Aims and method A retrospective evaluation was undertaken of the clinical and economic effectiveness of three in-patient rehabilitation units across one London National Health Service trust. Information on admission days and costs 2 years before and 2 years after the rehabilitation placement, length of rehabilitation placement and the discharge pathway was collected on 22 service users.Results There were statistically significant reductions in hospital admission days in the 2 years following rehabilitation compared with the 2 years before, further reflected in significantly lower bed costs. Longer length of rehabilitation placement was correlated with fewer admission days after the placement. A substantial proportion of the sample went into more independent living, some with no further admissions at follow-up.Clinical implications The findings suggest that in-patient rehabilitation is both clinically and cost effective: if benefits are sustained they will offset the cost of the rehabilitation placement. National policy on appropriate levels of in-patient rehabilitation provision has been lacking,3 although the majority of mental health trusts provide such care.4 The National Service Framework for Mental Health5 focused on specialist community services, and a relative reduction of rehabilitation services has followed its publication.7Mental health rehabilitation services work with ‘low volume, high need’ individuals8 creating competition for ever-reduced resources.9 There are specific dangers for rehabilitation services in this context: a lack of clear governmental policy support and a limited – though generally positive – scientific evidence base could result in a loss of money to competing services. Commissioners' knowledge of the role and effectiveness of rehabilitation is uncertain, although specific guidance to assist commissioning has been produced.1Mental health services in the UK are facing the uncertain challenges of moving to clinical commissioning groups, with the parallel drive to evidence outcomes (or at least activity) through payment by results. This must be further filtered through the reality of a mandated £20 billion in efficiency savings in the National Health Service (NHS) by 2014,In this study, we explored objective clinical (bed use), economic (bed costs) and psychosocial functioning outcome data in three in-patient rehabilitation units across a single NHS mental health trust. We hypothesised that there would be measurable improvements in individuals' lives provided in a cost-effective manner, and that data could help inform commissioning decisions about rehabilitation service provision.The study retrospectively explored clinical and costing markers in 22 individuals sequentially discharged from three in-patient rehabilitation units in a single NHS trust in the 2 years prior, the time during and the 2 years after rehabilitation care.10 providing a total of 46 placements. They each provide 24-hour nursing care, offer a range of professional inputs and can accommodate patients detained under the Mental Health Act 1983.Within a wider rehabilitation service, Oxleas NHS Foundation Trust has three in-patient rehabilitation units, one in each of three London boroughs that encompass both inner and outer London ,n = 14), closed rehabilitation units (n = 2), forensic in-patient units (n = 2) and a private hospital (n = 1). Three people were referred directly from the community, although these were more ‘intensive’ community services rather than standard community mental health teams. Mean age was 49 years . All had a primary psychotic diagnosis, with paranoid schizophrenia most common (n = 13); 12 had at least one secondary diagnosis and these included substance misuse (n = 4), intellectual disability (n = 4), personality disorder (n = 3) and an anxiety disorder (n = 2). Twelve patients were detained under Section 3 of the Mental Health Act.The sample was drawn from the 24 individuals who were discharged from the three units between 1 October 2009 and 30 September 2010: two patients were excluded as they had spent fewer than 6 weeks on a unit, leaving a sample of 22. The mean length of admission was 701 days . Referrals came from acute psychiatric wards , risk and meaningful social activities in the 2 years before and 2 years after the placement, length of stay in the rehabilitation unit, and the pathway from referral to discharge. Information was obtained from the trust's electronic record system, RiO: where information prior to RiO's implementation in 2006 was needed, this was taken from paper notes. Differences in bed occupancy and costs were assessed for significance using paired t(21) = 3.052, P = 0.006)  .Fitting with a reduction in admission days, the costs post-rehabilitation were statistically significantly lower than the costs of admissions pre-rehabilitation . Linear v. 701 days). Of this subgroup four were discharged to residential care, one to an independent flat, and three had no admissions in the follow-up period.The five individuals who had been in continuous admission for the 2 years before rehabilitation remained in rehabilitation care longer than average : these were the only 2 patients in the sample who went into continuous in-patient care and, speculatively, the shorter duration of input may represent a clinical recognition that the rehabilitation programme was not going to progress effectively.Pre-rehabilitation there was no information available in the notes for three people regarding their functioning, and for ten others, despite detailed information being available, there was no evidence of engagement in regular daytime activities. Of the remainder, where there was evidence of regular activities: three were having regular contact with family members, five were attending ward groups or occupational therapy activities and one was doing voluntary work. Greater levels of activity were recorded in the period post-rehabilitation, with only three patients having no evidence of any form of meaningful activity. Ten were engaged in community groups, three were service user representatives, two were engaged with ward occupational therapy, two had re-established meaningful links with their families, one was at college, and one was undertaking voluntary work. Quite a few were doing more than one activity; for the purposes of this report, we documented what we deemed to be each person's highest level of achievement.Most of the sample had complex risk histories: eight had a forensic history including firesetting, assaults, actual bodily harm, carrying weapons and shoplifting; five had a history of substance misuse; nine had a history of substantial acts of self-harm or suicide attempts; and almost all had some degree of self-neglect. Sixteen of the 22 individuals had one or two, and 3 individuals had three to five major risk incidents  in the 2 years preceding their rehabilitation placement. Post-rehabilitation half the sample had no major risk incidents in the follow-up period, five had one major risk incident, four had two such incidents, and two individuals had four major risk events.11 and the rationale for input may include treatment resistance, comorbidities such as neurodevelopmental disorders and substance misuse, behavioural disturbances, and an inability to effect discharge from an acute ward.12 It has traditionally been argued that the majority of rehabilitation patients are not hard to engage despite their needs7 – unlike a typical assertive outreach cohort – and under current UK NHS criteria will usually fall under payment by results ‘cluster 13’. However, this assertion might be challenged given the general reduction in in-patient beds and often in the provision of assertive outreach services, and rehabilitation case-loads might be changing with time. The first national survey of 133 rehabilitation units in England11 found a mean of 14 beds per unit and 16 admissions in the previous year. Median service user characteristics included an age of 40, an admission of 18 months' duration, a 13-year history of contact with mental health services and 4 previous in-patient admissions.A limited evidence base exists for in-patient rehabilitation services. About 80% of rehabilitation services users have a primary diagnosis of psychosis,13 demonstrated positive outcomes of greater independence, fewer episodes of aggression and problematic behaviour from a rehabilitative approach. Participants showed significant gains in skills despite persisting or worsening symptoms but a ‘slow-stream’ approach was required, with improvements taking over a year to manifest. Killaspy & Zis14 analysed clinical outcome data on 141 users of an inner London rehabilitation service retrospectively: 40% had positive outcomes 5 years after initial assessment – defined as achieving and sustaining a less supported placement – with 10% moving to independent accommodation and sustaining a tenancy, and 27% had unchanged support. Adherence to medication was the most significant factor positively affecting outcomes at 5 years, although, conflicting with the finding of Bredski et al,15 a longer time from first contact with mental health services to contact with rehabilitation services was also associated with better outcomes. In the national study of rehabilitation units in England11 positive scores in the seven domains measured by the Quality Indicator for Rehabilitative Care (QuIRC)16 were associated with better subjective experiences of care and the therapeutic environment.A 5-year prospective follow-up of 72 individuals ‘difficult to place’ following a hospital closureet al15 demonstrated that serious self-harm, high-dose and polypharmacy antipsychotic use, past forensic input and longer past duration of hospital admission(s) were significantly associated with non-discharge. Killaspy et al11 showed that older patient age was significantly associated with worse outcomes. This national survey identified delayed discharges in 14% of rehabilitation patients, whereas a lack of suitable move-on accommodation – particularly from lower intensity community units – was noted in another, ten-unit survey by Cowan et al:17 over half of the patients had no clear community team follow-up. A lack of appropriate follow-up for those with high levels of disability, a quarter of whom ended up under the care of assertive outreach teams, was identified as a gap.A case–control study  by Bredski 13 concept that rehabilitation care takes time to effect change. It was particularly pleasing to see that a number of people who had spent extended periods of their lives in hospital were able to move into the community and maintain this progress in the follow-up period, and that 16 of the sample had no admissions during this time. From a service perspective, this led to cost savings on hospital admissions of around £48 000 a year per individual treated in the follow-up period, compared with the 