway observed in cardiomyocytes29, which is cycled with β2-AR protein between cell membrane and endosome through β-Arrestin protein. The biological significance of β3-AR desensitization still is not fully recognized, and it remains unclear how β3-AR is transcriptionally regulated. Yet, these questions are absolutely critical in evaluating the role of β3-AR in obesity treatment.Both the abundance and activity of β3-AR in adipocytes are tightly regulated. For instance, the subjects carrying a Trp64Arg mutation within the 33. The Prdm16-C/ebpβ complex functions as a switch to determine the thermogenic program of brown/beige adipocytes35. On the other hand, Twist1 and Rip140 act to arrest BAT thermogenesis by repressing PGC-1α activity37. Foxhead P1 (Foxp1) typically acts as a transcriptional repressor in a variety of developmental pathways, including cardiomyocyte proliferation39, lung development41, lymphocyte differentiation43, glucose homeostasis44, endochondral ossification45, and neuronal morphogenesis48. A recent study from our group further reveals an important role for Foxp1 in mesenchymal stem cell senescence49. In this study, we identify Foxp1 as a crucial component of the thermogenic program, which arrests brown/beige differentiation and thermogenesis through regulation of β3-AR transcription in adipocytes.Brown/beige adipocyte differentiation and activation is controlled by sequential actions of transcription factors, including Ebf2, Prdm16, C/ebpβ, PGC-1α and PPARγ50, we detected primarily isoforms B and D in BAT, and isoforms A and B in WAT via western blotting analyses  was progressively upregulated  cells isolated from BAT of wild type mice were induced to brown adipocyte differentiation in vitro. After one day of induction, the elevated ted Fig. . SimilarFoxp1 expression progressively inclined, whereas β3-AR expression inversely declined  overnight, Foxp1 expression in BAT was upregulated Fig. . At the ned Fig. . Consisttro Fig. . These o54. To test if either of these kinases mediates induction of Foxp1 by the β3-AR agonist, we first evaluated the dynamics of Foxp1 expression in an 8-h course of adipogenic cultures of 3T3-L1 cells. Briefly, the cells were stimulated with CL-316,243 alone or in combination with SB202190 (p38 kinase inhibitor), FR180204 or SCH772984 (Erk1/2 inhibitors). As shown in Fig. Foxp1 transcripts by the β3-AR agonist was markedly arrested by FR180204 and SCH772984, whereas it was exaggerated by SB202190. This suggested that Erk1/2 is necessary for Foxp1 induction by adrenergic stimulation. These results were further validated by western blotting following exposure of brown adipocytes to CL-316,243 and SCH772984  and mitochondrial  genes in Foxp1Myf5∆/∆ BAT   Fig. , whereasWAT Fig. .Foxp1 in adipocytes by aP2-Cre, which was also extensively utilized for adipose-specific knockout models56. Foxp1 was efficiently reduced at the mRNA and protein levels in BAT and sWAT from Foxp1aP2∆/∆ mice  and mitochondrial  transcripts were significantly upregulated in cultured brown and beige adipocytes  cells were isolated from BAT or sWAT depots of Foxp1Ad∆/∆ and Foxp1aP2∆/∆ mice exhibited higher rates of oxygen consumption and carbon dioxide production, expended more energy . Foxp1Ad∆/∆ mice appeared leaner in body, with smaller in adipose depots  and insulin tolerance test (ITT) scores .To examine the long-term effect of ots Fig. , and gaiots Fig. . The redots Fig. . Meanwhiols Fig. . Foxp1Adres Fig. . Foxp1aPFoxp1 on energy expenditure, we generated aP2-Foxp1 transgenic mice in which Foxp1 cDNA overexpression was driven by a 5.4 kb Fabp4 (aP2) gene promoter/enhancer cassette. Overexpression of Foxp1 was validated by qPCR and western blotting of four independent founders  reporter mouse57, as a tool to detect adaptive thermogenesis in brown and beige adipose tissues under cold exposure or adrenergic stimulation. Two representative strains of aP2-Foxp1;Ucp1-Luc transgenic mice displayed lower luciferase activities in BAT and sWAT compared to Ucp1-Luc controls when challenged with 4 °C cold exposure for 6 h  was down-regulated within the BAT of aP2-Foxp1 mice relative to controls. Next, we employed qPCR to validate the expression profiles of several marker genes in the BAT of transgenic mice. A significant decrease of β3-AR expression was observed among a broad panel of thermogenic genes  was detected at −2251 bp upstream of the β3-AR promoter  assays that Foxp1 interacted with C/ebpβ and Prdm16 in SVF cells isolated from BAT  patients, WAT browning was detected as a result of severe adrenergic stress under continuously high levels of catecholamines, which disrupted the energy balance in adipose tissues to result in weight loss51. To restrain this overactivation of BAT thermogenic program, a number of transcriptional co-repressors has been employed to counteract the activity of PGC-1α, e.g. Rip 140 and Twist137, or to repress transcription of Ucp1, e.g. Rb160 and LXRα61. In this study, we examined the influence of Foxp1 on thermogenesis in adipocytes from knockout mice employing Myf5-Cre, aP2-Cre and Adiponectin-Cre, as well as by transgenic mice with Foxp1 overexpression in adipocytes. Our analyses identify Foxp1 as a repressor for brown/beige adipocyte differentiation and energy expenditure. Luciferase reporter suggested that Foxp1B, the isoform that was expressed in BAT and sWAT, mostly accounted for the repressive function in BAT activation and thermogenesis  in Foxp1aP2∆/∆ WAT , Adiponectin-Cre (Stock no. 028020), Thermo Mouse (Stock no. 026690) were obtained from Jax lab. Myf5-Cre mice was kindly provided by Prof. Dahai Zhu in Peking Union Medical College. Transgenic vector containing 5.4 kb promoter of Fabp4 (aP2) gene were kindly provided by Prof. Qiqun Tang in Fudan University. The genetic backgrounds of all knockout mice were C57Bl/6J and the background of transgenic mice were 129S1/Sv. Mice were bred with standard rodent chow food or HFD. Male mice were used in the experiments unless otherwise indicated. The experiments were not