In the previous studies, the author has proposed the probabilistic peer-punishment based on the difference of payoff, and presented that the proposed peer-punishment utilizes its mechanism for preventing antisocial punishment like retaliation of a defector on a cooperator, effectively enhances the evolution of cooperation, and greatly increases the average payoff of all players in various parameters regarding static three types of topology of connections. Here, this study introduces both activities of breaking and creating connections of every player based on his/her preference to the model of the proposed peer-punishment. Every player will keep connections with his/her preferable players, whereas he/she will frequently break connections with his/her dissatisfied other players. Therefore, the new model of this study is the combination of probabilistic peer-punishment and coevolutionary mechanism that not only strategy of players but also connections between players evolve. This study discovers new knowledge that such combination induces high-level evolution of cooperation and great increase of the average payoff of all players in the condition where cooperation is hard to evolve. Further investigation2 shows that the proposed peer-punishment utilizes its mechanism for preventing antisocial punishment like retaliation of a defector on a cooperator, effectively enhances the evolution of cooperation, and greatly increases the average payoff of all players in various parameters regarding static three types of topology of connections that do not change during each simulation run. Players should make the difference of payoff small for the evolution of cooperation by utilizing the proposed peer-punishment because those previous studies2 employ cumulative payoff from all matches. Chen and Perc3 show similar knowledge that absolute (cumulative) payoff requires high-degree players to be punished stronger than low-degree players. Note that degree means the number of connections of every player. Szabó and Szolnoki4 also present similar result that players achieve the highest total payoff when sharing their payoff fraternally in the spatial evolutionary game with the myopic strategy update rule.The author has proposed the probabilistic peer-punishment based on the difference of payoff in the previous study7 is fixed among players, and does not change depending on the difference of payoff between punishing and punished players. Szolnoki et al.9 propose the notion of emotional profiles that seems to be conceptually like the proposed peer-punishment. However, such profiles consist of only two types of emotion. They introduce sympathy and envy as the two emotional profiles that determine the strategy of every player, and define such profiles as the probability that every player cooperates with players having lower and higher payoff, respectively8. They also consider the imitation of emotional profiles of neighbor players instead of pure strategy9. Szolnoki and Perc10 consider the conditional punishment that is not based on the difference of payoff between punishing and punished players, but proportional to the number of other conditional and unconditional punishers within the group. Regarding other cases utilizing probability, Chen et al.11 show that the introduction of probabilistically monitoring defectors and implicated punishment is indeed effective in realizing cooperation in infinite and finite well-mixed populations. In implicated punishment, each player of a group is punished regardless of his/her strategy once a defector is detected within that group. They also present that the addition of peer-punishment further promotes the evolution of public cooperation. Perc et al.12 systematically review the main results obtained in the realm of statistical physics of human cooperation. They describe that probabilistically sharing the responsibility to sanction defectors can solve the problem of costly punishment in the section regarding adaptive punishment.The main trait of the proposed peer-punishment is that a player determines whether he/she punishes another player or not according to the dynamically changing probability based on the difference of payoff between them. The probability of punishment of some previous studies30. For example, either random or intentional rewiring process contributes to cooperation16. Double resonance phenomenon, i.e. slowly varying topology of connections and additive random payoff disturbances, facilitates cooperation17. Pacheco et al.18 discuss the difference in the capability of searching new connections of players, considering the relation between the dynamics of strategy of players and that of connections between players. Szolnoki and Perc19 show that another simple coevolutionary mechanism of adoption of successful strategy and instructive behavior may lead to highly heterogeneous distributions of such behavior contributing cooperation. Szolnoki et al.20 show that there is an optimal maximal degree for cooperation utilizing coevolutionary mechanism. Poncela et al. employ the mechanism of preferential attachment that a new player makes connections to either the randomly selected player or the player succeeded in the past21, and show that such mechanism generates topology of connections between players where cooperation survives22. Szolnoki and Perc reveal that coevolutionary mechanism of adoption of new strategy and either breaking or creating connections between players after some rounds of game has a strong effect on cooperation23, and supports cooperation within entire range of temptation to defect24. Van Segbroeck et al.26 newly introduce the diversity to adverse connections between players, and show how such coevolutionary mechanism is beneficial to entire population when myopic players act for their payoff. Zhang et al.27 show that the heterogeneity of topology of connections and coevolutionary mechanism attain high-level cooperation in public goods game interactions. Lee et al.28 investigate the case where parameters of payoff matrix and connections between players coevolve with game interactions. Perc and Szolnoki29 explain that coevolutionary mechanism influences connections between players, capability of reproduction of players, their reputation, mobility or age. Perc et al.30 review recent advances in the study of evolutionary dynamics of group interactions on structured populations including coevolutionary mechanism. They also compare these results with those obtained on well-mixed populations.The proposed peer-punishment has a remarkable effect on the evolution of cooperation regarding static three types of topology of connections as noted before. On the other hand, some studies prove that coevolutionary mechanism of strategy of players and connections between players facilitates cooperation2. Every player will keep connections with his/her preferable players, whereas he/she will frequently break connections with his/her dissatisfied other players. Therefore, the new model of this study employs the proposed peer-punishment, and includes coevolutionary mechanism that not only strategy of players but also connections between players evolve. No previous studies have investigated an effect of the combination of probabilistic peer-punishment and coevolutionary mechanism on the evolution of cooperation. This study exhibits that such combination has a remarkable effect on the evolution of cooperation.Here, this study introduces both activities of breaking and creating connections of every player based on his/her preference to the model of the proposed peer-punishment2. As shown in Fig. 31, (b) (Completely) Random31, and (c) Scale-free known as the Barabási-Albert model32. They are defined as one dimensional lattices of periodic boundary conditions, and a vertex exhibits a player. The degree (the number of connections) of player i is k(i). The average of k(i) (〈k〉) can be expressed as 1. Figure k> = 4. Note that this figure has only 12 players for the intelligibility.The model of this study is basically based on the proposed model of the previous studiesi (s(i)) is expressed as either (0 1) (defector) or (1 0) (cooperator) utilizing unit vectors. Player i plays the prisoner’s dilemma game with the other players connected with him/her, and then acquires the cumulative payoff P(i) from all games (matches). When N is the number of all players, player j is the opponent player of player i , and s(j) and P(j) is the strategy and payoff of player j, respectively, we can express P(i) as the following equation (A. O(i) is the set of the other players connected with player i, and b is the temptation to defect in the prisoner’s dilemma game. The initial ratio of the number of defectors to the number of cooperators is approximately one to one, and they are randomly distributed in every simulation run following the previous studies2.The strategy of player either 0  (defectoi compares his/her payoff (P(i)) with the payoff of the opponent player j (P(j)), and he/she inflicts the cost rP(i) on the opponent player j with the probability qi(j) expressed as the following equations (P(i)(1-rn(i)) > 0 and P(i) < P(j) ≤ 2 P(i) hold. Note that r is the coefficient of punishment (0 ≤ r ≤ 1), and n(i) is the number of players jO(i) that satisfies both P(j) > P(i) and s(j) = (0 1) (defector). When P(i)(1-rn(i)) > 0 and P(j) > 2 P(i) hold, qi(j) equals 1. P(i)’ and P(j)’ cannot be negative because the decrease of payoff by punishing and punished is independently calculated regarding all players, and finally P(i)’ is set to 0 when it becomes a negative value. The proposed peer-punishment does not work in the case of r = 0 and 1 as described in the previous study2.Player quations  and 3)  i compari adopts the strategy of player jmaxiO(i) for his/her strategy of the matches of the next generation as the following equation  connections between players, and introduce the uniform expression of the probability r(i) that player i breaks his/her connection to player jO(i) as γi  is the peculiar probability at which player i ) wants to break the connection with player jO(i) . On the other hand, this study, considering the pairing patterns of players i and j, precisely defines the activity of breaking connections as follows. (1) When both players i and jO(i) are cooperators, player i breaks his/her