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Humans adapt to a dynamic environment while maintaining psychological equilibrium. Systems theories of personality hold that generalized processes control stability by regulating how strongly a person reacts to various situations. Research shows there are higher order traits of general personality function (stability) and dysfunction (general personality pathology [GPP]), but whether they capture individual differences in reactivity is largely theoretical. We tested this hypothesis by examining how general personality functioning manifests in everyday life in two samples ( Ns = 205 and 342 participants and 24,920 and 17,761 observations) that completed an ambulatory assessment protocol. Consistent with systems theories, we found that (a) there is a general factor reflecting reactivity across major domains of functioning and (b) reactivity is strongly associated with stability and GPP. Results provide insight into how people fundamentally adapt to their environments (or not) and lay the foundation for more practical, empirical models of human functioning.

Cancer genetics clinics have seen increasing demand, challenging genetic counselors (GCs) to increase efficiency and prompting some clinics to implement genetic counseling assistants (GCAs). To evaluate the impact of GCAs on Geisinger’s cancer genetics clinic, we tracked GC time utilization, new patient volume, and clinic cost per patient before and after implementing a GCA program. GCs used time-tracking software while completing preappointment activities. Electronic health records were reviewed for appointment length and number of patients per week. Internal salary data for GCs and GCAs were used to calculate clinic costs per patient. Time spent by GCs completing each preappointment activity (21.8 vs. 15.1 minutes) and appointment length (51.6 vs. 44.5 minutes) significantly decreased after GCA program implementation (p values < 0.001). New patients per week per GC significantly increased (7.9 vs. 11.4, p

Emerging genetic testing delivery models have enabled individuals to receive testing without a medical indication. This article will highlight key considerations for patient care in the setting of adult patients with positive results for monogenic disease identified through genomic screening. Suggestions for how to adapt genetic counseling to a genomic screening population will encompass topics such as phenotyping, risk assessments, and the use of existing guidelines and resources. Case examples will demonstrate principles of genotype-first patient care.

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. IMPACT STATEMENT: Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

BackgroundBorderline personality disorder (BPD) is associated with altered activity in the prefrontal cortex (PFC) and amygdala, yet no studies have examined fronto-limbic circuitry in borderline adolescents. Here, we examined the contribution of fronto-limbic connectivity to the longitudinal stability of emotion-related impulsivity (ERI), a key feature of BPD, in symptomatic adolescents and young adults.MethodsWe compared resting-state effective connectivity (EC) in 82 adolescents and emerging adults with and without clinically significant borderline symptoms (n BPD = 40, ages 13-30). Group-specific directed networks were estimated amongst fronto-limbic nodes including PFC, ventral striatum (VS), central amygdala (CeN), and basolateral amygdala (BLA). We calculated directed centrality metrics and examined if these values were associated with initial levels and rates of change in ERI symptoms over a one-year follow-up using latent growth curve models (LGCMs).ResultsIn the healthy group, ventromedial prefrontal cortex (vmPFC) and dorsal ACC had a directed influence on CeN and VS respectively. In the borderline group bilateral BLA had a directed influence on CeN, whereas in the healthy group CeN influenced BLA. LGCMs revealed that in borderline adolescents, ERI remained stable across follow-ups. Further, higher output of R CeN in controls was associated with stronger within-person decreases in ERI.ConclusionsFunctional inputs from BLA and vmPFC appear to play competing roles in influencing CeN activity. In borderline adolescents BLA may predominate over CeN activity, while in controls the ability of CeN to conversely influence BLA activity is associated with more rapid reductions in ERI.

Background Unbiased estimates of penetrance are challenging, but are critically important to make informed choices about strategies for risk management through increased surveillance and risk reducing interventions. Methods We studied the penetrance and clinical outcomes of seven breast cancer susceptibility genes (BRCA1, BRCA2, TP53, CHEK2, ATM, PALB2 and PTEN) in almost 13,458 participants unselected for personal or family history of breast cancer. We identified 242 female participants with pathogenic or likely pathogenic variants in one of the seven genes for penetrance analyses, and 147 women did not previously know their genetic results. Results Out of the 147 women, 32 women were diagnosed with breast cancer at an average age of 52.8 years. Estimated penetrance by age 60 years ranged from 17.8%-43.8%, depending on the gene. In clinical-impact analysis, 42.3% (95% confidence interval = 31.3% to 53.3%) of women had taken actions related to their genetic results and two new breast cancer cases were identified within the first twelve months after genetic results disclosure. Conclusions Our study provides population-based penetrance estimates for the understudied genes, CHEK2, ATM, and PALB2, and highlights the importance of using unselected populations for penetrance studies. It also demonstrates the potential clinical impact of genetic testing to improve healthcare through early diagnosis and preventative screening.