randomized, and the investigators were not blinded to allocation during experiments or outcome assessments. All animal experiments were performed according to the guidelines (SYXK 2011-0112) and received ethical approval from committee of Bio-X Institutes of Shanghai Jiao Tong University.The Human browning omental adipose tissue for immunohistochemistry was obtained from clinically and pathologically diagnosed patients with pheochromocytoma and sex-, age-, and BMI-matched control subjects. Three samples were analyzed with immunofluorescence or IHC in this study. Primary human stromal vascular fraction (SVF) from subcutaneous WAT (sWAT) for the beige adipocyte induction were obtained from underaged subjects. The adipose tissue pieces were obtained during surgical procedures followed by immediate storage in liquid nitrogen and fixation in formalin. The human study was performed in accordance with relevant guidelines and received ethical approval from the Institutional Review Board of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Written informed consent was provided from each participant prior to inclusion in the study.2 consumption, CO2 production and physical activity were measured by using indirect calorimetry system , installed under a constant environmental temperature (22 °C) and a 12-h light (07:00–19:00 h), 12-h dark cycle (19:00–07:00 h). Mice in each chamber had free access to food and water. The raw data were normalized by body weight and the histograms of day (07:00–19:00 h) and night (19:00–07:00 h) values were the mean value of all points measured during the 12-h period. Bioluminescence analysis for ThermoMouse is performed using Lumina III . An infrared camera  is placed over top of the anesthetized mouse to acquire static image according to standard protocols.Minispec TD-NMR Analysers (Bruker Instruments) were used to evaluate adiposity composition on anesthetized animals. Food intake, energy expenditure, OFor GTT, mice were given i.p. injection of 100 mg/ml D-glucose (2 g/kg body weight) after overnight fasting, and tail blood glucose concentrations were measured by a glucometer . For ITT, mice were fasted for 4 h before i.p. administration of human insulin (Santa Cruz) (0.75U/kg body weight), and tail blood glucose concentrations were monitored.Adipose tissues were fixed in 4% PFA for 24 h at 4 °C, embedded in paraffin or tissue freezing medium (Leica) and sectioned to 8 μm. HE staining was conducted according to standard protocols. For immunofluorescence, heat-induced antigen retrieval with sodium citrate buffer  was performed before bone sections were blocked with 10% normal serum containing 1% BSA in TBST (pH 7.6) for 2 h at room temperature, then incubated overnight at 4 °C with primary antibodies to mouse Foxp1 , Ucp1 , Prdm16 . Subsequently, sections were incubated with secondary fluorescent-conjugated or HRP-conjugated antibodies at room temperature for 2 h in the dark. Samples were imaged by the Leica TCS SP5 confocal microscope, Leica DM2500, or Leica 3000B microscope. Transmission electron microscopy (TEM) of white and brown adipose tissue was carried out in accordance with a previous study.−1 collagenase type I (Sigma) in DMEM (Invitrogen) supplemented with 1% bovine serum albumin for 25 min at 37 °C, followed by density separation. The digestions were quenched with DMEM containing 10% FBS, and filtered through 70-mm filters to remove connective tissues and undigested trunks of tissues. Cells were then centrifuged at 1000 × g for 5 min to separate the SVF cells in the floating layer. The freshly isolated SVF cells were seeded and cultured in growth medium containing DMEM, 20% FBS, 1% penicillin/streptomycin (P/S) at 37 °C with 5% CO2 for 3 days, followed by feeding with fresh medium every 2 days to reach confluence. For brown adipocyte differentiation, the cells were induced with induction medium contains DMEM, 10% FBS, 5 μg ml−1 insulin, 0.5 mM isobutylmethylxanthine (Sigma), 1 μM dexamethasone (Sigma), 50 nM T3 (Sigma) and 5 μM troglitazone (Sigma) for 48 h, and further in growth medium supplemented with insulin, T3 and troglitazone for six days followed by 0.5 mM cyclic AMP (Sigma) treatment for another 4 h. 3T3-L1 (ATCC) and HEK293T (ATCC) were cultured in DMEM with 10% FBS. For 3T3-L1 adipogenic differentiation, cells of 100% confluence were kept in growth medium for 2 days then induced with induction medium for 2 days, after that differentiated in differentiation medium (without T3) for 6 days. For oil red staining, cultured cells were washed with PBS and fixed with 10% formaldehyde for 15 min at room temperature. Then the cells were stained using the Oil red O working solutions (5 g/l in isopropanol) and 4 ml ddH2O for 30 min. After staining, the cells were washed with 60% isopropanol and pictured.For SVF cell isolation, primary BAT and sWAT were digested with 1 mg ml2. The OCR was measured using the XF Extracellular Flux Analyser (Seahorse Biosciences). Oligomycin (2 mM), FCCP (2 mM), and Antimycin A (0.5 mM) were preloaded into cartridges and injected into XF wells in succession. OCR was calculated as a function of time (pmoles per minute per μg protein).Primary SVF cells from BAT and sWAT were isolated and cultured for 3 days before being plated in XF cell culture microplates (Seahorse Bioscience). SVF cells  were seeded in each well, and each treatment included cells from three BAT or sWAT replicates. After 6-day differentiation, cultured adipocytes were washed twice and pre-incubated in XF medium  for 1–2 h at 37 °C without COg for 5 min, followed by filtered with MILLEX GP. The oligos for shRNA construct were listed in Supplementary Table To construct shRNA for Foxp1-shRNA or β3-AR shRNA virus, the forward and reverse oligos for each shRNA were mixed and denatured at 95 °C for 5 min, annealed at 58 °C for 10 min, and then digested by XbaI and BamHI before cloning into lentivirus-based vector pLenti-shRNA-NF1-GFP. The lentivirus were packaged in 293T cells with helper vector pMD2G and psPAX2 by transfecting cells