2 years pre-rehabilitation. This of course must be weighed against the cost of the rehabilitation placement: in these specific units this would be recouped within 3.5 years of discharge if such gains were sustained, although we do not yet have such longer-term clinical data.Our data indicate that the majority of people using the three evaluated rehabilitation units appeared to derive significant benefit in terms of increased stability, with fewer hospital admissions and the ability to live in more independent settings. The duration of rehabilitation placement varied, tailored to individual need rather than specific time scales, with a considerable range in duration, although the majority moved through the units in well under 2 years. Interestingly, a greater duration of rehabilitation input was significantly associated with better outcomes as measured by readmission data. Although on one level this might appear intuitive as a proxy marker of quanta of clinical input, it is also reasonable to consider that those least well and most in need would necessitate the greatest input and potentially have worse outcomes – though as noted, two individuals who were transferred to continuous in-patient care had such a move made relatively early. These data thus fit with Trieman & Leff's14 in terms of positive outcomes following rehabilitation care, although that larger study focused more on the nature of placement after rehabilitation rather than admission days and costs, and it was powered to evaluate factors correlated with outcome such as medication adherence.It was harder to determine the secondary measures of whether people had been able to pursue other personally meaningful goals and aspirations, and to reliably assay their risk profile: the sample size was small and validated scales were not utilised. Overall, the findings are concordant with the somewhat similarly designed retrospective work by Killaspy & ZisA major limitation of this work is the lack of a control group: although the data show reduced readmissions and related cost savings, as well as improvements in psychosocial functioning and reduction in risk, it remains possible that such gains would have occurred without the rehabilitation-specific input. A randomised control trial would be necessary to determine causality. Hospital admission and cost are clearly directly related factors, although we have reported them separately, and the former is only an indirect marker of clinical state. However, particularly in the current economic climate, much service-related research has neglected the issues of costings, and we believe our model could be applied to parallel services such as assertive outreach, early intervention and crisis teams, to assess their cost-effectiveness over time. Nevertheless, the opportunity cost of standard community ‘treatment as usual’ is not factored into the overall costs in this current work.Data collection was retrospective and analysis of psychosocial functioning and risk was descriptive rather than through the use of objective and validated scales. Linear regression analysis did not identify age or gender as predictors of bed days or costs, but the sample size was too small to allow any meaningful attempt to evaluate clinical or demographic predictors of outcome and caution should be exercised in the interpretation of this negative finding. However, this was not a primary study aim, and we note the general lack of such markers both in mental health rehabilitation and more general psychosis studies to date. Finally, although this study covered three boroughs, encompassing both inner and outer London, we recognise this is from a single NHS trust in one city, which may limit the generalisability of the results.19 but have suffered in recent years in relation to specialist community teams. Assertive outreach, early intervention and crisis teams are themselves open to challenge about their evidence base and effectiveness,20 but they appear in favour with service developers and national drivers for change, and might be perceived as producing more active research. Rehabilitation might be relatively ‘unfashionable’,3 but if this is the case then the rehabilitation psychiatry subspecialty must accept some responsibility for this. Rehabilitation has historically been an under-researched branch of mental health,17 despite the cost of such services21 and the obvious challenge of severe and enduring mental distress.23 Killaspy et al's4 description of the field as an ‘evidence free zone’ remains, if no longer wholly accurate, nevertheless still apposite a decade after the statement was made.Rehabilitation services evolved as a development of the broader process of deinstitutionalisation and the end of the asylum era,16 and the large multi-site Rehabilitation Effectiveness for Activities for Life (REAL) study in progress.24 Some of the difficulties in identifying demographic or clinical predictors of outcome are common to the larger field of psychosis research,26 but the evidence that does exist is broadly favourable in terms of rehabilitation input producing positive outcomes.In the face of clinical commissioning groups and payment by results, continuing practice as usual is not a valid proposition for any branch of mental health, and recent history augurs badly for rehabilitation without active, ongoing self-evaluation to justify continuance – let alone increase – of funding. However, there are positives such as the development of the QuIRC toolkit29 neuroimaging31 and nascent genetics work,32 to positive results for individual cognitive–behavioural therapy33 and family systems approaches,34 sometimes without psychotropic medication. However, excitement about these assessment and therapeutic tools can be juxtaposed with the reality that despite advances in treatments and knowledge, psychosocial disability remains as much a problem now as it did half a century ago, and many diagnosed with psychosis continue to struggle to live satisfying and meaningful lives.35 Although learning to place the lived experience of the patient at the heart of our thinking is perhaps a more recent concept, the recovery philosophy has become a guiding principle for mental health services.37 Of all fields of mental health practice, the rehabilitation tradition may have the longest convergence with the recovery model, supporting people who face the greatest mental health challenges in establishing meaningful lives.38 We believe our data support the evidence for rehabilitation services, which continue to be neglected in policy and local planning. Rehabilitation embodies the humanistic heart of mental health: it has, perhaps, just been too silent for too long. The arrival of clinical commissioning groups and payment by results could present an opportunity to help change this.Psychosis research presents new developments in many areas, from new pharmacotherapeutics,


P<0.01), especially in maintenance phase. Contrary to the previous reports, obesity was not associated with L-asparaginase-related hyperglycemia. These findings suggest that protracted administration of L-asparaginase is the risk factor for hyperglycemia when treating adolescent and young adult acute lymphoblastic leukemia patients.Although L-asparaginase related hyperglycemia is well known adverse event, it is not studied whether the profile of this adverse event is affected by intensification of L-asparaginase administration. Here, we analyzed the profile of L-asparaginase related hyperglycemia in a 1,176 patients with pediatric acute lymphoblastic leukemia treated according to the Japan Association of Childhood Leukemia Study ALL-02 protocol using protracted L-asparaginase administration in maintenance phase. We determined that a total of 75 L-asparaginase related hyperglycemia events occurred in 69 patients. Although 17 events  developed in induction phase, which was lower incidence than those (10–15%) in previous reports, 45 events developed during the maintenance phase with protracted L-asparaginase administration. Multivariate analysis showed that older age at onset (≥10 years) was a sole independent risk factor for L-asparaginase-related hyperglycemia ( The majority of current pediatric acute lymphoblastic leukemia (ALL) cases have a high long-term survival rate due to the recent advances in treatment using multiple chemotherapeutic agents including L-asparaginase (L-asp) . Since HThe written informed consent was obtained from the patients’ guardians according to the Declaration of Helsinki, and the treatment protocols were approved by the institutional review boards of National Hospital Organization Nagoya Medical Center and the participating institutes.. Seventy-six patients were removed from the analysis due to the reasons described in  The characteristics of the remaining 1,176 patients  were then analyzed  with newly diagnosed ALL were enrolled in the JACLS ALL-02 study from April 2002 to May 2008 , 18. The years wiTables A-E in S1 File. The naïve E. coli L-asp preparation, i.e., Leunase  was used because PEG asparaginase has not been approved in Japan. Generally, L-asp was administered with prednisolone (PSL) (40 mg/m2), not dexamethasone (DEX), in this protocol. Protracted administration of L-asp in maintenance phase was employed in HR, ER and T-ALL protocol.The L-asp administration schedule according to the JACLS ALL-02 protocol is summarized in Fig 1A–1C). The relationship between obesity and L-asp-related hyperglycemia was analyzed in 1,104 patients only due to a lack of height and weight data for 72 patients.During the protocol study, adverse events were assessed according to National Cancer Institute (NCI)-Common Terminology Criteria for Adverse Events (CTCAE) version 2.0 and reported to the Data Center. L-asp-related hyperglycemia was defined as grade 3 or 4 hyperglycemia (serum glucose>250 mg/dl) occurring during L-asp treatment. Hyperglycemia due to L-asp-related pancreatitis was excluded. The risk factors for L-asp-related hyperglycemia were analyzed during the entire protocol: the induction phase, the re-induction phase, and the maintenance phase for HR, ER and T-ALL patients (Table F in S1 File).The characteristics of the 72 patients who lacked data did not differ from those of the remaining patients except for leukocyte count at onset and central nervous system status (2), BMI percentile ), and obesity index . BMI was calculated as follows: body weight (kg)/{height (m)}2. The exclusive software  was used to calculate BMIp. OI was calculated using the formulae provided by the Japanese Society for Paediatric Endocrinology (Table G in S1 File). The obesity rate according to the age group in this cohort is shown in S2 Fig.Obesity was evaluated according to body mass index  were included in the final model; results were reported using adjusted odds ratios with 95% confidence intervals (95% CIs). Other comparisons were performed using