connection to player j with the probability of minimum γi (i.e. γS). (2) When player i is a cooperator, player jO(i) is a defector, and γi is less than or equal to γj (or player jO(i) is a cooperator, player i is a defector, and γj is less than or equal to γi), player i breaks his/her connection to player j with the probability γS. (3) In any case not covered by those conditions, player i breaks his/her connection to player jO(i) with the probability Firstly, regarding the activity of breaking connections, the previous studies25 describe that player i has his/her propensity αi to create new connections, and creates his/her new connection to another player j′ O(i) with the probability αiαj′. In this study, unlike the previous studies25, every player creates new connections as follows. When either player i or jO(i) breaks his/her connection to player j or i, on the one hand either player i or j creates his/her new connection to randomly selected another player j′ O(i) or i′ O(j) with the probability αiαj, but on the other hand players i and j create their connections with the probability 1-αiαj again. That activity of creating new connections considers the case where we cannot break connections to others because of the geographical or social restrictions (e.g. we cannot easily change our place of residence or jobs) although we decide to break such connections once. Unlike the previous studies25, each value of <k> regarding initial topology of connections is conserved in the dynamics of connections of this study. Figure i and j.Secondly, regarding the activity of creating connections, the previous studies2, N = 1000, b = 1.5, and the initial ratio of the number of defectors to the number of cooperators approximately equals one to one. Defectors and cooperators are randomly distributed in every simulation run. Secondly, following the previous studies25, this study determines that the minimum γi (γS) and the maximum γi (γF) equal 0.25 and 0.75, respectively. The peculiar probability at which player i wants to break connections (γi) randomly falls into the M = 50 subdivisions of the interval  so that the number of players with the same peculiar probability of breaking connections is equal regarding all subdivisions. Regarding the propensity αi to create new connections, every player has the same propensity α = αi = 0.4 following the previous studies25. This study defines the process composed of (1) all matches of the prisoner’s dilemma game, (2) all punishing and punished activities, (3) the adoption of new strategy regarding all players, and (4) all activities of breaking and creating connections as one generation. Every simulation run lasts until the number of generations reaches 300. The following results are the average of 20 simulation runs, and have error bars  if necessary.The setting of parameters regarding computer simulation is as follows. Firstly, following the previous studiesk> = 4 is regular  where high-level cooperation evolves and the average payoff of all players greatly increases is wider than such range in the case without those activities. This trend is especially exhibited in the initial random case  and tolerance threshold of each player. Cooperators can survive and even prevail when they have benign CE around. They show that moderate rationality level can result in the optimal cooperation level. In their model, players unilaterally break the connection to the neighbor player whose lower CE level is out of tolerance threshold, and carry out ordered preferential rewiring. When such rewiring fails, random rewiring is considered. Whereas in this study, players break connections according to the probability that is calculated by their peculiar probability of breaking connections considering their pairing patterns, and create new connections according to the probability of the product of their propensity. Therefore, both activities of breaking and creating connections reflect the preference of each player.The author describes the difference between the model of this study and the relevant previous work as follows. Chen 17, and also not covered by Szolnoki and Perc23. This study discovers new knowledge that the combination of probabilistic peer-punishment and coevolutionary mechanism generates small cooperative groups that are necessary for the evolution of cooperation (by coevolutionary mechanism), enlarges such groups (by probabilistic peer-punishment), and as a result, induces high-level evolution of cooperation and great increase of the average payoff of all players in the condition where cooperation is hard to evolve.As described in the explanation of the model, in this study, the following events (1) all matches of the prisoner’s dilemma game, (2) all punishing and punished activities, (3) the adoption of new strategy regarding all players, and (4) all activities of breaking and creating connections successively occur, and then topology of connections varies fast. Moreover, there are no additive random payoff disturbances to the payoff of every player. This condition is the reverse of double resonance phenomenon facilitating cooperation presented by Perc35 present an effect of the coexistence of reward and punishment. For example, Szolnoki and Perc34 discuss whether the combined application of reward and punishment is evolutionary advantageous or not, and find rich dynamical behavior that generates intricate phase diagrams where continuous and discontinuous phase transitions successively occur. Chen et al.35 propose the institutional sanctioning policy that switches the incentive from rewarding to punishing when the frequency of cooperators exceeds a threshold. They find that such policy establishes and recovers full cooperation at lower cost. Therefore, the author plans to investigate an effect of the coexistence of reward, coevolutionary mechanism, and the proposed peer-punishment on the evolution of cooperation. In addition, although the topology of connections becomes roughly random in the end when introducing both activities of breaking and creating connections, the kurtosis of the average degree distribution of lattices regarding the cases of finally all cooperators has proved to be more positive than that regarding the cases of finally all defectors. This phenomenon is observed in the case of <k> = 16 regarding the initial random (r = 0.75) and scale-free (r = 0.5) cases at the moment. The main reason why this phenomenon occurs is that connections to cooperators will last for a long period, whereas those to defectors will frequently rewired. Therefore, it will be necessary to investigate how this phenomenon affects the spatiotemporal dynamics of the evolution of cooperation.Regarding the future work, the author recognizes that the introduction of reward to the proposed model of this study is necessary. Some previous studies


Complaints of imbalance are common non-resolving signs in individuals with post-concussive syndrome. Yet, there is no consensus rehabilitation for non-resolving balance complaints following mild traumatic brain injury (mTBI). The heterogeneity of balance deficits and varied rates of recovery suggest varied etiologies and a need for interventions that address the underlying causes of poor balance function. Our central hypothesis is that most chronic balance deficits after mTBI result from impairments in central sensorimotor integration that may be helped by rehabilitation. Two studies are described to 1) characterize balance deficits in people with mTBI who have chronic, non-resolving balance deficits compared to healthy control subjects, and 2) determine the efficacy of an augmented vestibular rehabilitation program using auditory biofeedback to improve central sensorimotor integration, static and dynamic balance, and functional activity in patients with chronic mTBI.Two studies are described. Study 1 is a cross-sectional study to take place jointly at Oregon Health and Science University and the VA Portland Health Care System. The study participants will be individuals with non-resolving complaints of balance following mTBI and age- and gender-matched controls who meet all inclusion criteria. The primary outcome will be measures of central sensorimotor integration derived from a novel central sensorimotor integration test. Study 2 is a randomized controlled intervention to take place at Oregon Health & Science University. In this study, participants from Study 1 with mTBI and abnormal central sensorimotor integration will be randomized into two rehabilitation interventions. The interventions will be 6 weeks of vestibular rehabilitation 1) with or 2) without the use of an auditory biofeedback device. The primary outcome measure is the daily activity of the participants measured using an inertial sensor.The results of these two studies will improve our understanding of the nature of balance deficits in people with mTBI by providing quantitative metrics of central sensorimotor integration, balance, and vestibular and ocular motor function. Study 2 will examine the potential for augmented rehabilitation interventions to improve central sensorimotor integration.NCT02748109)This trial is registered at clinicaltrials.gov ( Between 1.6 and 3.8 million sports-related mild traumatic brain injuries (mTBI) occur annually in the United States . The estThe difficulty establishing consensus rehabilitation protocols is likely due to the complex nature of balance deficits in individuals with mTBI that can include neurological damage to the brainstem , 11, thaA proportion of individuals with mTBI who do not have prominent or measurable sensory deficits may still have balance problems attributable to abnormal central control of balance. Central sensorimotor integration is a critical aspect of balance control that may underlie dysfunction in static and dynamic balance. Proper central sensorimotor integration relies on the central nervous system to 1) select the optimal combination of sensory sources for balance and 2) reweight the sensory contributions as sensory conditions change , 23. WheThe lack of knowledge about central sensorimotor integration following mTBI may be in part due to the current clinical tools used to assess balance, gait, and sensorimotor integration. The Balance Error Scoring System (BESS) is the most frequently administered clinical balance