with indicated constructs. After 48-h culture, supernatant was collected by centrifuge at 1000 × 70. Briefly, 2–5 million cells were cross-linked with 1% formaldehyde (Sigma-Aldrich) for 10 min at room temperature, and then incubated with 125 mM glycine for 5 min to quench the cross-linking reaction. The harvested cells were sonicated on ice to generate DNA fragments of 200–500 bp. The fragmented chromatin fragments were immunoprecipitated with 20 μl of protein A/G magnetic beads (Millipore) coupled with 4 μg of anti-Foxp1 antibody  at 4 °C overnight with rotation. ChIP DNA was treated with protein K (Thermo Fisher) at 55 °C for 6.5 h for decrosslinking. Both ChIP and input DNA libraries were generated using the NEBNext Ultra II DNA Library Prep Kit for Illumina . The DNA libraries were amplified for 18 cycles and subjected to sequencing by Illumina sequencer. For ChIP-PCR, purified DNA was quantified using quantitative PCR. The primer sequences are listed in Supplementary Table SVF cells were isolated from mouse BAT or sWAT and expanded in passaging cultures for 2 weeks. The ChIP samples were prepared as followingFor in vitro co-immunoprecipitation (Co-IP), His- or FLAG-tagged proteins were produced in HEK293T transfected by FuGENE with corresponding plasmids. For in vivo Co-IP, SVF cells were isolated and cultured from wild-type mice. Total cell lysates were incubated overnight at 4 °C with antibodies or normal IgG (Santa Cruz) as control. Antibody-antigen complexes were absorbed by Protein A/G PLUS-Agarose . After several washes, samples were boiled and analyzed by western blotting.Total RNA was extracted with Trizol (Invitrogen), reverse transcriptase was used for cDNA generation with the GoScript reverse transcription system (Promega). qPCR was performed with a real time PCR system (ABI 7500) using SYBR Green (Roche). The primer sequences are listed in Supplementary Table PPARγ-Luc containing a 2.2 kb fragment of the 5’ flanking region of the PPARγ gene, was obtained from Dr. Hiroshi Takayanagi of the Tokyo Medical and Dental University. Adrb3-Luc plasmid containing 4.3 kb of the 5’ flanking region of Adrb3 gene, was constructed by our lab. The expression plasmids of Foxp1 and Cebpβ were constructed into the pcDNA3.0 vector. Cells were transfected using FuGENE HD (Promega) in 24-well plates. The transfection amount of each plasmid was 200 ng, and the total amount of transfected DNA across each transfection was balanced by pcDNA3.0 plasmids when necessary. After 32 h, dual luciferase assay was performed according to the manufacturer’s protocols (Promega).Luciferase assays were performed in HEK293T or 3T3-L1 cells. The reporter plasmid, t-tests for comparisons between two groups. For all experiments, P ≥ 0.05 were marked as ns, P < 0.05 were considered significant and indicated by *P < 0.01 were indicated by **P < 0.001 were indicated by ***.All data are presented as mean ± SEM. Error bars are SEM. Two-tailed Student’s Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


In quantum theory, the retrodiction problem is not as clear as its classical counterpart because of the uncertainty principle of quantum mechanics. In classical physics, the measurement outcomes of the present state can be used directly for predicting the future events and inferring the past events which is known as retrodiction. However, as a probabilistic theory, quantum-mechanical retrodiction is a nontrivial problem that has been investigated for a long time, of which the Mean King Problem is one of the most extensively studied issues. Here, we present the first experimental test of a variant of the Mean King Problem, which has a more stringent regulation and is termed “Tracking the King.” We demonstrate that Alice, by harnessing the shared entanglement and controlled-not gate, can successfully retrodict the choice of King's measurement without knowing any measurement outcome. Our results also provide a counterintuitive quantum communication to deliver information hidden in the choice of measurement. The thought experiment, named the Mean King Problem (MKP), is a quantum-mechanical retrodiction problem, which originates from the paper in 1987 by Vaidman et al.  and poweσx, σy, σz} is performed on a spin − (1/2) particle at a given time. By means of measurements carried out both before and after the time in question, they came up with a method to ascertain the result of the spin measurement, even though they do not know in which direction the spin was measured and no matter what those measured results may happen to be. The solution of this problem is intriguing because it is by no means obvious to make definite inferences about σx, σy, σz.In the original version described by Vaidman et al., a measurement chosen from {In the Mean King Problem, a physicist named Alice prepares a two prime-dimensional particle state and avails one of the particles to the King who then measures his particle by freely choosing one of the mutually unbiased bases (MUB) , 9–12. TIt seems that in the MKP the physicist can assign the state of the system in the past. However, with the knowledge of the King's choice of measurement, this state retrodiction is actually conditional. What if the King does not tell Alice his choice? This question leads to another extension of the MKP termed “Tracking the King Problem” (TKP). In this new problem, Alice is not informed by the King's choice (nor the outcome). Inversely, her control measurement is designed to track the basis that the King used. The interesting thing is, in TKP, the King's choice of measurement (without recording the outcome) can be viewed as a novel quantum communication , 14 signThe counterintuitive part of this problem is that in classical physics, measurements without outcomes being recorded carry no information and hence cannot be used for communication. However, such a measurement in quantum mechanics causes traceable disturbance to the measured system so that Alice's control measurement can retrieve the King's choice. Until recently, this thought