the Table 2. Sixty-nine of 1,176 (5.9%) patients experienced L-asp-related hyperglycemia and the hyperglycemic patients were significantly older . Sex, initial WBC counts, immunophenotype and the presence of extramedullary disease were not statistically different between two groups. Down syndrome tends to be more in hyperglycemic patients, although it is not statistically significant (P = 0.05). A total of 75 L-asp related hyperglycemia events occurred in 69 patients. The grade, therapeutic phase, and risk group of the 75 events are summarized in Table H in S1 File. Fifty and 25 events were classified as grade 3 and grade 4 hyperglycemia, respectively. Seventeen (22.7%) events developed during the induction phase, 11 (14.7%) during the re-induction phase, 45 (60%) during the maintenance phase, and two during other phases. Forty-seven of 75 events (62.7%) occurred in the HR group treated with protracted administration of L-asp in maintenance phase. Three of the 69 patients (two HR and one T) switched to a protocol that did not contain L-asp because of severe hyperglycemia that developed during the maintenance phase.The clinical characteristics of the patients who developed L-asp-related hyperglycemia are summarized in 2), higher BMIp (≥85%), higher OI (≥20%) and Down syndrome , which are consistent with previous reports [Table 3). The analysis also showed that older age (older than 10 and 15 years) and higher BMI (≥22 kg/m2) correlated with L-asp-related hyperglycemia during the induction phase (P<0.05). Type of protocol was not associated with L-asp related hyperglycemia in induction phase just because the dose and schedule of L-asp administration was the same in all risk groups. Univariate analysis also showed that older age (older 5 and 10 years), female patient, higher BMI, and higher OI also correlated with hyperglycemia during the maintenance phase (P<0.05), but did not show any significant risk factors related to L-asp-related hyperglycemia in the re-induction phase, partly because the number of events (n = 11) was small. Multivariate analysis revealed that older age (older than10 years) was associated with L-asp-related hyperglycemia during the entire treatment and the maintenance phase. However, BMI (≥22 kg/m2) was not associated with L-asp-related hyperglycemia (Table 4). These findings suggested that protracted administration of L-asp with PSL was risk factor of hyperglycemia in the patients older than 10 years irrespective of obesity. On the other hand, either older age or higher BMI was not associated with L-asp-related hyperglycemia in induction phase, partly because the number of events was small (n = 17) (Table 4). However, 14 of 17 patients were older than 10 years and only two of 17 patients showed higher BMI.Univariate analysis revealed that L-asp-related hyperglycemia correlated with older age , type of protocol (HR and ER), higher BMI .To evaluate the prognostic significance of L-asp-related hyperglycemia, we performed univariate and multivariate analyses of HR patients (n = 416). Univariate analysis revealed that older age (≥10) and development of L-asp-related hyperglycemia during the maintenance phase were associated with poorer event-free survival (EFS). However, multivariate analysis revealed that only older age (≥10) was associated with poor EFS (Previous studies of pediatric ALL patients show that older age (≥10 years) at disease onset and obesity are risk factors for L-asp-related hyperglycemia, especially during the induction phase , 19, 20.Table 4). In our protocol, none of the SR patients (who did not receive L-asp during the maintenance phase) were older than 10 years-of-age. Thus, the high incidence of L-asp-related hyperglycemia in older patients may be associated with prolonged use of L-asp combined with prednisolone during the maintenance phase (Tables A-E in S1 File), as 45 of 75 events (60%) occurred during this phase (Table H in S1 File). Accordingly, multivariate analysis identified a strong correlation between older age (≥10 years) and L-asp-related hyperglycemia during the maintenance phase . Because protracted administration of L-asp in maintenance phase was not popular in other pediatric ALL protocols, hyperglycemia in maintenance phase seemed to be peculiar in our protocol. Thus, current national protocol for pediatric ALL does not employ protracted administration of L-asp in maintenance phase in Japan. On the other hand, the overall incidence of L-asp-related hyperglycemia during the induction phase was 1.4% , which was lower than those in the previous reports (10–15%) [2) use and no administration of DEX might explain the low frequency of L-asp related hyperglycemia in induction phase in this study.Here we analyzed 1,176 pediatric ALL patients treated according to the JACLS ALL-02 protocol. This is the largest cohort in which L-asp-related hyperglycemia has been investigated. We found that L-asp-related hyperglycemia occurred in 69 of 1,176 (5.9%) patients. Multivariate analysis identified older age (≥10 years) as the sole independent risk factor for L-asp-related hyperglycemia. The unique finding of this study was that obesity was not an independent risk factor  ,20. RelaTables 2). Thus, the small number of the obese patients might cause different result from those in previous studies. Considering insulin resistance is deeply associated with L-asp-related hyperglycemia, the limitation of this study is that we could not evaluate serum biomarker of insulin resistance such as leptin, adiponectin, GH and IGF1. Because the age or BMI is just surrogate marker of insulin resistance, we should use more reliable biomarker(s) to evaluate personal insulin resistance in these studies.L-asp inhibits insulin synthesis by the pancreatic β cells, and L-asp-related hyperglycemia may occur readily in patients with insulin resistance . InsulinPrevious studies demonstrate that older age is a risk factor for various adverse events associated with L-asp treatment for pediatric ALL, including pancreatitis, thrombotic complications, hypertriglyceridemia, and hyperglycemia , 29. HowTable 5). The excellent supportive care in our study ensured that most of the patients  completed the protocol. A univariate analysis performed by Sonabend et al showed that pediatric ALL patients with overt hyperglycemia (serum glucose > 200 mg/dl) had a 6.2 times greater risk of death [Finally, we found that multivariate analysis revealed that L-asp-related hyperglycemia did not correlate with poor prognosis  as the sole independent risk factor, suggesting that prolonged administration of L-asp combined with prednisolone may cause increased hyperglycemia in patients ≥10 years-of-age due to age-related insulin resistance. However, obesity  was not correlated with L-asp-related hyperglycemia. These findings suggest that careful monitoring of hyperglycemia is essential when treating AYA with ALL, irrespective of obesity, using a pediatric protocol that includes intensive L-asp therapy.S1 FigALL, acute lymphoblastic leukaemia; AUL, Acute undifferentiated leukaemia; CNS, central nervous system; ER, extremely high risk; F, induction failure; HR, high risk; IT, intrathecal therapy; M1, blasts < 5% in bone marrow; M2, 5% ≤ blasts < 25% in bone marrow; M3, blasts ≥ 25% in bone marrow; MTX, methotrexate; PGR, PSL good response; Ph-ALL, Philadelphia chromosome )-positive ALL; PPR, PSL poor response; PSL, prednisolone; SR, standard risk; T T cell type ALL; WBC, white blood cell.(TIF)Click here for additional data file.S2 FigTotal number is 1,104, which has the data of height and weight. The rate of obesity is roughly around 10%, and at the most 20%. BMI, body mass index, BMIp, BMI percentile; n, number; OI, obesity index.(TIF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Moderate-to-severe allergic rhinitis (AR) is often poorly controlled. Patients remain symptomatic on treatment, despite multiple therapies. A more effective treatment is needed. We assessed the efficacy of MP29-02*  and fluticasone propionate (FP) in an advanced delivery system) during different seasons and for different symptoms and severities vs AZE, FP or placebo (PLA).Four thousand and twenty two moderate/severe SAR patients (≥12 yrs) were randomized into 4 double-blind, PLA-controlled, 14-day, parallel-group trials to MP29-02*, AZE, FP or PLA nasal sprays (1 spray/nostril bid), during the Texas mountain cedar (MP4001), Spring (MP4002), Autumn (MP4004) and Spring/Summer (MP4006) seasons. Overall change from baseline (CFB) in reflective total nasal symptom score (rTNSS) was the primary endpoint. It was assessed by severity post-hoc in 2 ways . CFB in individual nasal and ocular symptom scores was also assessed.The response to MP29-02* was consistent across seasons; mean CFB -5.5, -5.5, -5.6 and -5.6 in each study (p<0.001 vs PLA). Nasal symptom relief was significantly greater with MP29-02* than with FP or AZE in all studies. In study MP4001 MP29-02 was approx. twice as effective as FP (relative difference (RD) 47%; p=0.0031) and 3 times as effective as AZE . For less severe AR (defined by median baseline rTNSS) the RD to MP29-02* was 42% vs FP (p=0.0188) and 64% vs AZE (p=0.0002), increasing to 49% and 70% vs FP (p=0.0436) and AZE (p=0.0035), respectively for more severe AR. When severity was categorized according to median baseline RQLQ the RD to MP29-02* was 60% vs FP (p=0.0244) and 69% vs AZE (p=0.0068) for those with less severe AR compared to 49% vs FP (p=0.0194) and 64% vs AZE (p=0.0013) for those with more severe AR. MP29-02* provided superior relief from all nasal and ocular symptoms than AZE or FP, which was particularly evident for congestion  & ocular itching .MP29-02* provides consistently superior symptomatic relief to an intranasal antihistamine or topical corticosteroid in AR patients regardless of season, symptom or severity, supporting MP29-02 as the drug of choice for AR.*Dymista


This study was conducted to assess the relation between allergic rhinitis (AR), acute rhinosinusitis (ARS) and chronic rhinosinusitis (CRS) and environment, comorbidity and ethnicity.A posted GA2LEN screening questionnaire was sent to all those in a random sample of Dutch population (n=16700) in three different areas.The prevalence of ARS is significantly related to AR, a doctor’s diagnosis of CRS, urticaria, eczema, smoking, gender, ethnicity and age. The prevalence of CRS is significantly related to AR, a doctor’s diagnosis of CRS, urticaria, adverse response to painkiller, smoking, ethnicity, asthma and age. The prevalence of AR is significantly related to a doctor’s diagnosis of CRS, urticaria, eczema, adverse response to painkillers, smoking, occupation, ethnicity, asthma, age, CRS and ARSSome environmental factors, comorbidity and ethnicity are positively or negatively related to AR, ARS and CRS. Place of residence in the Netherlands is not related to the prevalence of these diseases.