We studied the penetrance and clinical outcomes of seven breast cancer susceptibility genes (BRCA1, BRCA2, TP53, CHEK2, ATM, PALB2 and PTEN) in almost 25,000 participants unselected for personal or family history of breast cancer. We identified 420 participants with pathogenic or likely pathogenic variants, and 147 were women who did not previously know their genetic results. Out of these 147 women, 32 women were diagnosed with breast cancer at an average age of 52.8 years. Estimated penetrance by age 60 years ranged from 18-44%, depending on the gene. Within the first twelve months after genetic results disclosure, 42% of women had taken actions related to their genetic results and two new breast cancer cases were identified. Our study provides population-based penetrance estimates for the understudied genes, CHEK2, ATM, and PALB2, and highlights the importance of using unselected populations for penetrance studies. It also demonstrates the potential clinical impact of genetic testing to improve healthcare through early diagnosis and preventative screening.

Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad "spectrum level'' dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the "problem of comorbidity'' by explicitly modeling patterns of co-occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach. (C) 2020 Published by Elsevier Masson SAS.

Purpose The clinical utility of screening unselected individuals for pathogenic BRCA1/2 variants has not been established. Data on cancer risk management behaviors and diagnoses of BRCA1/2-associated cancers can help inform assessments of clinical utility. Methods Whole-exome sequences of participants in the MyCode Community Health Initiative were reviewed for pathogenic/likely pathogenic BRCA1/2 variants. Clinically confirmed variants were disclosed to patient–participants and their clinicians. We queried patient–participants’ electronic health records for BRCA1/2-associated cancer diagnoses and risk management that occurred within 12 months after results disclosure, and calculated the percentage of patient–participants of eligible age who had begun risk management. Results Thirty-seven MyCode patient–participants were unaware of their pathogenic/likely pathogenic BRCA1/2 variant, had not had a BRCA1/2-associated cancer, and had 12 months of follow-up. Of the 33 who were of an age to begin BRCA1/2-associated risk management, 26 (79%) had performed at least one such procedure. Three were diagnosed with an early-stage, BRCA1/2-associated cancer—including a stage 1C fallopian tube cancer—via these procedures. Conclusion Screening for pathogenic BRCA1/2 variants among unselected individuals can lead to occult cancer detection shortly after disclosure. Comprehensive outcomes data generated within our learning healthcare system will aid in determining whether population-wide BRCA1/2 genomic screening programs offer clinical utility.

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.

Modern small-scale biomass burners have been recognized as an important renewable energy source because of the economic and environmental advantages of biomass over fossil fuels. However, the characteristics of their gas and particulate emissions remain incompletely understood, and there is substantial uncertainty concerning their health and climate impacts. Here, we present online measurements conducted during the operation of a residential wood-burning boiler. The measured parameters include gas and particle concentrations, optical absorption and chemical characteristics of gases and particles. Positive matrix factorization was performed to analyze data from a high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS) equipped with a filter inlet for gases and aerosols (FIGAERO). Six factors were identified and interpreted. Three factors were related to the chemical composition of the fuel representing lignin pyrolysis products, cellulose/hemicellulose pyrolysis products, and nitrogen-containing organics, while three factor were related to the physical characteristics of the emitted compounds: volatile compounds, semi-volatile compounds, and filter-derived compounds. An ordinal analysis was performed based on the factor fractions to identify the most influential masses in each factor, and by deconvoluting high-resolution mass spectra fingerprint molecules for each factor were identified. Results from the factor analysis were linked to the optical properties of the emissions, and lignin and cellulose/hemicellulose pyrolysis products appeared to be the most important sources of brown carbon under the tested burning conditions. It is concluded that the emissions from the complex combustion process can be described by a limited set of physically meaningful factors, which will help to rationalize subsequent transformation and tracing of emissions in the atmosphere and associated impacts on health and climate.