experiment has not been realized yet.In this work, we experimentally demonstrate the TKP of the 2-dimension case in an optical system. By sharing maximally entangled photon pairs and employing a controlled-not gate (C-NOT) as Alice's control measurement, we show that it is possible for Alice to retrodict the choice of King's measurement without knowing any measurement outcome. The realization of this thought experiment unravels the intrinsic characteristics of quantum measurement and deepens our understanding of quantum theory. Furthermore, the choices of measurements can also be seen as the delivered messages, which put it forward to realize a counterintuitive quantum communication.d = 2; ω = i; c, r; s = 0, 1, ⋯, d − 1; and |n〉 = |n + d〉 for any n. Superscript s labels a basis for Hilbert space of the two qubits, while c, r label the d2 orthonormal states within the basis. For example, s = c = r = 0 represents the state of The scheme of the TKP is shown in 3O5 (LBO) crystal by frequency doubling and is focused on the 2 mm thick type-II degenerate noncollinear cut beta-barium borate (BBO) crystal as a pump light [H(V) represents horizontal  polarization of the qubit.The schematic view of the experimental setup is shown in mp light . The comZZ and obtain an average fidelity of the C-NOT gate up to 0.827. More details about the implementation and optimization of C-NOT gate can be found in Methods and D, A} for the control arm and in the basis {H, V} for the target arm. The results shown in One of the entangled photons (P1) is stored with a fixed time delay by being coupled into a single-mode fiber. The other one (P2) is sent to the King's station, where a standard single-qubit analyzer consisting of two quarter-wave plates (QWPs) and a polarizer (POL) serves as the required nonselective measurement  1/3 (2/3) V components. The two incident photons are ensured to perfectly interfere on the PPBS-I to erase their which-way information. We optimize their spatial and temporal overlap by observing the Hong-Ou-Mandel interference with an injected identical polarization of H. ZZ and obtain an average fidelity of the C-NOT gate up to 0.827. Take it specifically, if we inject the V into the control arm, the target bit will keep its original state. Otherwise, if we inject H into the control arm, the target bit will flip, which we denote using red bars in ϕ+〉 will come out with the result D in the control arm and V in the target arm  and two supplemental PPBS-IIs , 20. The arm see .d + 1 MUBs as his nonselective measurement on the qubit he received. His choice is labeled by b = 0, 1 ⋯ , d − 1. Note that in this scheme the King's measurement outcome is completely irrelevant, and he sends back the collapsed qubit to Alice without recording any results afterwards. Now the two-qubit state can be expressed as follow:Alice keeps qubit 1 (labeled by the subscript 1) and sends qubit 2 to the King. Then the King chooses one of the b according to the following decoding table:At last, Alice measures the two-qubit system in the basis denoted by


The role of forkhead-box A1 (FOXA1) and Androgen receptor (AR) in breast cancer (BC) has been extensively studied. However, the prognostic role of their co-expression in Estrogen receptor positive (ER+) BC has not been investigated so far. The aim of the present study was thus to assess the co-expression (protein and mRNA) of FOXA1 and AR in BC patients, in order to evaluate their prognostic impact according to ER status.Immunohistochemical expression of AR and FOXA1 was evaluated on 479 consecutive BC, with complete clinical-pathological and follow up data. Fresh-frozen tissues from 65 cases were available. The expression of AR and FOXA1 with ER was validated using mRNA analyses. Survival and Cox proportional hazard analyses were used to evaluate the relationship between FOXA1, AR and prognosis.r = 0.8975; P < 0.001) and ER mRNA . In ER+ BC, FOXA1 was associated with a good prognosis independently of AR expression in the three subgroups analyzed . Multivariate analyses confirmed that FOXA1 may provide more information than AR in Disease-Free Interval (DFI) of ER+ BC patients.Expression of ER, AR and FOXA1 was observed in 78, 60 and 85% of cases respectively. Most AR+ cases (97%) were also FOXA1+. The level of FOXA1 mRNA positively correlated with level of both AR mRNA  contains supplementary material, which is available to authorized users. In breast cancer (BC), Estrogen (ER) and Androgen Receptors (AR) regulate cell proliferation and differentiation. They are frequently co-expressed, however AR may be expressed in ER-negative (ER-) BC, where it modulates gene transcription by using regulatory molecules and pathways normally activated by ER . As a reFOXA1, a member of the forkhead family protein , is an iThe aim of the present study was first to assess the co-expression, at both protein and mRNA levels, of FOXA1 and AR in BC, and then to evaluate their prognostic impact in ER+ BC patients.We collected a series of 479 female patients that underwent surgery for BC from June 1994 to December 2012 at the Breast Unit of the Città della Salute e della Scienza Hospital of Turin, Italy. All patients were treated with surgery, either mastectomy or wide local excision, followed by radiotherapy.Clinical-pathological data such as age at time of diagnosis, surgery , type of therapy , type and site of recurrences, histological types, tumor size (< 15 mm vs ≥ 15 mm), nodal involvement, histologic grade and vascular invasion were collected. Medical charts of all patients were reviewed to confirm accuracy of previously recorded data. Tumor slides were re-evaluated to select representative blocks that were used to construct multicore tissue microarrays , as previously described .To confirm the results of the diagnostic reports, immunohistochemistry (IHC) was performed on TMA sections using an automated slide processing platform  and the following primary antibodies were used: prediluted anti-ER rabbit monoclonal antibody ; prediluted anti-Progesterone