assessment for patients with suspected mTBI. Yet, the BESS suffers from a high degree of subjectivity, provides only limited, low-resolution information about the balance control system , and is For instance, wearable sensors enable biofeedback based on quantitative measurements of balance and gait. Specifically, auditory biofeedback (ABF), which communicates sway through changes in the pitch or location of a tone, has shown promise in aiding balance and retraining central sensory integration mechanisms in populations with balance impairments , 43. QuaThe heterogeneity of balance deficits and varied rates of recovery after mTBI suggest varied etiologies and a need for interventions that address the underlying causes of poor balance function. Over-simplified clinical balance measures often do not account for the complex integration of sensory and motor systems. Therefore, the first goal of this study is to characterize balance deficits in people with mTBI who have chronic, non-resolving balance deficits compared to healthy control subjects without a history of mTBI. We hypothesize that a) objective measures of central sensorimotor integration, static and dynamic balance will better distinguish people with mTBI from control subjects than clinical measures, b) a subset of people with mTBI will have abnormal central sensorimotor integration test measures, even without peripheral vestibular or ocular motor deficits, and c) the relationship between poorer static/dynamic balance performance and mTBI is regulated/mediated by central sensorimotor integration. The second goal is to determine if augmenting a traditional balance rehabilitation program with a wearable-sensor based ABF system improves central sensory integration in patients with chronic mTBI compared to the standard balance rehabilitation program. Our central hypothesis is that chronic balance deficits after mTBI result from impairments in central sensorimotor integration that may be helped by rehabilitation that challenges balance while simultaneously providing feedback on balance performance. We hypothesize that a) central sensorimotor integration scores will improve with rehabilitation and ABF will increase the improvement of central sensorimotor integration scores beyond the standard of care, b) intervention with ABF will improve objective measures of balance and c) people with central sensorimotor integration impairment will show sustained improvement in central sensorimotor integration scores and balance after rehabilitation with ABF.This study has two parts: Study 1) A cross-sectional study to identify and characterize balance control strategies after mTBI compared to healthy controls, and Study 2) An interventional randomized pilot study using a novel ABF rehabilitation technique to improve central sensorimotor integration after mTBI. All subjects will complete baseline clinical sensory, ocular motor, vestibular, neurocognitive, static and dynamic balance, and central sensorimotor integration testing. Following baseline testing, a subset of mTBI participants will be randomized into two treatment groups for 6 weeks of rehabilitation. Within 1 week after completing the rehabilitation program, subjects will repeat the balance and central sensorimotor integration testing to examine the short-term effects of rehabilitation. Subjects will complete the balance and central sensorimotor integration testing a third time, 6 weeks after completion of the rehabilitation, to assess retention. This trial is registered at clinicaltrials.gov (NCT02748109).The study will include 65 mTBI participants and 65 control subjects for the cross-sectional arm of the study. All subjects will be between the ages of 18 and 60. Participants in the mTBI group will have a diagnosed mTBI or concussion and chronic, non-resolving complaints of balance problems determined by a score >0 on the Dizziness Handicap Inventory (DHI) at the time of testing and > 3 months post-injury. Control subjects will be gender and age matched to the mTBI group. For the second arm of the study, a subset of 40 mTBI subjects with abnormal central sensorimotor integration with or without peripheral vestibular or ocular motor deficits will be randomized into two rehabilitation groups, experimental treatment and standard of care, consisting of two rehabilitation sessions per week for 6 weeks. All subjects will be tested at either Oregon Health & Science University or the VA Portland Health Care System. All rehabilitation will take place at Oregon Health & Science University. Informed written consent will be obtained from all participants.Inclusion criteria for mTBI recruitment are 1) a diagnosis of mTBI based upon VHA/DoD criteria with persisting symptoms >3 months post injury , 2) betwAll people who are eligible per phone screening will come into the clinic for the informed consent process. An investigator will verbally explain the consent form, allow the person ample time to read through the consent form and then will acknowledge consent by signing the form. All subjects will first read and sign consent forms. For study 1, 65 mTBI participants and 65 control subjects will be assessed on a battery of tests designed to measure vestibular function, sensorimotor integration, static and dynamic balance, cognition, reaction time, clinical sensory loss, and self-reported symptoms. All protocols have been approved by the OHSU Institutional Review Board.Participants will complete a novel central sensorimotor integration test (CSMI test)  on a modThe primary outcome measures of central sensorimotor integration are the parameters derived from a model-based interpretation of CoM sway evoked by pseudorandom stimuli Table . The proThe parameters include sensory weights that represent the relative contributions of proprioceptive, visual, and vestibular sensory information to balance control such that in each of the 4 test conditions the sum of weights equals 1. These weights are known to vary with test conditions that alter the availability (e.g. eyes open or closed) or reliability (e.g. varying stimulus amplitudes) of sensory information and are affected by sensory abnormalities . For condition 1 tests (surface tilt stimulus with eyes closed) the model fit to the experimental FRF provides a measure of the proprioceptive contribution (Wprop). With eyes closed, the visual contribution (Wvis) is zero and therefore the vestibular contribution is Wvest = 1 – Wprop. Condition 2 tests also use a surface tilt stimulus but eyes are open with both visual and vestibular cues providing accurate orientation information. The model fit again provides an estimate of Wprop but now 1 – Wprop = Wvis + Wvest. Condition 3 tests present a visual tilt stimulus where the model fit provides a measure of Wvis and 1 – Wvis = Wprop + Wvest. Finally condition 4 tests simultaneously present the same surface and visual-tilt stimulus. In this condition the model fit provides a measure of Wprop + Wvis with Wvest = 1 – (Wprop + Wvis).The neural controller parameters Kp and Kd Table  represenPower analysis for Study 1 was derived from a pilot dataset of 12 control subjects who participated in CSMI tests with the goal of estimating the sample size necessary for a normative reference group. This was defined as the number of control subjects necessary to see a coefficient of variation (CV) of less than 10%, a common threshold for measurement precision, in the CMSI outcome measures in order to develop a robust reference control group with a reasonable and acceptable amount of variability (49). Estimation was done with bootstrapping techniques using the stiffness control parameter (Kp) of the CMSI test of the pilot cohort as a sample distribution. For a considered sample size (ranging from 10 to 100 subjects), 1000 random samples with replacement were taken from the cohort’s Kp dataset to simulate a null distribution at that sample size and estimate the corresponding CV. Based on these permuted samples, the CV of the Kp parameter is expected to be less than 10% with 65 subjects, giving an adequately representative reference group. This same sample size of 65 will allow for a CV of ~6.5% in other CSMI measures such as visual sensory weighting. Therefore, a cohort of 65 mTBI subjects should be more than sufficient to properly identify any underlying classification differences arising from central sensorimotor integration abnormalities.Secondary outcome measures of symptomology, neurocognition, static and dynamic balance, and central sensorimotor integration are provided in Table Subjects will be instrumented with body-worn inertial sensors during the performance of static and dynamic balance tests. Five synchronized wireless Opal inertial sensors  will be affixed to the participants’ head, sternum, lumbar, left and right feet using elastic straps. Data will be collected at 128 Hz and transferred to a laptop for automatic generation of gait and balance measures by Mobility Lab software  and addiTo classify mTBI participants as with or without vestibular dysfunction, standard vestibular testing will be performed as described in Table To determine if objective/novel measures of balance will better distinguish people with mTBI from control subjects than clinical measures, we will use logistic regression classification models. Improvements in classification will be determined by calculating the area under the curve (AUC) of the receiver-operator characteristic and then comparing AUC for each traditional clinical measure with the novel objective measures. Significance will be assessed using bootstrapping to determine if the observed differences in AUC are greater than would be expected by chance at a level of 0.05.To determine if a subset of people with mTBI will have abnormal CSMI test measures, even without peripheral vestibular or ocular motor deficits, we will use descriptive statistics to characterize the distribution of CSMI test measures and vestibular/ocular motor scores. We will define CSMI test measures and vestibular/ocular motor scores as abnormal if the scores are greater than 2 standard deviations from the mean in controls. We will then classify mTBI subjects into one of 4 CSMI-vestibular/ocular motor categories 1) abnormal CSMI –abnormal vestibular/ocular motor scores, 2) abnormal CSMI –normal vestibular/ocular motor scores, 3) normal CSMI –abnormal vestibular/ocular motor scores, 4) normal CSMI –normal vestibular/ocular motor scores.To determine if the observed relationship between poorer static/dynamic balance performance and mTBI is regulated/mediated by central sensorimotor integration, we will use mediation analysis. Mediation analysis  will allSubjects with mTBI who participate in Study 1 will be screened for Study 2. An investigator will verbally explain the consent form and time commitments involved with the rehabilitation, allow the person ample time to read through the consent form and then will acknowledge consent by signing the form. For Study 2, 40 mTBI participants will participate in a 6-week rehabilitation program randomized to either 1) a standard vestibular focused rehabilitation program or 2) the same standard vestibular focused rehabilitation program with the addition of audio biofeedback. Post-rehabilitation assessments will be conducted one week after the conclusion of the program. Retention will be assessed 6 weeks following the post-rehabilitation session. A flowchart illustrating the study design is depicted in Fig. Participants will be randomized into either 1) a standard of care vestibular rehabilitation program or 2) a standard or care vestibular rehabilitation plus ABF training program. Rehabilitation will begin approximately 7-10 days after pretest assessment. Both groups will receive the rehabilitation that includes progressive (3 levels of difficulty) exercises that target gaze stabilization, vestibular stimulation, balance and proprioceptive retraining –52 . The ABF system detects AP and mediolateral (ML) linear accelerations near the body’s CoM. The participant wears headphones and the ML inclination with respect to gravity is encoded as a sound in either the right or left ear while the AP tilt is encoded as changes in pitch as the person leans forward or backward. When the body is in perfect equilibrium, the system is quiet. A calibration test is used to determine optimal balance parameters such that the auditory feedback increases only when the person exceeds their baseline sway by 1° or the tilt of the trunk during gait exceeds an established threshold , 53. BotThe primary outcome measure is the change between pre-rehabilitation, post-rehabilitation, and 6-week follow-up testing of the model-based parameters of the novel CSMI test Table . Secondap = 0.065) and visual sensory weight Wvis under fixed surface visual tilting  were used to calculate sample size based on independent sample t-tests. Rehabilitation-based differences within the TBI cohort are not expected to be as extensive as those identified between mTBI subjects and controls. However, an effect size at 50% of the magnitude of the difference observed between mTBI and controls in the preliminary data (Coehn’s d = 0.748) would still yield 72% power with α = 0.10 in a repeated measures framework in as little as 10 subjects per group. Conservatively, we will recruit 20 subjects each for the experimental treatment and standard of care groups to provide results that are capable of powering a more in-depth future study investigating rehabilitation for balance deficits in mTBI patients.We powered the intervention study to detect improvement in the CSMI test deficit we found in our pilot subjects. Conservatively, 20 subjects will be recruited for both the experimental treatment and standard of care groups to provide results that are capable of powering a more in-depth future clinical trial investigating rehabilitation for balance deficits in mTBI patients. The CSMI test deficits observed in 3 mTBI pilot subjects and 12 healthy controls were used to power the study. Specifically, the CSMI parameter Kp under open eye surface tilting  in CSMI measures will control for the correlation of the repeated pre- and post- measures within an individual as well as maximizing the number of covariates we can potentially add into the regression models. Covariates of interest are vestibular/ocular motor scores, PTSD, Veteran versus civilian status, age, and gender. In addition, we will determine whether rehabilitation improved the static and dynamic balance metrics that were measured at the pre- and post-rehabilitation testing visits.To determine if people with CSMI impairment show sustained improvement in static and dynamic balance after rehabilitation, even after ABF is discontinued, we will use linear mixed effects regression modeling which will include a third time point of interest (6 weeks after the rehabilitation intervention has stopped). Time will be treated as a categorical variable in order to evaluate changes in CSMI scores and balance during specific time periods, in this case, between the cessation of the intervention and the 6 weeks post-intervention follow-up. We will explicitly test the significance of a group by time interaction to determine if participants in the ABF rehabilitation group showed sustained improvements in outcomes beyond that experienced by participants with the standard of care. If applicable, we will then stratify the analysis by those with abnormal/normal measures of vestibular/ocular motor function to assess the relationship between vestibular/ocular motor function deficits and sustained improvement in CSMI score and balance.The goal of this study is to determine whether persistent balance and gait deficits following mTBI are attributable to improper central sensorimotor integration and to examine the potential for augmented rehabilitation to improve daily activity levels. In particular, this study is unique in its investigation of central sensorimotor integration in people with persistent balance complaints following mTBI in that confounding covariates such as vestibular disruption, neurocognition, and physical activity will be objectively quantified. Additionally, if we find that the quantitative balance, gait, or novel CSMI measures have a significantly greater AUC compared to the clinical measures  at detecting patients with chronic mTBI, this will indicate the necessity for objective quantification of gait and balance deficits for mTBI assessment.If a portion of subjects with chronic balance problems after mTBI have abnormal CSMI test measures but normal clinical vestibular testing results, this suggests that sensory integration processes in the brain can be impaired following mTBI, even without peripheral sensory deficits, and that abnormal central sensorimotor integration plays a key role in persistent abnormal balance in subjects with normal vestibular function. If a portion of mTBI subjects have abnormal CSMI test measures and abnormal clinical vestibular function, the key mediator for balance problems is less clear and may be due to the peripheral vestibular dysfunction, abnormal central sensorimotor integration, or inadequate adaptation of central sensorimotor integration to account for abnormal sensory information. If we find that abnormal central sensorimotor integration mediates abnormal static or dynamic balance, rehabilitation will need to be focused on improving central sensorimotor integration to improve balance. If we find that CSMI scores improve with rehabilitation, we will conclude that central sensorimotor integration strategies are flexible and can improve with training. Further, if we find that central sensorimotor integration and gait and balance jointly improve with rehabilitation, we will have further evidence that central sensorimotor integration plays a key role in the mediation of gait and balance following mTBI. If we find a greater improvement in the ABF group, we will conclude that ABF may facilitate improved rehabilitation. Finally, if we find that people with central sensorimotor integration impairment maintain improvements after stopping the use of ABF device, we will conclude that a recalibration of sensory integration and/or sensorimotor transformation for balance has occurred, rather than sensory substitution that requires active ABF. If we find that sensory strategies can change with practice and biofeedback, we will conclude that this targeted approach to balance rehabilitation should be explored further in a larger, randomized trial. It is possible that different subclasses based on CSMI measures respond differently to rehabilitation or ABF-augmented rehabilitation. In such a case, the results of this study will provide valuable information for powering an in-depth future study carefully examining the relationship between CSMI subclasses and the response to rehabilitation with and without ABF. The results of this study will further our understanding of the mechanisms underlying posture and gait dysfunction in individuals with chronic mTBI and inform future studies about the efficacy of ABF augmented rehabilitation programs.


The function and the mechanism of long non‐coding RNAs (lncRNAs) on the osteogenic differentiation of maxillary sinus membrane stem cells (MSMSCs) remain largely unknown.The expression of lnc‐NTF3‐5 and Runt‐related transcription factor 2 (RUNX2), Osterix (OSX), and Alkaline Phosphatase (ALP) was examined by quantitative real‐time PCR (qRT‐PCR) in MSMSCs during the process osteogenic differentiation. Then the function of lnc‐NTF3‐5 was evaluated by loss‐ and gain‐of‐function techniques, as well as qRT‐PCR, western blot, and Alizarin Red staining. In addition, the microRNAs (miRNAs) sponge potential of lnc‐NTF3‐5 was assessed through RNA immunoprecipitation, dual luciferase reporter assay, and in vivo ectopic bone formation.Lnc‐NTF3‐5, RUNX2, OSX, and ALP increased alone with the differentiation. Inhibition of lnc‐NTF3‐5 decreased the expression of RUNX2, OSX, and ALP both at mRNA and protein levels. Alizarin red staining showed similar trend. In contrast, overexpression of lnc‐NTF3‐5 presented totally opposite effects. Besides, overexpression of lnc‐NTF3‐5 could decrease the expression of microRNA‐93‐3p (miR‐93‐3p). Enhance miR‐93‐3p could also inhibit the expression level of lnc‐NTF3‐5. RNA immunoprecipitation demonstrated that lnc‐NTF3‐5 is directly bound to miR‐93‐3p and dual luciferase reporter assay proved that miR‐93‐3p targets 3′ UTR of RUNX2 to regulate its expression. Ultimately, in vivo bone formation study showed that lnc‐NTF3‐5 and miR‐93‐3p inhibitor co‐transfection group displayed the strongest bone formation.The novel pathway