The potential effect of changing to a nonfossil fuel vehicle fleet was investigated by measuring primary emissions (by extractive sampling of bus plumes) and secondary mass formation, using a Gothenburg Potential Aerosol Mass (Go:PAM) reactor, from 29 in-use transit buses. Regarding fresh emissions, diesel (DSL) buses without a diesel particulate filter (DPF) emitted the highest median mass of particles, whereas compressed natural gas (CNG) buses emitted the lowest (MdEFPM 514 and 11 mg kgfuel–1, respectively). Rapeseed methyl ester (RME) buses showed smaller MdEFPM and particle sizes than DSL buses. DSL (no DPF) and hybrid-electric RME (RMEHEV) buses exhibited the highest particle numbers (MdEFPN 12 × 1014 # kgfuel–1). RMEHEV buses displayed a significant nucleation mode (Dp< 20 nm). EFPN of CNG buses spanned the highest to lowest values measured. Low MdEFPN and MdEFPM were observed for a DPF-equipped DSL bus. Secondary particle formation resulting from exhaust aging was generally important for all the b...

Objective Examine the uncertainty management process of individuals with Lynch syndrome (LS). Methods 19 phone interviews were conducted with individuals with LS. The interview guide included questions on family communication, risk perceptions, and uncertainty management. Data were analyzed using the constant comparison method to code for emergent themes. Results Qualitative analysis found individuals with LS tried to manage their uncertainty through preventive care, but were often confounded by healthcare barriers. Healthcare barriers included cost and insurance issues, absence of coordinated care, insufficient provider knowledge, and lack of patient-centered communication. Participants reported increased uncertainty and anxiety due to these barriers and used alternative uncertainty management strategies such as advocating for themselves with providers, seeking information online, and communicating with family for emotional support. Conclusion Healthcare barriers identified in this study exacerbated uncertainty and anxiety for individuals with LS and challenged their ability to engage in preventive care. In response, participants used alternative uncertainty management strategies to reduce their uncertainty, which may have unintended negative consequences. Practice implications Findings support the need for providers to partner with specialists in genetics and/or LS to better care for individuals with LS. Findings highlight opportunities for interventions in healthcare to better support individuals with LS.

BackgroundAims of the study were to determine the effects of humerus intraosseous (HIO) versus intravenous (IV) administration of epinephrine in a hypovolemic, pediatric pig model. We compared concentration maximum (Cmax), time to maximum concentration (Tmax), mean concentration (MC) over time and return of spontaneous circulation (ROSC).MethodsPediatric pig were randomly assigned to each group (HIO (n=7); IV (n=7); cardiopulmonary resuscitation (CPR)+defibrillation (defib) (n=7) and CPR-only group (n=5)). The pig were anesthetized; 35% of the blood volume was exsanguinated. pigs were in arrest for 2 min, and then CPR was performed for 2 min. Epinephrine 0.01 mg/kg was administered 4 min postarrest by either route. Samples were collected over 5 min. After sample collection, epinephrine was administered every 4 min or until ROSC. The Cmax and MC were analyzed using high-performance liquid chromatography. Defibrillation began at 3 min postarrest and administered every 2 min or until ROSC or endpoint at 20 min after initiation of CPR.ResultsAnalysis indicated that the Cmax was significantly higher in the IV versus HIO group (p=0.001). Tmax was shorter in the IV group but was not significantly different (p=0.789). The MC was significantly greater in the IV versus HIO groups at 90 and 120 s (p2 indicated the IV group (5 out of 7) had significantly higher rate of ROSC than the HIO group (1 out of 7) (p=0.031). One subject in the CPR+defib and no subjects in the CPR-only groups achieved ROSC.DiscussionBased on the results of our study, the IV route is more effective than the HIO route.

Shipping is becoming a major source of particulate air pollution in coastal cities. Here we describe the use of a stationary measurement site to characterize nanoparticle emissions (5.6–560 nm) from a large ship fleet (154 ships) in a harbor region of an emission control area (ECA) under real-world dilution conditions. Emission factors (EFs) are described with respect to particle number (PN), mass (PM), size and volatility. Automatic Identification System data were used to obtain information on ship class, direction, speed and acceleration. Cargo and passenger ships had similar average EFs: 2.79 ± 0.19 vs. 2.35 ± 0.20 × 1016 # (kg fuel)−1 and 2550 ± 170 vs. 2200 ± 130 mg (kg fuel)−1 respectively. The number size distributions for cargo and passenger ships were unimodal, peaking at ∼40 nm. Tug-boats and pilots emitted smaller particles with lower EFPN and EFPM. For emissions of non-volatile particles from cargo and passenger ships EFPM increased with decreasing speed and acceleration while the EFPN decreased. The size distributions of the non-volatile particles for all ships contained a large mode at ∼10 nm. This peak is believed to be formed during plume aging. A detailed understanding of size-resolved particle emissions from individual ships will be important in designing appropriate emission regulations for coastal areas.