receptor (PgR) rabbit monoclonal antibody ; anti-Ki67 monoclonal antibody ; anti-human c-erbB2 oncoprotein (Ventana Pathway HER-2/Neu-4B5). In addition, AR and FOXA1 expression were tested using anti-AR mouse monoclonal antibody  and prediluted anti-FOXA1 mouse monoclonal antibody . Positive and negative controls (omission of the primary antibody and IgG-matched serum) were included for each IHC run.The cut-off value for ER and PgR expression was set at 1%, as suggested by St Gallen Consensus meeting , and theTo determine the specificity of AR and FOXA1 antibodies, we compared gene expression levels (using qPCR) with IHC results. The relationship between AR and FOXA1 was validated using relative quantification mRNA analyses.qPCR for AR and FOXA1 mRNA was performed on 65 fresh-frozen BC samples .Pearson’s Chi square test and Student’s t-test were preliminary performed to compare respectively categorical and continuous variables, and to evaluate potential differences in the variable distribution among groups. Test for median and means  were performed. For more than two groups Tukey HSD post-hoc test was performed. Disease-Free Interval (DFI) was calculated from the date of surgical excision of the primary tumor to the date of first disease relapse or last check-up. Disease-specific survival (DSS) was calculated from the surgical excision date of the primary tumor to the date of BC death or last check-up , 27. SurP < 0.001)   mRNA  Fig.  and ER  and ER−/AR+ (3 cases) , we created three BC subgroups . We were unable to perform any analyses on the FOXA1−/AR+ BC since only 8 patients carried this phenotype Table . As showFinally, we investigated the relationship between FOXA1, AR and prognosis in BC patients stratified for ER expression. As shown in Fig. Multivariate analyses Table  performeWe assessed, for the first time, the expression of FOXA1 and AR in BC, evaluating their prognostic impact according to ER status. We found that (i) the expression (protein and mRNA) of FOXA1 and AR was closely related: the majority of cases expressing AR showed FOXA1 positivity, conversely, negative expression of FOXA1 correlates with very low level of AR; (ii) the expression of FOXA1 is strictly related to good outcome, and in the subgroup of patients with ER+ BC may provide more information on DFI than AR.FOXA1 is a “winged helix” transcription factor. It was demonstrated that, by interacting with histones H3 and H4, FOXA1 is responsible for opening compacted chromatin , permittIn addition, FOXA1 has been associated with recruitment of AR  and, it ChIP-seq analysis of AR, ER, and FOXA1 in BC cell lines revealed a significant level of co-occupancy between these markers, presumably due to the presence of forkhead motif found at AR and ER binding sites , 36, 37.In several studies has been demonstrated that AR expression is a favorable prognostic marker of disease outcome in ER+ BC , 6. ThisSahu, B et al. suggested that in prostate cancers FOXA1 level may contribute to select specific AR binding sites on DNA, activating different gene expression signatures . In our Our results suggest that in BC the expression of FOXA1 is directly proportional to the expression of AR. Despite that, FOXA1 is found as a superior predicting marker of recurrences compared to AR in ER+ BC patients. Therefore, FOXA1 expression evaluated by IHC on ER+ BC specimens could be considered in routine diagnosis as an additional support to oncologists in the definition of the patient prognosis.Additional file 1:Table S1. Primers for real-time PCR. Table S2. Patients’ clinical and histopathological characteristics according to FOXA1 expression. Table S3. Clinical and histopathological characteristics of BC patients according to FOXA1 and AR status. Table S4. Multiple comparisons of FOXA1 mRNA expression in tumors classified according to ER and AR status. Figure S1. Kaplan–Meier estimates of a) disease free interval and b) disease-specific survival according to FOXA1 status in 479 breast tumors. (DOCX 662 kb)


Scincus are widely known under the common name sandfish due to their ability to swim in loose, aeolian sand. Some studies report that this fascinating property of sandfish is accompanied by unique tribological properties of their skin such as ultra-low adhesion, friction and wear. The majority of these reports, however, is based on experiments conducted with a non-standard granular tribometer. Here, we characterise microscopic adhesion, friction and wear of single sandfish scales by atomic force microscopy. The analysis of frictional properties with different types of probes  demonstrates that the tribological properties of sandfish scales on the microscale are not exceptional if compared to snake scales or technical surfaces such as aluminium, Teflon, or highly oriented pyrolytic graphite.Lizards of the genus  Scincus scincus [S. scincus, however, is not only able to bury, it can also “swim” and travel reasonable distances in sand [Areas with loose, aeolian sand in the deserts of North Africa and the Arabian Peninsula are the habitat of the lizard  scincus  . This value is indeed lower than those of other technical surfaces such as aluminium (θ = 25°), steel (θ = 26°) or Teflon (θ = 35°), which were examined with the same sand and setup [Scincopus fasciatus, θ = 31°) or the Berber skink  are considerably higher than that of the sandfish [st, determined in this more classical way, was higher for sandfish  than for the shovel-nosed snake  which also does sand swimming.It is surprising that sandfishes manage to bury and swim in sand without visible wear on their skin –36–9. Th3sandfish . Nonethesandfish  sedated Eryx colubrinus) and the wedge-snouted skink (Sphenops sepsoides). Therefore, he assumed that these are the origin of the favourable frictional properties of reptiles living in a sandy environment. Klein et al. [Rechenberg  observedn et al.  speculatn et al. ,10 the cn et al. . ConsequS. scincus and observed extremely low values. They analysed the chemical composition of the scales and concluded that the