lnc‐NTF3‐5/miR‐93‐3p/RUNX2 could regulate osteogenic differentiation of MSMSCs and might serve as a therapeutic target for bone regeneration in the posterior maxilla. Numerous types of mesenchymal stem cells (MSCs) have been found in craniofacial tissues, which have been shown to possess stem cell properties such as self‐renewal, colony formation, and multilineage differentiation capacity.Despite several researches on MSMSCs differentiation, the precise molecular mechanisms of osteogenic differentiation remain unclear. Osteogenic differentiation of MSCs is controlled by a variety of signaling pathways.In our previous research, differential lncRNAs expression profiles in MSMSCs during osteogenic differentiation were established via microarray, and several upregulated lncRNAs between undifferentiated and osteogenic differentiated MSMSCs have been identified.To address these questions, this study aims to explore the role of lnc‐NTF3–5 in osteogenic differentiation of MSMSCs and how it regulates the process. This study may provide a new molecular mechanism for osteogenic differentiation of MSMSCs and a new therapeutic target for bone regeneration in the posterior maxilla.22.1Studies were conducted in accordance with the guidelines of the Medical Ethics Committee of Sun Yat‐Sen University and approved by the Sun Yat‐Sen University Joint Institutional Review Board (IACUC‐DB‐16–1212). All donors provided informed written consent prior to specimen collection.2.22 culture flasks  with alpha modification of Eagle's medium , and then incubated in 5% CO2 and 37°C.HEK‐293T cells were purchased from the American Type Culture Collection (ATCC). MSMSCs were isolated from normal human MSM according to our previously published methods.2.34 cells/well. BMP2 (100 ng/mL) was used to induce the differentiation when cells were reached 80% confluence.Osteogenic differentiation was conducted according to the instructions from the manufacturer . MSMSCs were plated into 6‐well plates  at a density of 1 × 102.4RNA was extracted from cells via TRIzol reagent , then cDNA was synthesized with SuperScript III reverse transcriptase kit  according to the instruction from the manufacturer. The qRT‐PCR was performed with TaqMan Universal PCR master mix  as described previously.2.5Cells were washed with precooled PBS and lysed using RIPA buffer . Then proteins were separated by 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS‐PAGE) and transferred to polyvinylidene difluoride (PVDF) membrane. Appropriate primary antibodies against RUNX2 , OSX , ALP , and GAPDH  incubated respectively overnight at 4°C. Then membranes were incubated with relevant secondary antibodies and visualized using ECL Western blotting detection kit . Results of western blotting were analyzed with Gel Image system software .2.64 cells/well contain control or osteogenic media. After the indicated number of days of culture, cells were stained with Alizarin Red S  according to the instruction from the manufacturer and imaged using an inverted Zeiss AxioObserver Z1 microscope . Alizarin Red staining quantification was performed according to our previously published methods.Cells were seeded into 6‐well plates at a density of 2 × 102.7For knockdown of lnc‐NTF3–5, the shRNA sequence targeting lnc‐NTF3–5 (sh‐lnc‐NTF3–5) and a scrambled shRNA sequence (scrambled) was cloned into pENTR/U6 vector . The target sequence for sh‐lnc‐NTF3–5 is 5′‐TAGGCCAACCAACACAATTCATA‐3′. All constructs were verified by sequencing. The MSMSCs were transfected with Lipofectamine™ 2000  following the protocol from the manufacturer.For overexpression of lnc‐NTF3–5, the full length of lnc‐NTF3–5 was cloned into pLKD‐CMV‐G lentiviral vector . Then the lnc‐NTF3–5 plasmid vector (lnc‐NTF3–5) or empty plasmid vector (vector) and the packaging plasmids Helper 1.0 and Helper 2.0  were transfected into MSMSCs. Lentiviral infection was conducted as described previously.For miRNA function study, mimics for miR‐93‐3p, miR‐93‐3p inhibitor, as well as a nontargeting control miRNA mimic (nc RNA), were obtained from RiboBio . Then MSMSCs were transfected in the presence of 8 mg/mL Polybrene  overnight.2.84 293T cells in 24‐well plates were plated. A wild‐type lnc‐NTF3–5  or mutated  plasmid was constructed with pmirGLO luciferase vectors . A wild‐type or mutated RUNX2 3′ UTR reporter plasmid were constructed with psiCHECK‐2‐derived reporter vectors . 293T cells were transfected with 1 µg of the reporter constructs and 200 ng of Renilla luciferase internal control vector using Lipofectamine 2000 . The luciferase activity was measured 48 h after transfection using the Dual Luciferase Reporter Assay System  according to the instructions from the manufacturer. Each experiment was repeated at least three times.The 1 × 102.9RIP assay was performed with Magna RIP RNA‐Binding Protein Immunoprecipitation Kit  in accordance with the instructions from the manufacturer. Briefly, magnetic beads were incubated with antibody against Ago2  and mouse IgG control. RNA was extracted and relative expression levels of lnc‐NTF3–5 and miR‐93‐3p in extract were detected by qRT‐PCR as described above.2.10Lnc‐NTF3–5, 18s, U6 subcellular localization in MSMSCs was conducted with a FISH kit . Briefly, MSMSCs were fixed with 4% paraformaldehyde before prehybridized with hybridization solution and then incubated with a digoxigenin‐labeled lnc‐NTF3–5, 18s, U6 probe. The antisense probe served as a negative control. Images were acquired via an LSM 700 Confocal Laser Scanning Microscope .2.11n = 8 for each group) were purchased from the Experimental Animal Department of the Chinese Academy of Sciences, and were housed in SPF conditions during the whole experiment. MSMSCs were transfected with (1) control group, (2) lnc‐NTF3–5, (3) lnc‐NTF3–5 and miR‐93‐3p mimic, and (4) lnc‐NTF3–5 and miR‐93‐3p inhibitor 2 d before transplantation. Approximately 4 × 106 MSMSCs were combined with 40 mg of deproteinized bovine bone  and then placed in the subcutaneous pockets as described in previous studies.Animal experiments in this study were conducted in accordance with protocols approved by the Ethical Committee on Animal Research of the Sun Yat‐Sen University. BALB/c‐nu male mice  from at least in triplicate and were analyzed using two‐tailed unpaired Student's 33.1In our previous microarray result, ENST0000537192.1 (lnc‐NTF3–5) was significantly raised after inducing with 100ng/ml of BMP‐2 for 7 d in MSMSCs. To confirm the expression level of lnc‐NTF3–5 in osteogenic differentiated MSMSCs, qRT‐PCR was conducted and results showed that BMP‐2 induced a 10‐fold increase on day 7 compared with the uninduced control . The miRNAs identified as targets by three of these software were selected. Among the genes, 5 Figure A. Then w5 Figure B.In addition, the expression of lnc‐NTF3–5 in response to the miR‐93‐3p mimics and miR‐93‐3p inhibitor was also examined. QRT‐PCR data revealed that lnc‐NTF3–5 expression was significantly suppressed by miR‐93‐3p mimics Figure C. DuringTo determine whether lnc‐NTF3–5 directly regulates miR‐93‐3p, we generated luciferase reporter constructs. The miR‐93‐3p mimics and lnc‐NTF3–5 wild‐type or mutant reporter were transferred into 293T cells together. The results showed that the luciferase activity of lnc‐NTF3–5 wild‐type reporter was strongly suppressed by miR‐93‐3p mimics Figure E. Howeve3.5RUNX2. Results showed that 3′ UTR of the human RUNX2 mRNA has a putative binding site for miR‐93‐3p within the 2310–2316 nt (Figure To further explore the mechanism of lnc‐NTF3–5 promote osteogenic differentiation, Target Scan and miRDB were used to analysis the binding sites of miR‐93‐3p on the osteogenic master gene 3.6Lnc‐NTF3–5 may function as miR‐93‐3p sponges to regulate the expression of RUNX2 to affect osteogenic differentiation of MSMSCs. To further explore how lnc‐NTF3–5 exert its function, we next transferred miR‐93‐3p inhibitor into lnc‐NTF3–5 downregulated MSMSCs. In comparison with the sh‐lnc‐NTF3–5 group, co‐transfection of miR‐93‐3p inhibitor with sh‐lnc‐NTF3–5 significantly rescued RUNX2 mRNA expression (Figures 3.7The results described above demonstrated that lnc‐NTF3–5 may function as miR‐93‐3p sponges to regulate osteogenic differentiation of MSMSCs in vitro, we next explored whether the regulation of the lnc‐NTF3–5 and miR‐93‐3p expression levels in MSMSCs also exerted an effect on ectopic bone formation in vivo. The regenerated new bone was observed after 8 wk transplantation, when there were nodules in graft. The Lnc‐NTF3–5 and miR‐93‐3p inhibitor co‐transfection group showed the strongest bone formation than the other groups Figure A, B. Whe4LncRNAs are generally defined as transcripts of longer than 200 nucleotides without evident of coding proteins and can regulate diverse cellular processes.To further confirm the function of lnc‐NTF3–5 in osteogenic differentiation of MSMSCs, loss‐ and gain‐of‐function strategy was employed. Lnc‐NTF3–5 overexpression and knockdown MSMSCs were then constructed and subjected to osteogenic differentiation. As is well known, RUNX2 is the core transcription factor in controlling osteogenic differentiation.There is now evidence that lncRNAs regulate gene expression and function at both transcriptional and post‐transcriptional levels.Many studies suggested that miRNAs can directly target the osteogenic master regulator RUNX2.Since lnc‐NTF3–5 functions as miR‐93‐3p sponges, we next explored whether the regulation of the lnc‐NTF3–5 and miR‐93‐3p in MSMSCs also exerts an effect on bone formation in vivo. Results suggested that lnc‐NTF3–5 overexpression led to significant new bone formation in mice. Moreover, co‐transfection of miR‐93‐3p inhibitor led to an even enhanced bone formation in mice. These data suggest that lnc‐NTF3–5 promoted bone formation through competitively binding to miR‐93‐3p in vivo.In conclusion, this study first characterized the osteogenic promoting function of lnc‐NTF3–5 in MSMSCs and demonstrated that lncRNA‐microRNA crosstalk may be the underlying mechanism. Lnc‐NTF3–5 competitively binding to miR‐93‐3p promotes the expression of RUNX2. This study indicated lnc‐NTF3–5 might be a novel biomarker or therapeutic target for promoting bone regeneration in the posterior maxilla.The authors declare that they have no conflicts of interest with the contents of this article.


