Philippa Kaur

Building from this background the NAM took on these issues as its first-ever grand challenge, as a critical issue of import and urgency for us all. In 2018, the NAM empaneled an international, independent and multidisciplinary commission to create a global roadmap for healthy longevity, complete with evidence-based, targeted and actionable recommendations to move societies forward from an almost-exclusive focus on ‘coping with aging populations’ toward enabling individuals and societies to age successfully, and to reap the economic and societal benefits of longevity. The commission offers a way forward for governments and societies by beginning with recommendations for the next five years, and how these solutions can be financially sustainable through the creation of a virtuous cycle.To support these goals, the commission was to “(1) comprehensively address the challenges and opportunities presented by global aging population; (2) catalyze breakthrough ideas and research that will extend the human healthspan; and (3) generate transformative and scalable innovations world wide”8. The resulting comprehensive report, which was delayed in good measure by the COVID-19 pandemic, was released in June 2022 (ref. 8). We report here a summary of the high-level vision, goals, findings and recommendations of this global roadmap.The evidence for opportunities of longevity and the costs of inactionWe are seeing longer lives with increasing years spent in ill health (that is, the decompression of morbidity)9. The implications of longevity without health are costly ones for the individual, their families and for society. By contrast, scientific evidence shows that the majority of chronic diseases are preventable, and that prevention works at every age and stage of life. Further, the subset of individuals who are the beneficiaries of cumulative health-promoting conditions across the life course are demonstrating healthy longevity, defined as “the state in which years in good health approach the biological lifespan, with physical, cognitive and social functioning, enabling well-being across populations”8. However, only a minority of people in any country have the benefit of the necessary investments that promote health, and disparities in access to these investments across the life course are a major cause of unhealthy longevity. The costs of inaction in the face of widening disparities include the high risk of young people aging with more ill health, and the attendant costs to them and society.Further, the commission reports that when people have health and function in older age, the considerable cognitive and socioemotional capabilities and expertise that accrue with aging, and the prosocial goals of older age, constitute human and social capital assets that are unprecedented in both nature and scale. Contrary to disproven myths, workforce participation not only brings these valuable capabilities (such that intergenerational teams in the workplace are more productive and innovative than single-age-group teams), but older people working is also associated with more jobs for younger individuals10. In the USA and EU, it has been shown that older adults contribute 7% of gross domestic product (GDP) through paid work and the economic value of volunteering and caregiving11, even before opportunities are specifically expanded for the increasing older population. Societies that recognize this potential and invest to create both healthy longevity and the societal organizations and policies through which older adults can contribute to societal good will develop the opportunity for all ages to thrive. The return on investment will be to create older ages with health, function, dignity, meaning, purpose and opportunities — for those who desire it — to work longer, care for others or contribute in ways that they value to their community and future generations.The definition, principles and vision of ‘Vision 2050’ for healthy longevityThe global roadmap builds on the WHO ‘Decade of Healthy Ageing’, the UN Sustainable Development Goals for 2030 and other reports. It sets out principles for achieving healthy longevity using data and meaningful metrics to track achievement of outcomes and guide decision making. The report offers a vision empowered by the evidence: that, by 2050, societies will value the capabilities and assets of older people; all people will have the opportunity to live long lives with health and function; barriers to full participation by older people in society will have been solved; and that older people, with such health, will have the opportunity to engage in meaningful and productive activities. In turn, this societal engagement will create unprecedented social, human and economic capital, contributing to intergenerational well-being and cohesion, and to GDP.Implementing Vision 2050Accomplishing this vision demands ‘all-of-society’ intent — with aligned goals for healthy longevity and transformative action across public, private and academic sectors, and all of civil society and communities — and the implementation of evidence across the full and extending life course. Transforming only one component or sector (for example, health systems) will not be sufficient to create healthy longevity or its full opportunities. Rather, given that nations are complex systems, this vision for our future requires governmental leadership and transformation of all sectors of our complex societal system (Fig. 1).Fig. 1: Relevant actors for an all-of-society approach to healthy longevity.Healthy longevity requires government leadership and cooperation across all sectors. Adapted with permission from figure S-2 of ref. 8.Full size imageInvestment for healthy longevity — across the enabling sectors of health systems, social infrastructure and protections, the physical environment, and work and volunteering contributions — will require intentional planning and leadership to transform those components in tandem, and to resolve disrupters such as ageism, the social determinants of health and inequity, and pollution. These investments across all sectors will create the conditions for achieving healthy longevity and build new capital (human, social and economic) that will benefit all of society. As a result of these investments, society will see younger people thrive and move into a position to age with healthy longevity; those individuals who are already older will be recognized as valuable contributors to society in a ‘pay-it-forward’ stage of life. The underpinning social compact between citizens and government will support valuing each age group’s capabilities and goals, and the building of a society of well-being and cohesion across generations. This is at the center of the virtuous cycle for healthy longevity (Fig. 2)Fig. 2: The virtuous cycle of healthy longevity.Healthy longevity (top) is an outcome of a virtuous cycle, itself contributing to capital development (bottom left). Bottom right, capital (human, financial and social) supports enablers (work, physical environment, health systems and social infrastructure). The enablers propel the cycle, contributing to healthy longevity. Intentional investment for healthy longevity across all enabling sectors will create new capital that will benefit all of society. Adapted with permission from figure 1-4 of ref. 8.Full size imageGoals for initiating the transformation to healthy longevityThe commission identified the following changes that should occur from now to 2027 to start transformation of all of society, towards Vision 2050 and the creation of healthy longevity for all:

Creating social cohesion, social engagement and addressing the social determinants of health through social infrastructure are among the most effective determinants of slowed aging and the prevention of chronic conditions across the life course. Financial security in older age is essential for all.