low adhesion, and the resulting low friction and high abrasion resistance, is a material property caused by glycosylated β-keratins in the scales. Neutral glycans with five to nine mannose residues in sandfish scales are supposed to act as low-density spacers separating sand particles from the dense scales thereby reducing van der Waals forces [Baumgartner and co-workers –1116 mea11S. scincus) by atomic force microscopy and microtribometer experiments. Using different types of AFM probes we do not observe favourable frictional properties of sandfish scales if compared to technical surfaces with tribological relevance. Even a direct comparison with scales of various snakes does not reveal superior features. Experiments with a microtribometer, where the same types of samples were paired against a 1 mm diameter sapphire ball, confirm this observation on a much larger scale as probed by AFM. Neither adhesion nor friction coefficient of sandfish scales are found to be lower than other surfaces if measured with an AFM. Also, the wear resistance recorded with an AFM tip is not outstanding. Microtribometer experiments do neither reveal exceptional frictional properties. We, therefore, conclude that the fascinating ability to swim in sand without observable abrasion is not solely caused by the scales of sandfish. Other, at least additional, mechanisms are likely to be involved.Here, we analyse the tribological properties of single scales of sandfish . These were not tested for their content of glycans [Spalerosophis diadema cliffordii (Clifford's diadem snake) is a psammophile snake living in a sandy environment but not in sand dunes like sandfish. Echis pyramidum  lives near sandy environments while Pantherophis guttatus (Eastern corn snake) and Naja atra (Chinese cobra) live in various habitats and they are not particularly psammophile. All samples of scales were stored and measured in an environment with controlled temperature (21–23 °C) and humidity (50–70%).Moulted sandfish skin collected from kept animals was cut in small pieces or scales were singled before sample preparation . In some glycans . For comFload while recording the lateral forces acting on the tip apex. Averaging these values gives the averaged frictional force <Ffric>. The corresponding frictional coefficient μ was obtained by subsequently fitting the data with Ffric = Fad + µ·Fload.All AFM experiments were conducted with a Dimension Icon AFM . The topography of the samples was measured in tapping mode while adhesion force, friction, and wear analysis were conducted in contact mode. No extra treatment was applied to the scales before imaging. Spring constant and deflection sensitivity of all cantilevers  were determined with the thermal tune method integrated into the corresponding AFM software. Normal load and lateral force were calibrated according to the procedure described by Schwarz and co-workers . The ramCantilevers and the cross section of a sandfish dorsal scale were imaged by scanning electron microscopy . Sandfish scales were sputtered with 20 nm of silver before imaging while the probes were not sputtered in order to prevent unwanted changes of the surface properties for the adhesion measurements. Therefore, a low working distance between probes and SEM detector and a low acceleration voltage between 1 and 1.5 kV were used to enable the SEM investigation.In addition to conventional sharp silicon tips , we prepRa < 0.01 µm, determined by optical profilometry .The microtribometer experiments were performed with our custom-built reciprocating linear setup similar to the one described elsewhere . The difThe water contact angle of sandfish scales was measured with the sessile drop method using an OCA 40 system with the corresponding SCA20 software It is well-known that many parameters influence the frictional properties of surfaces. Comparable small variations in structure or chemistry may lead to drastic changes in friction or wear . We, theS. scincus. Low adhesion is a sign of low surface energy, which typically coincides with high contact angles [Some studies –11 report angles . As showt angles . NonetheFad marked in all graphs in Several different types of AFM probes were utilised to measure the adhesion force on dorsal scales. S. diadema and on surfaces of some tribological relevance  and silicon). n = 10) on 15 arbitrarily chosen positions recorded on every sample mentioned above. These measurements reveal that the averaged adhesion forces on sandfish S. scincus (67.3 nN) are a little larger than on silicon (54.2 nN), Teflon (46.1 nN) or S. diadema (41.3 nN) while adhesion on PMMA (98.2 nN) is considerably higher. Interestingly, the adhesion on HOPG (225.2 nN) is much higher, nearly 3.4-times of that of the sandfish scale. Nonetheless, the adhesion forces on sandfish scales are not found to be exceptionally low.In order to compare these values to other materials we conducted additional adhesion experiments with a sharp silicon tip on scales of S. diadema cliffordii, E. pyramidum, P. guttatus, and N. atra. For these snakes, adhesion forces on dorsal scales are smaller than on ventral ones but nearly equal to that on the head. Although S. diadema and E. pyramidum are snakes living in or near sandy environments we observe no difference to P. guttatus and N. atra, which are not psammophile. The adhesion force on scales of sandfish, however, is interestingly larger than that of all other examined snakes. This outcome demonstrates again that the adhesion of the analysed sandfish scales from S. scincus is not exceptional low as it might be assumed to explain low granular friction during sand swimming.We extended our analysis by measuring adhesion with a sand probe also on scales of four snakes . The scaFfric/Fload) of two bodies in contact. For a sandfish swimming in sand, however, there are numerous microscale contacts inducing friction without a defined load. The friction angle measurement introduced by Rechenberg [gr = tan θ) with sand but it does not allow for a classical load-vs-friction