A hormone released from the gut after a meal can reset clock gene activity in the liver. Related research article Landgraf D, Tsang AH, Leliavski A, Koch CE, Barclay JL, Drucker DJ, Oster H. 2015. Oxyntomodulin regulates resetting of the liver circadian clock by food. eLife4:e06253. doi: 10.7554/eLife.06253Image When and what we eat can reset our body clocksAll forms of life have to be able to cope with changes in their environment, including daily cycles in temperature and light levels. As a result, organisms as diverse as bacteria and humans have evolved inbuilt timekeeping mechanisms that are capable of tracking the 24-hour day.eLife, Henrik Oster and colleagues—including Dominic Landgraf and Anthony Tsang as joint first authors—report how a hormone released from the gut after eating can help the body to track changes in mealtimes  with the world around them. To achieve this, circadian clocks can be influenced or reset by environmental factors called ‘zeitgebers’ (from the German for ‘time-giver’). The daily cycle of light and dark is a dominant zeitgeber for most organisms, while periodic food availability represents another powerful zeitgeber. Now, in ealtimes .Circadian clocks have a profound impact on mammalian biology, and virtually all aspects of our lives—from sleep-wake cycles to patterns of hormone release and energy metabolism—follow pronounced daily rhythms . In mammPer1 and Per2, within neurons of the SCN is increased in response to light. This means that the SCN clock can be advanced or delayed to ensure it remains in time with the prevailing light–dark cycle. However, it has also become clear that there are other circadian clocks in most of the cells and tissues in the body , with the expression of the clock genes in these tissues become aligned to feeding time . In contUnlike modulation of the SCN clock by light, it is unclear which signals convey information about feeding time to reset the circadian clock in the liver. Oster, Landgraf, Tsang and colleagues—who are based at the Max Planck Institute for Biophysical Chemistry, and the Universities of Lübeck and Toronto—report that a gut hormone called oxyntomodulin is one of these signals. Oxyntomodulin is a peptide hormone that is released from the gut in response to food intake, and has been suggested to be a potential drug target to combat obesity in humans .Landgraf, Tsang et al. started by screening around 200 peptide molecules that are known to be involved in appetite and the regulation of body weight to see if any could adjust the molecular clock of liver tissue. This in vitro screen identified two molecules: oxyntomodulin and glucagon. In particular, treatment with oxyntomodulin could shift the liver clock by several hours, either forward or back, depending on the time it was administered. Both of these characteristics suggest that oxyntomodulin serves to set the liver clock to feeding rhythms in living organisms.Per1 clock gene in the liver cells, which involves a signalling cascade that is reminiscent of the light-induced responses of the SCN (So how does oxyntomodulin reset the liver clock? Somewhat unusually, this hormone can bind to two different types of receptor protein (namely GLP-1 receptors and glucagon receptors;  the SCN .Per expression in the liver, and experienced a shift in the timing of their liver clock. Treatment with oxyntomodulin also delayed and/or reduced the expression of genes involved in carbohydrate metabolism. However, treating the mice with oxyntomodulin during the period when they were normally active had no effect on the liver. Thus, oxyntomodulin's effects on the liver clock only occur at times of the daily cycle when the mice do not typically eat. Furthermore, oxyntomodulin had no effect the SCN master clock.Mice that were given oxyntomodulin when they would normally be resting showed an increase in These experiments only demonstrate that artificially elevated levels of oxyntomodulin alter liver activity, so Landgraf, Tsang et al. then explored whether a mouse's normal levels of oxyntomodulin act to regulate its liver clock. When fasted animals were then given the opportunity to eat, oxyntomodulin levels in the blood increased within 20 minutes, and remained high for one hour. This signal was enough to alter clock gene activity in the liver. Landgraf, Tsang et al. then treated mice with antibodies that bind to oxyntomodulin. This treatment neutralized the hormone circulating in the body, and prevented its action, which weakened the resetting of the liver clock following food intake.Landgraf, Tsang et al. suggest that the oxynto-modulin released by the gut following a meal serves as a timing cue for the liver clock. When food intake occurs at the expected times of day, the liver clock is relatively blind to this signal. However, when a meal occurs outside of a normal feeding time, the induced rise in oxyntomodulin serves to adjust the clock to a new feeding schedule. In this way, oxyntomodulin may function as an important signal that translates feeding time into the timing of an internal clock.It will be important to examine oxyntomodulin signalling in situations were food is limited, and to explore whether it can also shift the clocks in other peripheral tissues, such as muscles and the pancreas. Furthermore, the therapeutic potential of this peptide hormone in correcting problems associated with ‘jet-lag’ and rapid travel through multiple time zones also awaits investigation.


However, it is not known whether β3-AR plays cardioprotective effects against myocardial infarction (MI) injury. Therefore, the present study was designed to determine the effects of β3-AR on MI injury and to elucidate the underlying mechanism. MI model was constructed by left anterior descending (LAD) artery ligation. Animals were administrated with β3-AR agonist BRL37344 (BRL) or β3-AR inhibitor SR59230A (SR) respectively at 0.1 mg/kg/hour one day after MI operation. The scar area, cardiac function and the apoptosis of myocardial were assessed by Masson's trichrome stain, echocardiography and TUNEL assay respectively. Western blot analysis was performed to elucidate the expressions of target proteins. β3-AR activation with BRL administration significantly attenuated fibrosis and decreased scar area after MI. Moreover, BRL also preserved heart function, and reduced the apoptosis of cardiomyocyte induced by MI. Furthermore, BRL treatment altered the phosphorylation status of endothelial NOS (eNOS) and increased the expression of neuronal NOS (nNOS). These results suggested that β3-AR stimulation has a substantial effect on recovery of heart function. In addition, the activations of both eNOS and nNOS may be associated with the cardiac protective effects of β3-AR. Despite a wide range of therapeutic approaches, myocardial infarction (MI) continues to be one of the leading cause of death worldwide −/− mice exhibited exacerbated pathological remodeling and impaired cardiac functional Recently, accumulating evidence demonstrated that β3-AR presents in the endothelium and myocardium. Moreover, β3-AR stimulation regulates specific effects in the cardiovascular system, including positive chronotropic effect, vasodilation effect and even affecting the electrical properties of the heart There is accumulating evidence suggesting that β3-AR plays an important role in the modulation of cardiovascular function in heart failure that. Moreover, these effects may be associated with nitric oxide (NO) release via nitric oxide synthase (NOS) activation MI, inducing permanent loss of cardiomyocyte mass and pathological left ventriclar remodeling, is the major cause of heart failure One hundred and twenty adult male C57BL6/J mice  were purchased from the animal centre in the Fourth Military Medical University. Mice were housed in a temperature-controlled animal facility with a 12-hour light/dark cycle, with tap water and rodent chow provided ad libitum. Mice were randomly allocated into the following groups with n = 30 each: (1) sham group (Sham); (2) MI group (MI); (3) MI + BRL37344 group (MI+BRL); (4) MI + SR59230A group (MI+SR). The experiments were performed in adherence with the National Institutes of Health Guidelines on the Use of Laboratory Animals and were approved by the Fourth Military Medical University Committee on Animal Care.Myocardial infarction (MI) was constructed by left anterior descending (LAD) artery ligation as described previously Mice in MI+BRL and MI+SR groups were administrated with BRL37344 or SR59230A respectively at 0.1 mg/kg/hour via osmotic mini-pumps  one day after MI operation.Mice were sacrificed for histological assay 4 weeks after MI operation. Hearts were fixed in 4% paraformaldehyde and embedded in paraffin. Serial sections were prepared at 5 µm thickness, and Masson's trichrome stain was performed to detect fibrosis in cardiac muscle. Ten anterolateral sections from each heart were evaluated in their entirety and quantified. Computerized morphometry was used to calculate the scar extent as the ratio of scar and total left ventricular area using Imaging Pro Plus software.Echocardiography studies were conducted at 2 days, 7days and weekly until sacrificed at 4 weeks after infarction as previously described Myocardial apoptosis was determined by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL) assay and caspase-3 activity assay as previously described Caspase-3 activity was determined with a fluorometric caspase-3 assay plate  according to the manufacturer's instructions Ser1177) ; Phospho-eNOS Thr495 (p-eNOS Thr495) ; Phospho-eNOS Ser114 (p-eNOS Ser114) ; Phospho-eNOS Ser633 (p-eNOS Ser633) ; iNOS ; Phospho-nNOS Ser1417 (p-nNOS Ser1417) ; Phospho-nNOS Ser847 (p-nNOS Ser847) ; nNOS ; β-actin ; β1-adrenergic receptor ; β2-adrenergic receptor ; β3-adrenergic receptor . Blots bands were visualized with an enhanced chemiluminescene system , and densitometric analysis of Western blots was carried out using VisionWorks LS, version 6.7.1.Western blotting was performed following standard protocol as described previously The mRNA expressions of nNOS were assessed by reverse transcription PCR (RT-PCR) assay. See P values <0.05 were considered statistically significant.All data are presented as mean ± SEM. Statistics were calculated using Prism 5.0 . Statistical comparisons for different groups were performed using one-way ANOVA followed by Student's paired, two-tailed t-test for two groups' comparison. vs. 35.21±1.47% in MI group, P<0.05). Moreover, β3-AR specific inhibitor SR59230A increased the scar area, although without significance   .We performed echocardiogram to evaluate the heart function after MI in all groups. Representative M-mode echocardiographic illustrations at 4 weeks after MI  revealedP<0.05) and that in MI+SR group . However, no significance of apoptosis index was observed between MI group and MI+SR group (Representative immunofluorescence photomicrograph in SR group .P<0.05) and MI+SR group   .Western blotting assay was performed to investigate the cardiac expressions of β-adrenoreceptors, including β1/β2-AR and β3-AR. As representative bloting results and semiquantitative analyses shown in Ser1177, eNOSSer114, eNOSSer633 and eNOSThr495. As representative bloting results and semiquantitative analyses shown in Ser114 and phosphorylated eNOSSer633 were unchanged in all groups. Moreover, phosphorylation of eNOSSer1177, which indicates eNOS activation, significantly decreased in MI group and MI+SR group, whereas the expressions of phosph-eNOSThr495 increased in MI group and MI+SR group. Furthermore, BRL-37344 treatment increased the expression of phosph-eNOSSer1177, and decreased the level of phosph-eNOS Thr495.Previous studies suggested that β3-AR stimulation results in NO production via three NOS isoforms . Therefore, we evaluated the expressions of NOS isoforms after MI, as well as the role they played in the cardioprotective effects of β3-AR. First, we examined the eNOS expression and activation which is generally modulated by 4 phosphorylation sites: eNOSSer1417 were also increased in MI group and MI+SR group. However, MI increased the expression of phospho-nNOSSer847. Furthermore, BRL-37344 treatment resulted in a 2-fold increase in nNOS protein expression. Moreover, BRL-37344 treatment increased the expression of phospho-nNOSSer1417 and decreased the phospho-nNOSSer847 expression compared with MI group  to evaluate the mRNA expression of nNOS. The representative results in MI group . In contMI group .In the present study, we found that β3-AR activation decreased the apoptosis of cardiomyocytes and also inhibited fibrosis, which contributed to the enhanced cardiac function after MI. Moreover, our data revealed a novel mechanism by which eNOS and nNOS pathways were related with the protective effect of β3-AR.