The aims of this study were to analyze the causes of morbidity and mortality in a large population of seabirds admitted to the Tafira Wildlife Rehabilitation Center (TWRC) in Gran Canaria Island, Spain, from 2003 to 2013, and to analyze the outcomes of the rehabilitation process.r, unassisted mortality Mr, and release Rr), the time until death, and the length of stay were also studied for the seabirds admitted alive.We included 1,956 seabirds  in this study. Causes of morbidity were classified into nine categories: light pollution , fishing gear interaction, crude oil, poisoning/intoxication, other traumas, metabolic/nutritional disorder, orphaned young birds, other causes, and unknown/undetermined. The crude and stratified (by causes of admission) rates of the three final disposition categories (euthanasia ELarus michahellis) was the species most frequently admitted (46.52%), followed by Cory’s Shearwater  (20.09%). The most frequent causes of morbidity were light pollution  (25.81%), poisoning/intoxication (24.69%), and other traumas (18.14%). The final disposition rates were: Er = 15.35%, Mr = 16.29%, and Rr = 68.34%. The highest Er was observed in the ‘other traumas’ category (58.08%). Seabirds admitted due to metabolic/nutritional disorder had the highest Mr (50%). The highest Rr was observed in the light pollution  category (99.20%).Yellow-legged Gull  achieved at the TWRC emphasizes the importance of wildlife rehabilitation centers for the conservation of seabirds. Seabird population changes are good indicators of long-term and large-scale changes in marine ecosystems because their populations are strongly influenced by threats to marine and coastal ecosystems –3. GlobaThe Canary Islands harbor 11 breeding seabird species from three families , four ofHuman participation through wildlife rescue networks and veterinary care in wildlife rehabilitation centers is essential for conservation purposes. The main benefits derived from the rehabilitation of wild birds are: the reinforcement of the natural population after the release, especially in endangered species, the identification of the causes of morbidity and mortality, and the regulatory changes implemented after determining human influences and causes of admission ,12. DataThe aims of this study were to analyze the causes of morbidity and mortality in a large population of seabirds admitted to the Tafira Wildlife Rehabilitation Center (TWRC) in Gran Canaria Island, Spain, from 2003 to 2013, and to analyze the outcomes of the rehabilitation process studying the crude and stratified (by causes of admission) rates of the three final disposition categories , the time until death, and the length of stay at the rehabilitation center.Seabird rehabilitation program at the TWRC was conducted with authorization of the Wildlife Department of the Canary Islands Government (Ms. Guacimara Medina), and the Environment Department of the Cabildo de Gran Canaria . Animal work and all sampling procedures were specifically approved by the Tafira Wildlife Rehabilitation Center Animal Care Committee and the insular government Cabildo de Gran Canaria, and were consistent with standard vertebrate protocols and veterinary practices. Seabirds that had to be euthanized for animal welfare reasons were administered barbiturates by intravenous injection.2) of the Canary Islands archipelago and its coastline is 236 km long , and “vulnerable”   .Few studies on causes of morbidity or mortality of seabirds covered more than one decade ,15,26–28Light pollution  was the most frequent cause of admission (25.81%), mainly among Procellariiformes species. For these seabirds, which commonly attend breeding colonies at night, light pollution has been reported to cause disorientation ,29–33 anClostridium botulinum , Uria aalge), Northern Gannet (Morus bassanus), and Common Scoter (Melanitta nigra) [Stuyvesant oil spill, authors found that the difference in survival between rehabilitated and control birds mostly occurred during the first 34 days after release; after that period of time, they survived comparably to non-oiled control murres [Spheniscus demersus) chicks abandoned by moulting parents, post-release survival rates were similar to African Penguin chicks reared after oil spills and to survival rates for naturally-reared birds [For rehabilitation to be classed as successful the bird must be re-established back into the wild population and have a similar chance to those of wild birds of entering the breeding pool . Severala nigra) ,52. Howel murres . In a moed birds .In the present survey, all the statistically significant differences detected in the final disposition rates when compared among seabird species were clearly related to the cause of admission; this suggests that the different causes of admission with their different prognoses determine the final disposition rates.d values ≤ 2 days, meaning that the decision was made very soon based on the poor prognosis of these cases. The median Td in the seabirds that died during the hospitalization period was ≤ 3 days for all categories, suggesting that these first days of stay at the facility are critical, and a complete clinical evaluation should be performed on these birds, despite their apparently less severe presentation.The parameter time to death, previously used in rehabilitation of raptors, provides direct insight into the initial assessment and prognostication, the complete rehabilitation process, and the validity of veterinary protocols . In our Several authors recommended that stay at wildlife rehabilitation centers must be as short as possible to reduce the risk of captive-related complications, infectious diseases, and behavioral disorders ,55. Accor, Mr, and Rr), and the parameters time until death (Td) and length of stay at the center (Tr) should be included in the outcome research of the rehabilitation of seabirds. Finally, although the high release rate for seabirds (68.34%) achieved at the TWRC emphasizes the importance of wildlife rehabilitation centers for the conservation of seabirds, further studies would be necessary to know the post-release survival rate of rehabilitated seabirds and to understand the effects of rehabilitation on the conservation of seabird populations in Gran Canaria Island.In conclusion, this survey is the first large-scale study on causes of morbidity and mortality of seabirds on Gran Canaria Island, providing useful information for the conservation of these birds. We suggest that, as proposed previously for the rehabilitation of raptors , at leasS1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S1 File(XLSX)Click here for additional data file.


AR copy number (CN) and outcome in a cohort of patients with advanced castration-resistant prostate cancer (CRPC) treated with enzalutamide after docetaxel. Fifty-nine CRPC patients were evaluated. AR CN was analyzed with real-time and digital PCR in the serum collected at starting of treatment. Progressive disease was defined on the basis of Prostate Cancer Working Group 2 criteria. AR CN gain was found in 21 of 59 (36%) patients. Median baseline PSA, alkaline phosphatase and lactate dehydrogenase levels were higher in the AR CN gained group . Median PFS of patients with AR CN gain was 2.4 (95%CI: 1.9−3.2) vs. 4.0 months (95%CI: 3.0−6.5) of those with no gain (p = 0.0004). Median OS of patients with AR CN gain was 6.1 (95%CI: 3.4−8.6) vs. 14.1 months (95%CI: 8.2−20.5) of those with no gain (p = 0.0003). At multivariate analysis, PSA decline ≥ 50% and AR CN showed a significant association with PFS  and OS . These findings indicate that the detection of circulating AR CN gain is a promising non-invasive biomarker for outcome prediction to enzalutamide treatment in CRPC patients.In the present study, we aimed to evaluate the association of circulating  However, hormone therapies lead to a transitory decrease in testosterone and dihydrotestosterone synthesis. Prostate cancer, especially in advanced disease, can only benefit temporarily from these treatments progressing to a castration-resistant prostate cancer (CRPC) status . Despiteval (OS) , 5. Moreval (OS) , 7. ThesAR in cfDNA, also thanks to the advancement in next generation sequencing technologies [AR copy number (CN) in cfDNA and the clinical outcome of metastatic CRPC patients treated with abiraterone [AR CN in cfDNA and resistance to enzalutamide in a cohort of 59 CRPC patients treated with enzalutamide after docetaxel.AR axis is one of the most important actors in acquiring resistance to androgen targeting therapies. It has been shown that studying specific AR-related alterations could represent an optimal chance for finding predictive markers . Circulanologies , 11. In raterone , 12. We AR CN. Median DNA concentration was 4.5 ng/μl (range: 2–23.7 ng/μl). AR gene was gained in 21 (36%) patients. Approach sensitivity was previously tested [AR CN gain . Nine out of 28 (32%) patients previously treated with abiraterone had AR gain. This frequency of AR gain was similar to the prevalence in patients who had not received abiraterone (39%) (p = 0.602), therefore the probability to find AR gain in patients with or without previous abiraterone treatment was the same (Table AR gain was similar in patients with response duration to androgen deprivation therapy less than 18 months (52%) vs. those with a response longer than 18 months .Fifty-nine patients with metastatic CRPC were treated with enzalutamide between August 2012 and November 2015 and serum samples were collected at starting of treatment. Serum DNA was extracted and was tested for AR gene gain and 14 of 38 (37%) without AR gene gain (p = 0.260). A PSA decline ≥ 90% was reported in 5 of the 59 (8%) patients, all cases without AR gene gain.A PSA decline ≥ 50% was reported in 18 of the 59 (31%) patients, 4 of 21 (19%) with AR gene gain was 2.4 months (95% CI: 1.9–3.2) vs. 4.0 months (95% CI: 3.0–6.5) of those with no CN variation (p = 0.0004)  compared to 14.1 months (95% CI: 8.2–20.5) for individuals with one copy of the gene (p = 0.0003)  and 39 patients had died. Median PFS and OS for the overall population were 3.5 months 95% CI: 2.7–4.4) and 9.4 months (95% CI: 7.8–14.1), respectively. The median PFS of patients with .7–4.4 anAR CN  and AR CN . We also analyzed the sensitivity and specificity of AR CN in PFS and OS , even if a bias