In many cities worldwide, modern fleets have been introduced to reduce gaseous and particle emissions from city buses. To date, most emission studies are limited to a few vehicles, making a statistically significant assessment of control options difficult, especially under real-world driving conditions. Exhaust emissions of 234 individual city buses were measured under real-world stop-and-go traffic conditions at a bus stop in Gothenburg, Sweden. The buses comprised models fulfilling Euro III-VI and EEV (Enhanced Environmentally Friendly Vehicle) standards with different engine technologies, fuels, and exhaust after-treatment systems, and also included hybrid-electric buses (HEV). Both gaseous (NOx, CO, HC, and SO2) and size-resolved particle number (PN) and mass (PM) emission factors (EF) were calculated for vehicles using compressed natural gas (CNG), diesel (DSL), Rapeseed Methyl Ester (RME) and Hydro-treated Vegetable Oil (HVO) equipped with various after-treatment technologies, e.g., diesel particulate filter (DPF), selective catalytic reduction (SCR) and exhaust gas recirculation (EGR) systems. The highest median EFPN was obtained from Euro VHEV-HVO-SCR buses (MdEFPN = 18×1014 # kg-1) when their combustion engines were used though 53% of their accelerations were below detection limits indicating the use of their electrical engine. The highest MdEFPM was obtained from the Euro V-DSL-SCR buses (MdEFPM = 150 mg kg-1) and the lowest from EEV-CNG buses (below detection threshold) and Euro VIHEV-HVO- SCR+EGR+DPF buses (MdEFPM = 19 mg kg-1). The highest MdEFNOx was obtained from the Euro V-RME-SCR (MdEFNOx = 30 g kg-1) and Euro VHEV-HVO-SCR buses (MdEFNOx = 24 g kg-1), and the lowest from CNG buses (MdEFNOx = 4.8 g kg-1) and Euro VIHEV-HVO-SCR+EGR+DPF buses (MdEFNOx = 7.4 g kg-1). Hybrid buses can give higher PN emissions compared to traditional diesel engines, likely due to downsized combustion engines. Replacing diesel by biodiesel fuel reduced MdEFPM significantly but increased MdEFNOx which may be due to the higher combustion temperature and oxygen contents of the fuel (for RME). Overall, the EEV-CNG buses performed the best regarding both the MdEF and low contribution to the high emitters. It was also found that a small (5%) proportion of the buses contributed significantly (14-30%) to the total emissions. Identification and monitoring the maintenance of the high emitters in the fleets should be considered for the improvement of air quality. Keywords: Vehicle emissions, Emission factor, Roadside measurement, Hybrid-electric vehicles (HEV), Rapeseed methyl ester (RME), Hydro-treated vegetable oil (HVO), Compressed natural gas (CNG)

Often, advanced practitioners must give clinical presentations. Public speaking, which is a major fear for most individuals, is a developed skill. Giving an oral presentation is a good way to demonstrate work, knowledge base, and expertise. Giving an effective presentation can help obtain recognition of skills and proficiency as an advanced practitioner or expert in the field. This paper will highlight skills and techniques that can help to improve presentation style and the ability to connect with an audience.

BackgroundResearch cohorts with linked genomic data exist, or are being developed, at many research centers. Within any such “sequenced cohort” of more than 100 participants, it is likely that there are participants with previously undisclosed risk for life-threatening monogenic diseases that could be identified with targeted analysis of their existing data. Identification of such disease-associated findings are not usually primary to the enrollment research goals. At Geisinger Health System, MyCode® Community Health Initiative (MyCode) participants represent one such large sequenced cohort. Since 2013, MyCode participants in discovery research have been consented for secondary analysis of their existing research genomic sequences to allow delivery of medically actionable findings to them and their healthcare providers. This return of genomic results program was developed to manage an anticipated 3.5% of MyCode participants who will receive clinically confirmed genomic variants from an approved gene list out of more than 150,000 total participants. Risk-associated DNA sequences alone without any clinical parameter, prompt “genome-first” follow-up encounters.MethodsThis article describes our process for generating clinical grade results from research-based genomic sequencing data, delivering results to patients and their providers, facilitating targeted clinical evaluations of patients and promoting cascade testing of at-risk relatives. We also summarize our early data about the results generated during this process and our ability to contact patients and their providers to disclose the information.ResultsThis process has been used to generate 343 results on 339 patients. 93% of patients with a result have been successfully contacted about their results as evidenced by direct interaction about their result with the research team or a healthcare provider. 222 healthcare providers have been notified of a result on one or more patient through this result delivery process.ConclusionsHere we describe the existing GHS model to deliver genomic data into the electronic medical record and the clinical interactions that are prompted and supported. Elements of this genome-first care model can be applied in other healthcare settings and in national efforts, such as “All of Us”, that wish to establish programs for returning genomic results to research participants.