–/– mice exhibited exacerbated hypertrophy and cardiac systolic dysfunction induced by pressure-overload –/– mice In the heart, 3 subtypes of β-ARs, belonging to the G protein–coupled receptor superfamily, potentially play an essential role in modulating cardiac function Although the mechanism of the protective effects of β3-AR is not clarified, previous studies have suggested that β3-AR stimulation increases eNOS activity and NO release Ser1177),threonine residue 497 (phosph-eNOSThr495), serine residue 633(phosph-eNOSSer633) and serine residue 114(phosph-eNOSSer114) Ser1177 and increased the expression of phosph-eNOSThr495. However, β3-AR stimulation with BRL significantly increased phosph-eNOSSer1177 and decreased phosph-eNOSThr495. Interestingly, the phosphorylations of eNOS at Ser114 and Ser633 were unaltered after MI with or without BRL treatment. The alterations in the phosphorylation status of eNOS suggested that MI inhibited the eNOS activation which was abolished by β3-AR stimulation. Moreover, the expression of iNOS was unchanged by BRL treatment, which was consistent with our previous results The activity of eNOS is generally modulated by either translocation or phosphorylation. We previously observed the β3-AR induced eNOS translocation only in the right atrium −/− mice exhibit more severe remodeling and functional deterioration induced by MI Emerging evidence demonstrated that nNOS-derived NO production plays a substantial role in regulating myocardial contraction Although our study bears some clinical relevance, there are many limitations. First, the detailed physiologic and pathologic functions of β3-AR have not been extensively characterized. Moreover, there are still intense controversies about which NOS isoform is the chief player in β3-AR signaling. Therefore, further studies defining the exact mechanism are needed. In conclusion, we provided evidence that the administration of β3-AR specific agonism has a substantial effect on recovery of heart function. In addition, the activations of both eNOS and nNOS may be associated with the protective effects of β3-AR. These data collectively indicate that β3-AR stimulation, as a therapeutic target, may have clinical potential for myocardial infarction.Methods S1RNA extraction and Reverse Transcription - Polymerase Chain Reaction (RT-PCR) analysis. Total RNA was extracted using TRIZOL reagent (Invitrogen) in accordance with the manufacturer's protocol. After reverse-transcribed, cDNA samples were subjected to polymerase chain reaction (PCR) amplification. The following primers were used: The primer sequence for nNOS: forward 5′-GGC ACT GGC ATC GCA CCC TT-3′, reverse 5′-CTT TGG CCT GTC CGG TTC CC-3′. The β-actin housekeeping gene was used as an endogenous internal control. The primers for β-actin: forward 5′-AAC CGC GAG AAG ATG ACC CAG ATC ATG TTT-3′; reverse, 5′-AGC AGC CGT GGC CAT CTC TTG CTC GAA GTC-3′. The PCR products were fractionated by 1.2% agarose gel electrophoresis and visualized under UV illumination after staining with ethidium bromide.(DOC)Click here for additional data file.Figure S1RT-PCR analysis of nNOS mRNA expression. A: PCR-based detection of nNOS in sham group,MI Group, MI+SR group and MI+BRL group. B: Semiquantitative analysis of the mRNA expression of nNOS in all groups .(TIF)Click here for additional data file.


There are two types of costly punishment, i.e. peer-punishment and pool-punishment. While peer-punishment applies direct face to face punishment, pool-punishment is based on multi-point, collective interaction among group members. Regarding those two types of costly punishment, peer-punishment is especially considered to have the flaws that it lowers the average payoff of all players as well as pool-punishment does, and facilitates antisocial behaviour like retaliation of a defector on a cooperator. Here, this study proposes the new peer-punishment that punishment to an opponent player works at high probability when an opponent one is uncooperative, and the difference of payoff between a player and an opponent one becomes large in order to prevent such antisocial behaviour. It is natural to think that players of high payoff do not expect to punish others of lower payoff because they do not have any complaints regarding their economic wealth. The author shows that the introduction of the proposed peer-punishment increases both the number of cooperative players and the average payoff of all players in various types of topology of connections between players. Those discussions are based on either negative123467891011121314In contrast to the preceding argument, regarding positive discussions, Garcia and Traulsenet al.et al.et al.1618et al.et al.As for peer-punishment in the public goods game, Helbing Here, contrary to those previous discussions, this study newly proposes the probabilistic peer-punishment based on the difference of payoff. To prevent the antisocial behaviour, the proposed peer-punishment enables a player to punish another uncooperative player having a connection with him/her and larger payoff than his/her payoff. The probability that a player punishes another uncooperative player is directly proportional to the difference between each payoff of punishing and punished players. Regarding the difference between the proposed peer-punishment and the ordinary peer-punishmentet al.p (0 < p < 1), and includes the peer-punishment on defectors with a probability q (0 < q < 1)The author also describes the difference between the proposed peer-punishment and other probabilistic punishment as follows. Chen This study refines Ohdaira’s previous workN is the number of all players, players of a match are i and j , s(i) and s(j) are their strategy, and P(i) and P(j) are their payoff, P(i) is expressed as the A of the s(i) and s(j) are either (1 0) (cooperator) or (0 1) (defector) of unit vectors. O(i) denotes the set of the other players having connections with player i.This study is based on Nowak and May’s spatial prisoner’s dilemma game introducing connectionsi is k(i). The average of k(i), k(i) is the same regarding all players  = <k> = 4, 1 ≤ i ≤ N). Note that N = 20) in order to make clear the spatial structure of connections. The ratio of the number of defectors to the number of cooperators approximately equals one to one, and defectors and cooperators are randomly distributed in every simulation run. That ratio also refers to Santos and Pacheco’s modelThe spatial structure of connections of this study is based not on Nowak and May’s two dimensional latticeet al.i (P(i)) is smaller than the payoff of player j∈O(i) (P(j)), i.e. P(i) < P(j) ≤ 2P(i), player i punishes player j with the probability qi(j) as the P(j) > 2P(i) holds, qi(j) = 1.In the following, the author describes the implementation of the proposed peer-punishment. Dreber i punishes player j∈O(i) that satisfies the condition of P(j) > P(i) based on the proposed peer-punishment, P(i) and P(j) change into P(i)′ and P(j)′ as the following i is not able to punish player j without the loss of payoff, and then has the same loss that is given to player j. Note that r is the coefficient of punishment, and player i is able to punish player j when the inequality of P(i)(1 − rn(i)) > 0 holds. The variable n(i) indicates the number of players j∈O(i) that satisfies the condition of P(j) > P(i).When player P(i)′ = 0 in the case of P(i)′ < 0. Therefore, P(i)′ cannot be negative. After the payoff of all players changes due to all punishing and punished activities, as the following i chooses the strategy of player jmax∈i∪O(i) for his/her strategy of the matches of the next generation. When more than one player has the maximum payoff, player i randomly chooses the strategy of one of them. Each strategy of all players is synchronously updated.The decrease in the payoff due to punishing the opponent and being punished by the opponent is calculated independently, and eventually k(i), <k> = 4, 8, and the number of all players, N = 1000. As noted before, in the initial state, the ratio of the number of defectors to the number of cooperators approximately equals one to one, and defectors and cooperators are randomly distributed in every simulation run. Each type of the topology of connections in each initial state does not change during every simulation run. The parameter in the payoff matrix A, b = 1.5 that is the intermediate value of the range of b. Regarding the coefficient of punishment, this study utilizes r = 0.15 because that value brings the greatest number of cooperators in each case of the regular topology of connections of <k> = 4, 8 within the range of 0 < r ≤ 0.3. The following results of both the frequencies of cooperators and defectors and the average payoff of all players until 300 generations are the average of 20 independent simulation runs, and basically have error bars of SD (standard deviation).Parameter settings of this study are as follows based on Santos and Pacheco’s results of the scale-free topology of connectionsk> = 4. Regarding the regular topology of connections (see i (=P(i)) is the resulting sum from several matches as well as Nowak and May’s modeli (=k(i)) like Santos and Pacheco’s previous studyFirstly, the author shows the results of <ions see , withoutions see , in the ions see , becausek> = 8. Regarding the regular topology of connections  is not normalized by k(i) as noted in the results of <k> = 4, without the proposed peer-punishment  = 1), the number of cooperators in the 300 generation greatly decreases approximately from 819 to 609 in the result of the random topology of connections of <k> = 4. This fact indicates that the introduction of the probability proposed in this study is universally effective in the evolution of cooperation in various types of topology of connections. As described in the results, without the proposed peer-punishment, the regular topology of connections of <k> = 4 has more opportunity for the evolution of cooperation than the random topology of the same <k>. This trend corresponds to Abramson and Kuperman’s resultA (=b) equals 1.5 and <k> is 4, more randomness in the topology of connections induces the situation where defectors easily invade many more clusters of cooperators.As noted in the introduction, Dreber r is small. Regarding r, it is necessary to introduce the case where r is different between punishing and punished players in the future because defectors of high payoff possibly have some kind of power. In addition, it is also required to investigate whether the value of r = 0.15 is effective in the evolution of cooperation except for the topology of connections presented in this study or not, and to examine other values of b. The author has already started to investigate the effect of changing r in broader range than that utilized in this study. Those results will be presented by another forthcoming paper.In the public goods game that is classified as the group interaction, the previous studyet al.et al.Iwasa and LeeThis study shows recent either negative123467891011121314i, k(i) is the same regarding all players. Secondly, by rewiring every connection of player i with the uniform probability p = 1, we build the random one dimensional lattice of periodic boundary conditions. As neither the deletion nor the duplication of every connection occurs, the average of k(i), <k> does not change after the rewiring. These procedures of the construction of the regular and the random lattice follow the method by Watts and Strogatzm0 = <k> + 1 players of a complete graph, at every time step we add a new player with m = <k>/2 edges that link the new player to m different players already present in the lattice until the total number of players (N) reaches 1000. A new player i will be connected to player u depending on k(u), so that p(k(u)) = k(u)/Σv k(v) . Therefore, players of small i (≤<k> + 1) turn into high degree players. Each vertex of the lattice is initially occupied by either a defector or a cooperator at the same probability (1/2). Therefore, in the initial state, the ratio of the number of defectors to the number of cooperators “approximately” equals one to one. There is the case where the number of cooperators is a little larger than that of defectors, and vice versa. In the As described in the Model, the spatial structure of connections of this study is defined as the one dimensional lattice of periodic boundary conditions. Firstly, we construct the regular one dimensional lattice of periodic boundary conditions. “Regular” means that the number of players having connections with player How to cite this article: Ohdaira, T. Evolution of cooperation by the introduction of the probabilistic peer-punishment based on the difference of payoff. Sci. Rep.6, 25413; doi: 10.1038/srep25413 (2016).