due to the small sample size needs to be considered [This study highlighted nsidered , 16.AR gain to select patients for enzalutamide but requires repeating in cohorts of patients with different clinical history to confirm that the association with outcome is maintained. Interestingly, prior use of abiraterone was no associated with a higher prevalence of AR gain, in keeping with our previous study that reported no significant change in AR status in sequential samples to progression [Currently, the sequential treatment is a hot topic of CRPC because of increasingly availability of survival-prolonging agents arising the importance of patient selection to improve CRPC management. We here selected a cohort of patients who had all received docetaxel and, in 48% of cases, also abiraterone. As expected, we therefore observed a shorter PFS and OS and lower response rate than reported in the chemotherapy-naïve setting. Our study supported the use of AR CN gain clones and thus, reduce the difference we observed between AR gain and normal cancers. Overall, our observation may therefore be shown to be even more significant in future studies.To allow analysis of all our patients treated in early access program, we used serum samples. Due to concerns with increased leukocyte DNA release, we have more recently collected plasma for cfDNA studies. Increased levels of normal DNA could reduce our ability to detect AR CN variation at baseline. These evidences confirmed the lack of changes in AR status during hormonal treatment identified in circulating tumor DNA from 44 CRPC patients treated with abiraterone [The only univariate analysis for PFS and OS showed a significant inverse association with prior abiraterone treatment. This confirms the limited antitumor activity of enzalutamide following abiraterone in metastatic CRPC patients irrespective of prior docetaxel use . Howeverraterone .AR CN frequency was observed. Conversely, the univariate analysis revealed that the presence of visceral metastases had a significant negative effect only on OS as well as we have shown in our previous work of 256 CRPC patients treated with abiraterone after docetaxel [In addition, among clinical features of prostate cancer, the presence of visceral metastases has been evaluated in this paper: no correlation with ocetaxel .AR CN gain may benefit in terms of survival from chemotherapy and from other therapies that do not target AR signaling. Previously, several studies have already demonstrated that AR CN gain together with somatic point mutations were correlated with worse clinical outcome in CRPC patients [The small sample size, the retrospective nature of the analysis and the absence of an independent validation set represented the main limitations of our study. Consequently, our results should be considered preliminary and hypothesis-generating. It can be hypothesized that enzalutamide-resistant patients with patients , 12, 19.et al. [AR, ARV7, found in circulating tumor cells, was strongly associated with a poor outcome in patients treated with abiraterone or enzalutamide. We believe that also the evaluation of AR CN variation could have a similar important impact in guiding clinical decision in CRPC patients.Identifying clinical and molecular factors predictive of response to enzalutamide remains a high priority for future research. Recently, Antonarakys et al.  have demAR CN variation seems to be a biomarker potentially valid from an analytical point of view and pave the way for a further clinical validation in prospective studies on larger number of patients.In conclusion, Patients with metastatic CRPC without neuroendocrine differentiation in the primary tumor after at least one chemotherapeutic treatment including docetaxel and treated with enzalutamide were included in this retrospective evaluation in a cohort of patients collected prospectively to study. Additional selection criteria included Eastern Cooperative Oncology Group (ECOG) performance status ≤ 2; adequate cardiac, hepatic, renal and bone marrow function; serum potassium level ≥ 3.5 mmol/L; and ongoing androgen deprivation therapy with serum testosterone < 50 ng/dL. The protocol was approved by the Institutional Review Board. Written informed consent was obtained from all patients.Treatment consisted of enzalutamide 160 mg daily, that was given continuously until evidence of PD or unacceptable toxicity. Baseline serum PSA levels were typically measured in the week before starting enzalutamide treatment. All recorded PSA test and scan results were collected for these patients: PSA was usually evaluated every 4 weeks for serologic response and imaging investigations as clinically indicated. The Prostate Cancer Working Group 2 (PCWG2) criteria were used to define response and progression . However® ND-1000, Celbio, Milan, Italy). CN analyses were performed by a duplex TaqMan real-time PCR assay  for AR gene and 2 different reference genes: RNaseP (TaqMan Copy Number Reference Assay) and AGO1 (ID: Hs02320401). Samples analysis was performed as previously described [AR, the cutoff values were > 1.5 for amplification. Data were also analyzed by Digital PCR QuantStudio® 3D System. We performed an absolute quantification of target and reference gene for each sample.Serum DNA was extracted with QIAamp DNA Mini Kit . DNA was quantified by spectrophotometric evaluation .The association between CN gain and clinical outcome was evaluated by the Kaplan-Meier method and log-rank test. A Cox regression model was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for PFS and OS. The multivariable Cox models included all factors that were significantly associated in the univariate models. All


Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors’ experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data. AD generally causes a decline in cognitive tasks such as progressive loss of memory, reasoning and language. This disease mostly happens in the patients of the ages between 40s and 90s  such thaCurrent consensus statements have emphasised the need for the early diagnosis of AD . HoweverAs mentioned, the detailed diagnosis of AD is primarily based on clinical, neuropsychological and imaging evaluation. The choice for the modality of the brain imaging  depends on the duration and severity of the condition. Since MRI or CT scans cannot show brain tissue loss affecting the brain activity, SPECT, PET or fMRI is often exploited to detect the abnormalities for an early diagnosis , 6.fMRI is advantageous compared with PET modality owing to its higher spatial and temporal resolution. In fMRI, the brain activity may be explored during cognitive, sensory and motor task. Hence, it can be supposed as powerful means to identify disrupted neuronal circuits underlying disorders such as AD. Considering the cortices affected by AD, a ‘cognitive stress test’ can be used to bring out subtle brain abnormalities that would otherwise remain undetected during a resting state .During fMRI procedure, a brief focal neural activation evokes what is called a haemodynamic response function (HRF). The physiological processes underlying the blood-oxygenation-level-dependent (BOLD) signal and HRF, including neural metabolism, extracellular signalling, and cerebrovascular response are known to vary with age –11. The et al. investigated haemodynamic responses of the young and non-demented elderly adults using an event-related (ER) sensory–motor paradigm. In their study, the HRF has been simply extracted by averaging procedure. They found no significant difference for the HRF peaks between the young and non-demented elderly adults in either vision or motor region-of-interests (ROIs). In 15], AizeOIs). In , Huettel al. and  D'Esposiet al. reported, though cerebral blood flow response remains at the same level, but the BOLD response significantly intensifies for the old subjects in comparison with the young group. Then, the increase of BOLD peak with ageing was interpreted to be associated with a significant reduction in either the oxygen metabolism response or the neural activity. Using an ER sensory–motor paradigm and averaging scheme, in . Moreover, it is should be reminded that the noise contribution in fMRI data is much more critical for the elderly subjects [Trying to interpret the inconsistency of the results associated with prior works, in  Aizenstesubjects , 20, 21.t-test  as mentioned in Aizenstein et al. [It is still a research challenge. Moreover, there are contradictory findings reported in the literature. This work tries to deal with this challenge and to show some probable reasons for contradictory findings of earlier works. In this paper, it is proposed to use a different procedure for extracting HRF to avoid the above-mentioned problems. BOLD time series are here modelled as the product of convolution operation between an invariant HRF and an impulse train of stimuli . Then, tn et al. .Due to high intrinsic noise in the functional MR images and the weakness of the BOLD signal, the fMRI images suffer from low SNR , 30. The22.1Forty-one right-handed native English speakers took part by being paid $75. Fourteen young subjects consisting of five males between 18 and 24 years (mean 21.1 years) and without any history of neurological or visual illness. Twenty-seven older subjects were picked up out of the Washington University Alzheimer's Disease Research Center (ADRC) registry. The non-demented and demented were 14  and 13 subjects , respectively. All the elderly had normal visual legerity (corrected) and checked out regarding the neurologic, psychiatric or medical illnesses being associated with dementia.All the subjects either non-demented (control) or with demented subjects had an exam regarding the clinical dementia rating (CDR). Using CDR, the subjects with normal or mild dementia of the Alzheimer's type (DAT) were clinically categorised into: CDR0: no dementia; CDR0.5 and CDR1.0 relating to very mild and mild DAT, respectively . This me2.2A Power Macintosh computer  was used to control the stimulus display (PsyScope software) . A PsyScet al. [The task paradigm was basically composed as follows: (i) a visual stimulus was presented for 1.5 s; (ii) each subject then presses a key with his right index as soon as the stimulus begins. The visual stimulus included a checkerboard of 8 Hz counter phase