analysis. We, therefore, conducted microscopic measurements recording friction-vs-load for various probes and surfaces. Fitting a straight line to the data we obtained the friction coefficient from the gradient of this fit.Frictional properties of technical materials are generally described by the macroscopic frictional coefficient µ, which is the ratio between friction and applied load  and HOPG (0.02), respectively. The values for PMMA (0.63) and silicon (0.50) are larger than that for sandfish (0.22) and S. diadema (0.07). The microscopic frictional coefficient for sandfish scales measured with a sharp silicon tip is significantly lower than that for technical materials such as Teflon, PMMA and silicon but considerably higher than that for HOPG and a psammophile snake like S. diadema. This outcome shows that sandfish scales exhibit good but no excellent frictional behaviour at the microscale.ion loop  for eachion loop . Three dAs this outcome is different to the results obtained with the granular-friction method ,7–9 we wSandfish swim in sand grains and these particles close to the epidermis may act as a third abrasive when caught between the body and the rest of the sand, leading to a classical three-body abrasion system. Previous studies based on the granular approach indicated that sandfish skin got less damage and resisted wear much better than Teflon, glass or even steel ,7–8. We,S. diadema scales, PMMA, Teflon and aluminium. In order to provoke some wear, we increased normal load with the aim to scratch the surface of the samples. To achieve such a large normal load, we utilized cantilevers with a nominal spring constants of 40 N/m. To avoid that tip wear influences the scratching tests, we started every experiment with a fresh cantilever with a pristine tip. On each sample, we scratched nine small areas with the same size (5 µm × 5 µm), number of scan lines (128 × 128) and scan velocity (0.8 Hz) but systematically increased the load for each subsequently scanned area.Scratch resistance experiments were conducted on various samples including sandfish scales, Sver) varied for every pristine cantilever used for each sample. This effect causes a slight difference on normal load on each sample because we had to increase the loading force in voltage steps (Fload = cz·Sver·(Usetpoint − Udis)).The topography images at the top in The topography images at the bottom of S. diadema, we find that scratching depth increases almost linearly at first but finally reaches a plateau. The same result occurred in the scratching experiment with fixed normal load  bearing steel were investigated. The results of these experiments are presented in S. diadema. The friction coefficient of the sandfish scales is slightly higher. PEEK is a polymer widely used in tribological applications and it has approximately the same friction coefficient as the sandfish scales. Interestingly, some of the frictional coefficients obtained by atomic force microscopy are considerably higher as the ones recorded by microtribometry. All technical surfaces, with the exception of HOPG, exhibited considerably larger friction coefficients than the biological samples. These differences between tribological tests, conducted by AFM and microtribometry on technical surfaces, might be caused by submicron topography features present on these surfaces. Such components with a high wave vector of the power spectral density of a surface topography are most likely to have more influence on the nanoscale compared to the microscale [The comparison of friction coefficients between nanoscale and microscale experiments presented in croscale .S. diadema. Utilizing a spherical or sand probe results in the same overall outcome.We analysed the tribological properties including adhesion, friction and resistance to abrasion of sandfish scales in detail utilizing various AFM techniques and probes with different size and surface chemistry. The experimental results do not indicate superior tribological properties of sandfish scales if compared to scales of other reptiles or technical surfaces. In agreement with classical theory the adhesion forces depend mainly on the tip size (or diameter) but adhesion on sandfish scales is not extraordinary small. The frictional coefficient measured with a sharp tip on a sandfish scale is interestingly lower than that on technical materials such as Teflon, PMMA or silicon but still larger than that on a common dry lubricant such as HOPG or a psammophile snake like S. diadema in addition to three technical materials. Sandfish scales resist normal load better than most technical materials. However, they do not perform better over longer scratching periods. Consequently, tribology properties including adhesion, friction and abrasion resistance of sandfish scales are equal to the other samples investigated. In other words, it seems that sandfish scales do not feature outstanding tribological properties from the microscopic point of view. We, therefore, conclude that its scales are not the exclusive magic trick of the sandfish enabling its fabulous sand swimming.Abrasion resistance was characterised with two types of scratching experiments on scales of sandfish and the snake Reviewing literature and our recent results it now seems likely that the dynamics of the sandfish locomotion as well as the elastic properties of the epidermis are important factors and not exceptional low friction and wear of the scales alone. Consequently, it will be important to consider not only scales but also the tissue underneath the epidermis as well as the dynamics of the swimming sandfish. Such experiments might hold the key for understanding the fabulous swimming abilities combined with low wear rates. It is possible that sandfish scales are not primarily designed to lower friction  but to reduce wear in combination with the specific dynamics of sandfish. The latter is a significant technological challenge with high industrial impact that might lead to new robots which could swim through granular materials.