Here, we demonstrate that EPI-001 has general thiol alkylating activity, resulting in multilevel inhibitory effects on AR in PCa cell lines and tissues. At least one secondary mechanism of action associated with AR inhibition was found to be selective modulation of peroxisome proliferator activated receptor-gamma (PPARγ). These multi-level effects of EPI-001 resulted in inhibition of transcriptional activation units (TAUs) 1 and 5 of the AR NTD, and reduced AR expression. EPI-001 inhibited growth of AR-positive and AR-negative PCa cell lines, with the highest sensitivity observed in LNCaP cells. Overall, this study provides new mechanistic insights to the chemical biology of EPI-001, and raises key issues regarding the use of covalent inhibitors of the intrinsically unstructured AR NTD.The androgen receptor (AR) is a driver of prostate cancer (PCa) cell growth and disease progression. Therapies for advanced PCa exploit AR dependence by blocking the production or action of androgens, but these interventions inevitably fail via multiple mechanisms including mutation or deletion of the AR ligand binding domain (LBD). Thus, the development of new inhibitors which act through non-LBD interfaces is an unmet clinical need. EPI-001 is a bisphenol A-derived compound shown to bind covalently and inhibit the AR NH Prostate cancer (PCa) is the most commonly diagnosed male cancer in the US with approximately 233,000 new cases and 30,000 deaths predicted in 2014 . Importa2-terminal domain (NTD) [in vitro and in vivo [The AR is a modular steroid hormone receptor transcription factor with the primary transcriptional activation function mapping to Transcriptional Activation Units (TAU)1 and TAU5 in the intrinsically unstructured AR NHin (NTD) , 17. Thein (NTD) , 19. Thiin (NTD) , 19. EPI in vivo , 21. HerGal4 hybrid wherein the AR DBD had been replaced with the yeast Gal4 DBD , which is related structurally to EPI-001 but contains a bis-epoxide, has been shown to act as a selective PPARγ modulator (SPPARM) with diverse effects in different cell types –25. Give in vivo –29, we r in vivo , 30, whi5 Figure , analogo5 Figure .in vitro experiments to reflect the higher doses of drug that have been used for in vivo [ex vivo [To expand these observations to clinical disease, we treated fresh human PCa tissue maintained as explants –33 with  in vivo  or ex vi[ex vivo  experimeWe next tested for SPPARM activity of EPI-001 in PCa cells. Similar to troglitazone, EPI-001 activated a PPARγ-response element (PPRE)-regulated luciferase reporter in LNCaP cells Figure . This SPTo investigate the relationship between EPI-001-mediated PPARγ activation and AR inhibition, we knocked down PPARγ with siRNA in LNCaP cells. Despite effective silencing of PPARγ expression, both troglitazone and EPI-001 maintained robust inhibition of AR protein expression . This fi2O) is one example [Because SPPARM activity did not fully account for the multi-level anti-AR effects of EPI-001, we considered the fact that Bisphenol A (BPA) and BADGE are endocrine disruptors used in the production of polycarbonate plastics and epoxy resins , 37. The example . Chloroh example . Therefo example , which i example . In a pr example . HoweverIn this study, we describe unanticipated multi-level effects of EPI-001 on the AR and PPARγ pathways, leading to inhibition of cell growth. In previous reports, EPI-001 was shown to bind specifically to the AR NTD through a nucleophilic substitution reaction with the EPI-001 chlorohydrin group , therebyin vitro [Our data indicate that PPARγ activation represents at least one AR-independent activity of EPI-001 in PCa cells. However, EPI-001 displayed PPARγ inhibitory activity in a classical 3T3-L1 adipocyte model, indicating SPPARM activity as opposed to pure agonist activity. SPPARM activity for EPI-001 is consistent with studies demonstrating that the chemically-related compound, BADGE, is a SPPARM that binds to the PPARγ LBD  and exhiin vitro , and ourR, 20S) isomer of racemic EPI-001, has been shown to display stronger AR interactions and reduced toxicity in mice [These new data indicate that structural changes to the core bisphenol of EPI-001 as well as the covalent warhead may be required to mitigate the AR-independent effects reported in this study and in the toxicology literature , 41, 42. in mice , indicat2 for no longer than 15 passages after resuscitation of frozen stocks. Cell growth was assessed by crystal violet staining as previously described [LNCaP, C4-2, DU 145, VCaP, 22Rv1, 293T, and PC-3 cell lines were obtained from ATCC. The ATCC validates the authenticity of these cell lines via short tandem repeat (STR) analysis. CWR-R1 prostate cancer cells were the generous gift of Dr. Elizabeth Wilson . CWR-R1 cells were authenticated by sequence-based validation of two characteristic AR mutations: a H874Y mutation in the LBD, and a 50 kb deletion in AR intron 1 [escribed .Dihydrotestosterone (DHT), mibolerone (MIB), and BABDHE  were purchased from Sigma. Enzalutamide (ENZ) was purchased from Selleck Chemicals. Troglitazone was purchased from Cayman Chemical. EPI-001  was synthesized See  or purchGal4, NTD-Gal4, ARGal4ΔTAU1, ARGal4ΔTAU5, sPSA-Luciferase -LUC), and sPSAGal4-Luciferase -LUC) have been described [Plasmids encoding human AR (p5HBhAR-A), ARescribed . SV40-Reescribed , and wasLNCaP cells were transfected via single-pulse electroporation as previously described . C4-2 ceTransfected cells were lysed in 1X Passive Lysis Buffer (Promega) and subjected to dual luciferase assays using a Dual Luciferase Assay Kit (Promega) as previously described .Western blotting with antibodies listed in Nascent transcripts were isolated using the Click-iT Nascent RNA Capture Kit (Life Technologies) according to manufacturer specifications. First-strand cDNA synthesis was performed using the SuperScript VILO cDNA synthesis kit (Life Technologies) according to manufacturer specifications and quantified via qRT-PCR. Detailed information regarding reagent concentrations and time of treatment can be found in the RNA isolation and quantitative RT-PCR analysis were performed as described  using prPatient tissues were obtained from the University of Texas Southwestern Medical Center tissue core under UTSW IRB STU 112013–056 and explant studies were performed as previously described , 32. DetSolutions of reduced l-glutathione, 2-mercaptoethanol, and cysteamine in phosphate buffered saline were adjusted to the desired pH . EPI-001 or monoepoxide control (Compound 2, t-Test with a P value of 0.05 or less considered significant.All statistical comparisons were made using the two-tailed Student's


Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player’s performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players’ skills to the team’s success at running different plays, can be used to automatically learn players’ skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players’ respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player’s interactions with a given lineup based only on his performance with a different lineup. Numerical evaluation of basketball players has long been based on box score statistics. Such evaluations, by nature of the limited data set from which they draw, generally center around a player’s contributions to the five positive statistics—points, rebounds, assists, steals, and blocks—while neglecting more nuanced aspects of the player’s value, such as his/her ability to make high quality (non-assist) passes, or set good screens, or rotate effectively on defense. These less easily quantifiable aspects of a player’s performance are traditionally evaluated only qualitatively, informed by the intuition of a coach or analyst who has spent a significant amount of time watching the players perform.This distinction between quantifiable and non-quantifiable player skills may be on the verge of disappearing. Beginning in the 2013–2014 season, all thirty arenas of the National Basketball Association (NBA) contain a system of cameras and tracking software that allow the spatial coordinates of all players and the ball to be recorded and processed digitally [Thus far, publicly-available studies using player tracking data have largely focused on augmenting or refining the use of conventional statistics. For example, recent studies have examined the effect of a defender’s proximity on shooting percentage , broken Recent studies have proposed the idea that describing a basketball offense is essentially a network problem , in the The power of this network description becomes apparent when one imagines coupling it with the full player tracking data set. Indeed, player tracking data allows one to reconstruct directly how the offense progresses from the inbounds pass to a made basket. The success rate of each step along the way provides information about the effectiveness of links in the network, which in turn are a reflection of the skill levels of the players involved. Thus, by “watching” the performance of the offense with player tracking data, it should be possible to learn the skill levels of the players involved, including any skills that affect the team’s offensive performance. Once these skills are known, one can predict quantitatively how the offense will perform when called upon to run different plays or to substitute different players whose skills are also known.In this paper, we suggest a method for achieving these goals using player tracking data. We first outline generally how to build a network model of the offense that relates player skills to the effectiveness of a given play, and we provide a specific, simplified example of such a model. We then discuss the process of learning player skills from player tracking data, and we show that in general these skills cannot be measured directly from the data due to a lack of information about which play a team is attempting at the moment of a failed offensive sequence. Nonetheless, we show that this issue can be addressed using an iterative procedure based on Bayesian inference. The effectiveness of this procedure is then demonstrated by means of a test on simulated data. Finally, we use our simplified network model to examine hand-recorded data from the 2011 playoff series between the Oklahoma City Thunder and the Memphis Grizzlies. We show that, despite a very limited data set, we are able to correctly predict a player’s interactions with a particular lineup based on data from his performance with a different lineup.In the following Results section, we present our method and examine one specific implementation of it using simulated and real data. The Discussion section briefly outlines some limitations and possible generalizations of our work. Finally, the velocity of each player in the definition of nodes.) One could also employ a more course-grained definition of nodes, for example by neglecting the positions of off-ball players or by using a fine spatial grid for some players and a course grid for others. , which depend on the skill levels of the players involved, and the rates at which the plays are attempted, which depend on the coach and on the team’s overall offensive philosophy. A full description of a coached agent offense, then, involves knowing the set of all player skills and the rate at which each link in the network is attempted. If this information is known, then in principle the offense is completely described, and the effectiveness of the offense can be predicted under any number of hypothetical variations, including swapping one player for another or changing the team’s set of attempted plays.  