flickering (black to white) that was subtending about 128 of visual angle  as used by Miezin et al. .The trials were carried out in two distinct conditions. The stimuli were presented either each one in isolation, or in pair come apart with 5.36 s as inter-trial interval. Every trial condition lasted for 21.44 s to accomplish an eight-image acquisition. Every subject performed 60 trials, in 4 runs, i.e. each run included 15 trials. Those two conditions were pseudo-randomly distributed and intermixed. Then, the evoked haemodynamic response could be obtained for an isolated and transient event .2.32. Each run collected 128 total images of sequential whole brain in four trials for each subject. Total four runs lasted 5.5 min with a 2 min delay between runs for each subject. The subjects got a rest during delay intervals.The MRI scans were acquired using a 1.5 T Vision System  system with a head coil being standard circularly polarised. The scan parameters were: 128 images with TR = 2.68 s, TE = 30 ms, 90° flip angle, 30 slices (8 mm slice thickness), and the slice plane resolution of 3.75 × 3.75 mm2.4To make sure about longitudinal magnetisation stabilisation, four initial volume images were discarded. Then, preprocessing steps were applied including correction of even/odd slice timing and realignment or motion correction (SPM software) . Next, tThe anatomical and functional images were coregistered using the standard MNI space for each subject in two-step registration as follows. First, mean functional image was obtained and aligned with the respective anatomical image. Then, the anatomical image was registered to the MNI template. Lastly, the related transformation matrices were concatenated. That concatenated transformation matrix were then applied to all the functional scans for registering to MNI standard template. The alignment was realised with 12 DOF as affine transformation in FSL package . FinallyA visual check was also carried out for all the subjects to make sure about the accuracy of ROI selection.2.52.5.1t-test analysis was performed on each subject's data. According to the neuronal activation mechanism, it is supposed that the HRF peak occurs around 4–6 s after the stimulus (finger tap), here at the third scan. Subtracting the first scan from the third one, the difference is used for activation detection. To maintain the compatibility with the previous work of Aizenstein et al. [t-values for studying HRF at each ROI. For ROI selection, the threshold t-value was independently determined for each individual subject. It should be reminded that the t-values of the remaining voxels have been negligible compared with the selected top-32 voxels according to the experimentations (i.e. the group of selected 32 voxels are significantly important). The activation detection was performed for both time series: original data and MDL-denoised data. According to the experimentations, a considerable part of voxels [80% of top-32 voxels . Then, the average of these mean MR signals was used as the HRF. The resulting HRF was considered with respect to baseline (scan 1) in per cent for standard presentation.2.5.3.X with the size of time series × length of HRF, filled initially out with zero.Suppose the matrix X so that a vector of unity with the length of HRF is placed in each row at the onset of stimuli.Change X, to account for estimating the baseline as a regressor to estimate the baseline.Add a column of unity at the ninth column of X and multiply the product with the BOLD time series.Estimate the HRF by calculating Moore–Penrose pseudo-inverse of matrix HRF is corrected with adding the baseline.BOLD time-series data are here modelled as the product of convolution operation between an invariant HRF and an impulse train of neural events . For eac2.5.4.As mentioned in the previous section, it is necessary to use an extra denoising with robust performance to sufficiently suppress the noise contributions because of low SNR of BOLD signal, particularly in the elderly , 30. ThiIn MDL method, a cost function is proposed so that the optimum threshold may be obtained by optimising this cost function. Thus, the complexity of thresholding may be automatically controlled. In this study, the denoising is applied to fMRI applications for extracting the HRF at the activated voxels. The denoising method and particularly the thresholding step plays an important role in determining the overall performance .2.5.5.ny = T is assumed to have an additive noise in general linear model (GLM), as follows:nɛ is often assumed to be Gaussian and nx is the desired noiseless time series . In fact, the main objective is to optimally extract the uncorrupted original signal nx. In GLM method, the original signal is supposed to be the activation coefficients multiplied by the design matrix. Since, it is here desired to denoise the fMRI signal before the HRF extraction using deconvolution operation, then, the design matrix has been developed and considered in accordance with the input impulse train (stimuli vector). GLM method generally invokes the convolution operation in the matrix form (design matrix). Given the orthonormal regression matrix W, the DWT of the noisy data is defined asA represents the set of coefficients with length n. The observed time series, aneously –53. The aneously , 36. The follows –56:(3)\k and n − k members. First group of coefficients are considered for reconstruction and the remaining n − k are set to zero. In C-MDL, a subset γ including the k largest wavelet coefficients are selected to minimise the following cost function [S(ny) and γS(ny) stand for the sum squares of all wavelet coefficients and the coefficients belonging to γ, respectively. The number of coefficients in γ is equal to k and n is the length of time series. The upper limit for k always meets k < 0.95n [For MDL-based denoising, the wavelet coefficients are categorised into two groups of function (4)\docu < 0.95n .n possible conditions to select. The encoding of the class index affects so much on the performance of fMRI data denoising [In R-MDL as discussed in , the cosenoising .33.1t-test for selecting activated voxels. The activated voxels have been selected as shown in Fig. The effects of denoising fMRI data are here studied on the selection of activated voxels. The noise contribution gets larger and more important through ageing , 20, 21.In both vision and motor regions, the initial connected regions of activated voxels (without applying MDL denoising) have been extended for both the non-demented elderly adults and the young cases after applying MDL denoising. In the non-demented elderly, new connected sub-regions or spots have nonetheless appeared as well. It may be referred to the lower SNR of fMRI data in the non-demented elderly adults (0.35) compared with the young (0.52) , 21. HowTo better evaluate the MDL denoising process, the activated voxels associated with the top32 correlation values are compared without and with applying MDL denoising. Table Table 3.2The HRF is here estimated from the fMRI volume images. To better study the noise effects, the estimation has been here applied to the fMRI volume images before and after applying MDL denoising.3.2.1.2 demand etc.). Then, it may be imagined that the HRF peaks can indirectly demonstrate the vascular time constants. In the averaging method, the results show no significant difference in HRF peaks between the young and the elderly adults in both vision and motor regions (p > 0.1).All previous studies –19 triedp < 0.05). In vision cortex, the HRF peaks are significantly larger for the young compared with the elderly adults (non-demented and demented).Considering the fMRI data without MDL denoising, in contrast with averaging method, the HRF amplitude peaks appear to be significantly different in vision region between the young and the elderly adults (3.2.2.The effects of noise are here studied on the HRF estimation to compare the HRF extraction methods of averaging (used in earlier works) and deconvolution (this work). Fig. a/1b and 2c/1d are associated with the averaging and the deconvolution techniques, respectively. In the case of averaging algorithm, no significant difference at the peak amplitudes is observed between the elderly adults (non-demented and demented) and the young for both motor and vision regions.Figs. 2c and d demonstrate the mean HRF extracted by deconvolution after applying MDL denoising for vision and motor ROIs, respectively. A significant difference is not obtained for motor and vision regions either. It may be seen that the results after applying MDL denoising is different with the one found in the earlier subsection for the case of fMRI data without applying MDL denoising. The noise contribution gets larger and more important through age increasing [Figs. 2creasing , 20, 21.4In the step of HRF estimation, deconvolution appears to be much more robust against noise contribution because the related haemodynamic function is, at the same time, optimised through the whole time series scans. Consequently, the averaging method yields contradictory results for HRF peak amplitudes as reported in the previous works Table . Conside5In this paper, a comparative study has been realised on the HRF peaks amplitudes being compared between the young and the elderly adults (non-demented and demented old populations) assuming simple visual and motor tasks. It was shown that the previous works led to conflicting results on the HRF peak amplitudes because of averaging algorithm as well as noise contributions in fMRI data. In fact, fMRI data are heavily noisy particularly in the elderly adults. Accordingly, it is necessary to employ an HRF estimation algorithm which is sufficiently robust against noise. Employing the robust MDL-based wavelet denoising, it was shown that a large per cent of the selected voxels changed in the activation detection at both motor and visual cortices. According to the experimentation results, the area of activated voxels is extended for both the elderly adults and the young. In the step of HRF estimation, deconvolution appears to be much more robust against noise contribution. Considering deconvolution method, it may be concluded that the HRF peak amplitudes exhibit no significant difference in the visual and motor cortices between the elderly adults and the young.