The objective of the study was to describe psychological features of abstinent heroin users undergoing rehabilitation in Saint Petersburg, Russia. Study subjects (n = 197) were recruited prospectively at the time of their admission to rehabilitation between March 2010 and May 2011 at 7 inpatient opiate addiction rehabilitation centers in Saint-Petersburg and neighboring regions, Russia. The centers provided varying rehabilitation programs; 6 of them were religious centers. Socio-demographic information and self-reported HIV status were collected. Personality profiles and severity of drug-associated problems were estimated before and after rehabilitation using the Minnesota Multiphasic Personality Inventory 2 (MMPI-2), and the Addiction Severity Index (ASI).Thirty-three (17%) subjects dropped out before completing rehabilitation (non-completers). All subjects (completers and non-completers) had psychopathological personality profiles according to MMPI-2. These profiles were refractory to clinically significant improvement after rehabilitation, although some statistically significant changes toward improvement were observed. ASI scores showed statistically and clinically significant improvements after rehabilitation on all scales. Participants in longer-term versus shorter-term rehabilitation programs showed similar changes in their pre- and post-rehabilitation MMPI-2 and ASI scores. Our results suggest that unmet psychiatric needs should be addressed to potentially improve treatment completion in this population.The online version of this article (10.1186/s13104-018-3699-5) contains supplementary material, which is available to authorized users. Of the estimated 11–21 million people who inject drugs (PWID) in the world, many reside in China, the United States, the Russian Federation, and Brazil, which together account for an estimated 45% of the worldwide population of PWID [This study is the secondary analysis of the data collected during the project “implementation of a comprehensive program to overcome the HIV/AIDS epidemic with participation of religious organizations” conducted by the religious public organization “Diakonia” in Saint Petersburg, Russia, under support of United Nations Development Programme (UNDP/163/2008 by 01.10.2008).Study participants were enrolled at the time of their admission into 7 inpatient opiate addiction rehabilitation centers in Saint-Petersburg and neighboring regions in Russia. Table For acceptance into the rehabilitation centers, all clients had to meet the following requirements: have completed a detoxification program, be drug-free, be motivated to undergo rehabilitation, and meet gender requirements. Factors that excluded acceptance into or resulted in expulsion from the rehabilitation programs included lack of a firm will to discontinue drug use or breaking program rules .Prior to enlisting participants, this study was approved by the Ethics Committee of Pavlov Medical State University. All participants provided written, informed consent upon entering the study. There was no remuneration or other compensation for participation.On entering rehabilitation, participants filled a battery of questionnaires: the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), Addiction Severity Index (ASI), and a socio-demographic questionnaire. The subjects required about 60–90 min on average to fill the questionnaires, working individually with a psychologist (a member of the research team unaffiliated with the programs). At the end of rehabilitation, participants again filled the MMPI-2 and ASI surveys. Participants who failed to complete their rehabilitation programs were not retested.The use of drugs or alcohol was prohibited at each center under the threat of expulsion from rehabilitation, if discovered. However, no tests were used to track subjects’ drug abstinence during rehabilitation.The dates of early termination of rehabilitation by non-completers were not recorded by the centers, so there was no opportunity to analyze survival plots.Personality was assessed by the Russian version of the MMPI-2, which is a well-validated personality test for individuals aged 18 and older . The MMPThe Addiction Severity Index-5 (ASI) is a well-documented instrument , adminisA socio-demographic form was developed specifically for this study. It contained 17 questions about socio-economic, infectious disease history and family status, and 9 questions about alcohol consumption.Calculations were performed using R 3.4.2 statistical package . CategorTo evaluate whether PWIDs in our sample could be classified into several meaningful subgroups, k-means cluster analysis of MMPI-2 or ASI scales was performed.All rehabilitation clients who were invited to participate agreed to join the study. The study participants were 197 Caucasian adults , ranging in age from 16 to 59  dropped out of their rehabilitation programs. These non-completers did not have any distinctions in socio-demographic variables  of non-completers were in longer-term programs and only 9 (7.7%) in shorter-term programs . However the rate of non-completion  was the same: 4.3 non-completers per 100 patients per month (30 days) in shorter-term programs and 4.4 non-completers per 100 patients per month in longer-term programs .Comparing pre- and post-rehabilitation scores, MMPI-2 results showed a subtle, but statistically significant, reduction in all clinical scales except for the Ma-scale, reduction in the F-scale, and an increase in the K and L scales  at the start of rehabilitation. The rate of dropping out seemed to be constant over time and was not associated with the length of rehabilitation.We found no association between dropping out and variables such as family status, Family ASI scale, and having children. Other studies usually show a positive relationship between therapy retention and family involvement . Also, wASI scores showed statistically and clinically significant improvements on all scales after rehabilitation. These results were predictable. Since rehabilitation was in-patient, participants could not work or consume alcohol, which likely affected their Employment and alcohol ASI scales.Our results should be interpreted with the following limitations in mind. The study participants had long-standing addiction and significantly abnormal psychiatric personality profiles that may not be typical of PWID in Russia, limiting the generalizability of these findings. Limited resources and patient confidentiality also prevented any laboratory, medical, or evaluations by psychiatric clinicians for our study, and no drug tests were performed during rehabilitation to assess adherence to abstinence. Additionally, we had no information on those dropping out of rehabilitation and no long-term follow-up data on relapse rates after completion of rehabilitation. Lastly, we reported statistical test results comparable to those reported in the literature. However, no randomization was conducted in this study, and thus p-values cannot provide probability inferences as they would in randomized trials.Additional file 1: Table S1. Socio-demographic characteristics, continuous variables.Additional file 2: Table S2. Socio-demographic characteristics, categorical variables.