In Sec. 5 we comment briefly on possible hybrids between the “coached” and “random” agent models.a describes a state where the point guard has the ball on the wing and node b describes a state where the center has the ball in the low post, then the success rate fab of link ab must depend on the point guard’s skill in making the entry pass. More generally, for each possible link ij one needs to define a formula fij(S) that relates the set of all player skills, denoted S, to the probability of the offense successfully transitioning from node i to node j, given that the transition is attempted . In this way, the definition of all success rates {fij} also involves the quantitative definition of all player skills that are relevant to the offense. One specific definition of player skills and success rates is discussed in Sec. 2 and diagrammed in ij that involve a shot attempt—i.e., where the node j represents the goal—fij is best defined as the expected number of points scored by the shot attempt .In the coached agent model, one must provide not only a definition of the nodes of the offense, but also an analytical definition of how the success rates of the links between these nodes depend on the skill levels of the players involved. For example, if node fij(S) are defined, one can predict the expected number of points scored per possession by the offense, F, by summing over all possible attempted plays α, weighted by the probability Pα that the team will attempt them during a given possession:Once the formulae fij of different attempted transitions, depend not only on the skills of the offensive players but on the quality of the defense being faced. In this sense any player skill is understood to be defined relative to a given defense. When considering the performance of a player over an entire NBA season, for example, such skills are naturally defined relative to the league-average defense. One could also imagine considering a player’s performance against a specific opponent, and thus defining the player’s skills relative to that particular matchup.Of course, it is important to note that the apparent skill levels of the players, which define the success rates F of the offense can be evaluated for any set of play frequencies P ≡ {Pα} and any combination of players. But first we outline a simple model of a basketball offense, which we call the “high/low” model, that will serve as a proof of concept for the network approach advocated in this paper.In Sec. 4 we show how player tracking data can be used to learn the set of all player skills, so that the effectiveness In the previous section we outlined the general ingredients necessary for creating a network model of a basketball offense. In this section we introduce one specific example of such a model, which we dub the “high/low” model. In this model, the state of the system is considered to be entirely characterized by which player holds the ball and whether that player’s position is “high”—far from the basket—or “low”—close to the basket. We set the cutoff between high and low using the perimeter of the free throw lane (the “key”). For example, if the point guard holds the ball at the top of the key, this state is labeled “1 high”, or “1H”; if the shooting guard has the ball in the key this is labeled “2 low”, or “2L”; etc.  The nodes of this network are shown as bold, circled characters in the diagram of i to another player in a “high” state is defined to be equal to player i’s high passing ability i successfully moving from high to low is defined as the player’s “driving ability” di. For simplicity, we take the success rate of a move from low to high to be equal to unity.Within this model, we further assume that the success rate of any given link depends only on the skills of the player holding the ball. Specifically, the success rate of a pass by player di} of each player. Thus, one can completely determine the expected number of points scored by the team for a given use of plays {Pα} by performing the sum over all plays denoted by Within the model diagrammed in Of course, one can easily imagine more involved models for the offense than the high/low model presented here; for example, one could introduce a more finely resolved definition of player positions or define success rates in terms of multiple players’ activies rather than just that of the ball handler. Nonetheless, we show in Sec. 5 that even this very simple model can provide a basis for describing a player’s skill set and predicting its interaction with the skills of other players, even using a very limited data set.Pα} must be inferred simultaneously with the player skills. In this endeavor one can employ a statistical inference algorithm, such as the one we outline below.Unlike in the “random agents” model detailed in Appendix A, for the “coached agents” model that is the main subject of this paper, one cannot, in general, measure player skills directly from player tracking data. This is because player tracking data provides only a sequence of plays as they are performed by the players, and does not give any information about the team’s original intentions . Consider, for example, the problem of determining a player’s passing skill in the high/low model. A typical possession within this model might progress from the state 1H to the state 3H to a turnover . But in this sequence it is not clear how the turnover by the small forward should be interpreted. Was the turnover the result of a failed high pass? A failed low pass? A failed drive? In order to determine how to assign blame for this turnover among the player’s various skills, one needs to know something about which plays the team tends to attempt. Thus, the frequencies of play calls {S and the set of play frequencies P from this data using an iterative procedure. One particularly straightforward implementation of this procedure is as follows.Generally speaking, inference algorithms are used to infer the parameters of a statistical model in cases where there are latent variables, which cannot be measured directly from the data . In our P—that is, for the value of the frequency with which all plays are attempted. For example, below we use the guess where all play frequencies are taken to be identical. One should also assume a prior distribution for each of the skill values. Here, as an example, we use the uninformed “flat” prior, which assumes that all skill values are drawn with equal probability from their entire domain. These initial guesses for P and S are refined over time, and should not be reflected in the final inferred values of S for a large enough dataset. This can be verified explicitly by running the algorithm with different initial guesses.First, an initial guess is made for P should be very large in order to account for all possible plays that could be run by the team. In practice, however, it is sufficient to approximate the offense by some finite number Np of plays that tend to be attempted. These can be inferred from the data by collecting the Np most commonly seen plays by the offense that end in a shot attempt. The effect of increasing the size of the play set Np is examined in Appendix B.It is important to note that, in principle, the number of elements in P and S, one can estimate values for each player skill S using the following algorithm. First, one should count the total number of attempted and failed demonstrations of each skill in the dataset. For possessions ending in a shot attempt, this counting is straightforward: all steps before the shot attempt represent one successful demonstration of the corresponding skill , and the shot attempt represents one attempt at a shot (either successful or failed). For posessions ending in a turnover, however, the failed play can be counted fractionally toward a number of different skills, with the count being weighted by the probability that a given movement of the offense was being attempted. In particular, one should estimate the probability Prob(α∣k) that the observed sequence k was the result of an attempt to run a given play α, and then count each step of the observed sequence as a number Prob(α∣k) of attempts to run the corresponding step of play α. The probability Prob(α∣k) can be estimated using Bayes’ rule:k∣α) is the a priori probability that an attempt to run play α will result in the observed sequence k; this probability is a function of the skills of the players involved. The value α on a given possession, and Prob(k) is the total probability that a randomly chosen possession will follow the sequence k: Once initial guesses have been made for S. More specifically, these numbers are used to produce posterior distributions for each skill value. This is described in detail for the high/low model in Appendix C. In the limit of a very large dataset, the posterior distributions have small variance and the estimated skill value is generally defined by the ratio of the number of successful skill demonstrations to the total number of attempted skill demonstrations . We emphasize, however, that these numbers cannot, in general, be read directly from the dataset. As discussed above, making an estimate of skill values requires one to determine how blame should be assigned for failed play sequences, and this necessitates the use of a statistical inference scheme.Once the number of failed and attempted skill demonstrations have been tallied, one can use them to produce updated estimates for the skill values, S have been obtained, one can update the estimate for the play frequencies k denotes the sum over all sequences in the dataset, and N is the total number of such sequences.Finally, once updated estimates for the skills S, as defined by the posterior distribution for each skill  can be processed within a few seconds.The process outlined in the previous three paragraphs gives improved estimates of the player skills kill see , and forP were also randomly chosen, and 5000 possessions were simulated—this corresponds to 82 games’ worth of data (a full NBA season) for a lineup that plays together roughly 30 minutes per game. This data was processed using the algorithm described in Sec. 3 in order to infer the assigned traits without any prior input regarding their true values. The inferred values were then compared to the ones input to the simulation in order to assess the accuracy of the algorithm.In order to verify that the method outlined in Sec. 3 can consistently learn player skills, we tested our inference algorithm on simulated data from the high/low model. Specifically, we wrote a short simulation of a team of five players with arbitrarily-chosen skills running plays from a playbook of twenty plays. The play frequencies Np = 20; results at other values of Np are given in Appendix B.In Sec. 4 we showed that our statistical inference algorithm can correctly infer the skill levels of players in a simulated offense. Given this success, the prospect of using the algorithm to learn the traits of actual NBA players is naturally enticing. A successful inference scheme, coupled with a suitably sophisticated network model, would allow one to learn the skill levels of NBA players from player tracking data and thus predict how the team’s effectiveness would be affected by any number of changes, including player trades, substitutions, or altered play calling.Unfortunately, player tracking data is not yet available in the public domain. Thus, we