In natural environments with many options of uncertain value, one faces a difficult tradeoff between exploiting familiar, valuable options or searching for better alternatives. Reinforcement learning models of this exploration/exploitation dilemma typically modulate the rate of exploratory choices or preferentially sample uncertain options. The extent to which such models capture human behavior remains unclear, in part because they do not consider the constraints on remembering what is learned. Using reinforcement-based timing as a motivating example, we show that selectively maintaining high-value actions compresses the amount of information to be tracked in learning, as quantified by Shannon's entropy. In turn, the information content of the value representation controls the balance between exploration (high entropy) and exploitation (low entropy). Selectively maintaining preferred action values while allowing others to decay renders the choices increasingly exploitative across learning episodes. To adjudicate among alternative maintenance and sampling strategies, we developed a new reinforcement learning model, StrategiC ExPloration/ExPloitation of Temporal Instrumental Contingencies (SCEPTIC). In computational studies, a resource-rational selective maintenance approach was as successful as more resource-intensive strategies. Furthermore, human behavior was consistent with selective maintenance; information compression was most pronounced in subjects with superior performance and non-verbal intelligence, and in learnable vs. unlearnable contingencies. Cognitively demanding uncertainty-directed exploration recovered a more accurate representation in simulations with no foraging advantage and was strongly unsupported in our human study.

Laboratory studies of value-based decision-making often involve choosing among a few discrete actions. Yet in natural environments, we encounter a multitude of options whose values may be unknown or poorly estimated. Given that our cognitive capacity is bounded, in complex environments, it becomes hard to solve the challenge of whether to exploit an action with known value or search for even better alternatives. In reinforcement learning, the intractable exploration/exploitation tradeoff is typically handled by controlling the temperature parameter of the softmax stochastic exploration policy or by encouraging the selection of uncertain options.We describe how selectively maintaining high-value actions in a manner that reduces their information content helps to resolve the exploration/exploitation dilemma during a reinforcement-based timing task. By definition of the softmax policy, the information content (i.e., Shannon’s entropy) of the value representation controls the shift from exploration to exploitation. When subjective values for different response times are similar, the entropy is high, inducing exploration. Under selective maintenance, entropy declines as the agent preferentially maps the most valuable parts of the environment and forgets the rest, facilitating exploitation. We demonstratein silicothat this memory-constrained algorithm performs as well as cognitively demanding uncertainty-driven exploration, even though the latter yields a more accurate representation of the contingency.We found that human behavior was best characterized by a selective maintenance model. Information dynamics consistent with selective maintenance were most pronounced in better-performing subjects, in those with higher non-verbal intelligence, and in learnable vs. unlearnable contingencies. Entropy of value traces shaped human exploration behavior (response time swings), whereas uncertainty-driven exploration was not supported by Bayesian model comparison. In summary, when the action space is large, strategic maintenance of value information reduces cognitive load and facilitates the resolution of the exploration/exploitation dilemma.Author summaryA much-debated question is whether humans explore new options at random or selectively explore unfamiliar options. We show that uncertainty-driven exploration recovers a more accurate picture of simulated environments, but typically does not lead to greater success in foraging. The alternative approach of mapping the most valuable parts of the world accurately while having only approximate knowledge of the rest is just as successful, requires less representational capacity, and provides a better explanation of human behavior. Furthermore, when searching among a multitude of response times, people cannot indefinitely maintain information about every experience. A good strategy for someone with limited memory capacity is to selectively maintain a valuable subset of options and gradually forget the rest. In simulated worlds, a player with this strategy was as successful as a player that represented all previous experiences. When learning a time-varying contingency, humans behaved in a manner consistent with a selective maintenance account. The amount of information retained under this strategy is high early in learning, encouraging exploration, and declines after one has discovered valuable response times.

The newly proposed Federal "Right to Try" Act permits terminally ill patients to try experimental therapies that have completed phase I testing but have not been approved by the FDA. However, there are concerning provisions of the legislature regarding liability and costs.