devised the following basic test of our general strategy. In order to obtain some “poor man’s player tracking data”, we recorded by hand data corresponding to 401 possessions from the 2011 playoff series between the Oklahoma City Thunder (OKC) and the Memphis Grizzlies. This data is described in more detail in the One particularly exciting feature of our method is its ability to potentially predict how a given player will contribute to an offense based on his performance in a different offense and surrounded by different teammates. We tested this feature by examining how well the inferred skills for a player in one lineup can predict that player’s skills in a separate lineup. For the remainder of this section we focus our analysis on the point guards Conley and Westbrook, for whom the data was most robust, given their centrality to the movement of the offense. We used our inference algorithm to infer the skills of Conley and Westbrook using only the data corresponding to lineup 2, and then we checked whether these inferred skills constituted an accurate prediction of the skills exhibited by the players during their performance in lineup 1.A more stringent test of our approach would be to use the inferred skills for a group of players to predict their offensive performance after a significant lineup change  or a change in offensive philosophy . Unfortunately, our very limited data set does not allow us to perform such a test at present.In closing, we would like to emphasize that the goal of this paper is not to report a particular empirical finding or to promote a particular set of metrics and diagrams for basketball analysis, but rather to suggest an exciting and novel strategy for taking advantage of a revolutionary data source. The network-based strategy outlined here offers the potential for a new degree of predictive power for analysts, managers, and coaches in sports. Namely, it comprises a tool for learning, in a completely automated and unbiased way, the skill levels of different players and for predicting quantitatively how those players will perform together as a team. Such a tool has the potential to have a significant effect on coaching, scouting, and personnel decisions in professional sports .Of course, the real impact of the approach suggested here awaits its application to the actual player tracking data. Under the influence of such data, the simple high/low model will likely be replaced by richer models, including perhaps models that incorporate a finer spatial grid or a continuous description of spatial positions. Such detailed models may require one to adapt more advanced techniques from spatial statistics .We also note that, while our analysis has focused on basketball, our method can be equally applied to any sport that can be modeled as a network . Our defoffense, the entire analysis can be recast in terms of defense as well. For a defensive team, the goal is to minimize the effectiveness of the opposing offensive network. Thus, one can define a player’s defensive skills in terms of how well they prevent certain transitions in the opposing offense. In this way the entire analytical method can be used directly on the defensive end, and the effectiveness of an interacting “team defense” can be quantified. The full quality of the team, then, is the difference between the effectiveness F of the offensive network [given by One can also notice that, while this paper has focused on optimizing the performance of an given by  and the In closing, we look forward to future work that can apply the approach we have advocated here to a large corpus of player tracking data. Such work will likely spur the development of new network-style models and refined statistical inference algorithms, but we remain confident that the general strategy we have described can be successful if given sufficient attention and the full power of many games worth of player tracking data. If this is indeed the case, then perhaps the network-based description of basketball will provide a suitably revolutionary application for a revolutionary new data source.www.mysynergysports.com). For each possession, defined as the interval of time in which one team continuously has possession of the ball, the sequence of offensive states was recorded as defined by the “high/low” model described in Sec. 2. Specifically, the state of the offense at a given instant was labeled according to which player (labeled 1–5) had the ball and whether that player was outside the key  or inside the key . A total of 780 such sequences were recorded, 401 for Memphis and 379 for OKC.The data analyzed in Sec. 5 was recorded by hand from the first three games of the 2011 playoff series between the Oklahoma City Thunder and the Memphis Grizzlies. Footage for the games was provided by Synergy Sports  If all transition rates rij are known, then one can assemble them into a “propagation matrix” U, whose elements Uij = rij. This matrix describes the evolution of the offense during one step.In the random agent model, the effectiveness of a team of players can be determined as follows. First, one should expand slightly the set of nodes of the offense to account explicitly for the possibility of different possession-ending outcomes. For example, in basketball one would define separate nodes for “missed shot”, and “turnover”. Each pair of nodes y whose elements yi denote the probability that the team starts its offense at node i. For example, if the offense always starts with the ball in the point guard’s hands, then y is a vector whose elements are all zero except for that element corresponding to the node 1H , which has y1H = 1. The product Uy produces a vector that describes the expected state of the offense after one step. The state of the offense after many steps is given by Uny, with n → ∞; here, Un denotes the matrix U multiplied by itself n times.  Thus, if g corresponds to the “goal” node indicating points scored, then the expected output of the offense isg indicates the gth component of the vector Uny.To determine the expected offensive output of the team, one should know the average initial state of the offense, which can be described as a vector rij are known for all players in the offense. Interactions between player skills arise in the way that different rates rij are chained together sequentially in moving the ball from the initial state y to the goal g. Within this model, one can imagine swapping out one player for another, so that the corresponding transition rates rij are modified, and then evaluating Thus, the expected offensive output can be evaluated if the tendencies P. In principle, there is an enormous number of potential plays that a team can attempt, which is limited only by the finite duration of the shot clock. In any reasonable offensive model, trying to include every possible play that the offense can attempt makes P so large that it becomes impossible to deal with computationally. In practice, however, the vast majority of these possible plays will never be attempted. For example, the play 1H → 5L → 2L → 5L → 2L → 5L → 2L → goal has probably never been attempted in the history of professional basketball, much less occupied an importance place in a team’s playbook. Similarly, the great majority of sequences that are possible for the offense occur so rarely that they can be safely ignored by our algorithm. Instead, our strategy is to construct a finite set of possible plays that the team is likely to attempt by making a list of all possessions recorded in the data that end in a shot attempt and then choosing from this list the top Np most frequently used.In Sec. 3 we showed how a statistical inference algorithm can be used to infer player skills. This inference procedure requires one to make an estimate of which plays the team tends to attempt, which allows one to construct the set of play frequencies Np of plays included is too small, the model will fail to accurately describe the offense and thus provide a poor fit to the data, while if Np becomes too large one runs the risk of over-fitting the data and losing any predictive power. In order to see how the accuracy of the algorithm depends on the number of plays included in P, we evaluated the accuracy of the algorithm’s inferred skills as a function of Np, again using the simulated data described in Sec. 3. The result is shown in P. Given this result, we speculate that for the description of a real  basketball offense it is likely sufficient for P to contain only a few dozen potential plays.Clearly, if the number D to produce an accurate posterior distribution fpost(s∣D) for a given skill s. This posterior distribution is related to the assumed prior distribution, fprior(s) byD∣s) is the probability that a player with skill s will produce the observed data D. In this paper, we choose for the prior distribution fprior(s) a simple uniform distribution, which can be written in the notation of the beta distribution as fprior(s) = Beta.  are the shape parameters of the beta distribution.)The goal of the inference algorithm presented in Sec. 3 is to arrive at estimates for each skill value in the offense. Equivalently, one can say that the algorithm is designed to use some set of data P are used to arrive at estimates for the number of successful skill demonstrations, ns, and for the number of total attempted skill demonstrations, ntot. If the outcome of each step of each play is considered to be statistically independent, then the probability Prob(D∣s) is a binomial distribution: Prob(D∣s) = Binomial. Here, the skill s is taken to be the probability of successful completion of a given step of a play . In this case, since the beta distribution is conjugate to the binomial distribution, the posterior distribution is also a beta distribution:In the first step of the inference algorithm described in Sec. 3, initial guesses for the set of play frequences fprior(s) = Beta with some shape parameters α0, β0 > 0. Using the beta distribution as a prior allows one to write a simple expression for the posterior distribution:More generally, in cases where one can make a more informed statement about the prior expectation for player skill values, the prior distribution can be taken to be s〉 as given by α0 = β0 = 1. Of course, as mentioned in Sec. 1, for shooting skills this probability is multiplied by the average number of points scored per made shot for the player in question, so that s (as, for example, in In the main text, when we refer to an “estimated skill value”, we generally mean 〈In Sec. 5 we briefly described our results analyzing NBA games between the Memphis Grizzlies and the Oklahoma City Thunder in terms of the high/low model presented in Sec. 2. S and P, F for the two teams’ starting lineups: FOKC/FMemphis = 1.02. It is perhaps not surprising, then, that the series took seven games and four overtimes to decide.Based on the inferred values of One can notice from fij, which become functions not just of the player skills S but of the play usage rates P as well.On the other hand, there is perhaps an important dependency reflected in For models that do not make explicit consideration of such effects, one should be careful when making predictions about hypothetical lineups to ensure that each player’s usage rate in the new offense is not significantly different than the player’s usage rate in the data from which their skills are inferred. Otherwise, one can get spurious predictions based on unrealistically robust estimates of player skills.