Governments, the private sector and civil society should partner to design physical environments and infrastructure that are user-centered, and function as cohesion-enabling intergenerational communities for healthy longevity. Initiatives should focus on the inclusion of older people in the design, creating public spaces that promote social cohesion and intergenerational connection as well as mobility, physical activity and access to food, transportation, social services and engagement.

By 2027, governments should develop strategies and plans to arrive at adequately sized, geriatrically knowledgeable public health, clinical and long-term care workforces, and an integration of the pillars of the health system and social services. Together, these dimensions would foster and extend years of good health and support the diverse health needs and well-being of older people.

Governments should work to build the dividend of health longevity in collaboration with the business sector and civil society, to develop policies, incentives, and supportive systems that enable and encourage lifelong learning, and greater opportunities and necessary skills to engage in meaningful work or community volunteering across the lifespan.

We summarize the commission’s recommended goals for each of these sectors in brief in Box 1. Across all sectors, the key first steps that the commission identified are ones that can resolve obstacles to change and plan the change needed to shift multiple complex systems through both top-down and bottom-up approaches, in ways appropriate to each country and context. These initiatives should create enough momentum to foster early returns on investment and optimism to propel sustained investment for subsequent stages. This would need to begin for all governments by 2023, establishing calls to action to develop and implement data-driven, all-of-society plans to build the systems, policies, organizations and infrastructure needed, and for tracking change.Box 1 Goals for 2022–2027 to initiate the transformation to healthy longevityThese goals are reproduced from Global Roadmap for Healthy Longevity8.
Social infrastructure

Develop evidence-based multipronged strategies to reduce ageism against all groups.

Develop plans for ensuring basic financial security for all older people.

Develop strategies to increase financial literacy and mechanisms for promoting working longer, pension options and savings over the life course.

Plan opportunities for purposeful and meaningful engagement by older people at the family, community and societal levels.

Physical environment

At the societal level, improve broadband accessibility to reduce the digital divide and develop public transportation solutions that address first- and last-mile transportation.

At the city level, implement mitigation strategies to reduce the negative effects of the physical environment and related emergencies on older people (for example, air pollution and climate-induced events, including extreme heat and flooding) and design environments for connection and cohesion.

At the neighborhood level, promote and measure innovative policy solutions for healthy longevity, including affordable housing and intergenerational living, zoning and design for connection and cohesion, and the enabling of social capital.

At the home level, update physical infrastructure and policies to address affordability, provide coliving arrangements that match people’s goals and needs, and resolve insufficiencies and inefficiencies in housing stock.

Health systems

Establish healthy longevity as a major goal.

Increase investments in public health systems, which are needed to promote health and prevent disease, disability and injury at the population level, across the full life course. This may require rebalancing investments between this type of public health and medical care, recognizing that such public health is a public good and, as such, tends to be underinvested in.

Provide adequate primary care that includes preventive screening, addresses risk factors for chronic conditions and promotes positive health behaviors, and offers a continuum of medical care, including geriatrically knowledgeable care for older adults.

Make culturally sensitive, person-centered and equitable long-term care systems available, which (to the degree possible) offer dignity and honor people’s preferences about care settings.

Building the healthy longevity dividend

Governments, in collaboration with the business sector and civil society, should design (1) work environments and develop new policies that enable and encourage older adults who want or need to remain in the work force longer, and (2) engagement opportunities that strengthen communities at every stage of life.

Governments, employers and educational institutions should prioritize redesigning education systems to support lifelong learning and training, and invest in the science of learning and training for middle-aged and older adults.

Pilot innovations that incentivize and allow middle-aged and older adults to retool for multiple careers and/or participate as volunteers across their lifespan in roles with meaning and purpose. More

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General methodological approachTo examine if larvae utilize external cues (i.e., oriented movement) to swim in a directional manner (i.e., significant mean vector length), we develop two complementary analyses that compare the empirically observed directional precision (i.e., mean vector length) with the null distribution expected under a strict use of internal cues (i.e., unoriented movement). The empirically observed directional precision is quantified as the mean vector length (R) of larval bearings (θ) (Fig. 2a), herein ({hat{R}}_{theta }). The angular differences between consecutive bearings, herein turning angles (Fig. 2a; Δθt = θt-θt-1), are used to generate two null distributions of Rθ expected under the unoriented movement of Correlated Random Walk (CRW; ({R}_{{theta }_{0}})), based on the two analyses: Correlated Random Walk-von Mises (CRW-vm) and Correlated Random Walk- resampling (CRW-r), described below. The first is theoretical and is based on a von Mises distribution of simulated Δθ (Fig. 2b, c); the second is empirical, and is based on resampling the Δθ within each trial (Fig. 2d, e). These two analyses are complementary because the first can generate an unlimited number of trajectories but is based on a theoretical distribution rather than on observations, whereas the second is based on a finite number of observations. In addition to these two main analyses, we apply a third analysis, the Correlated Random Walk-wrapped Cauchy, herein CRW-wc, which is similar to CRW-vm, with the only difference of using wrapped Cauchy distribution instead of von Mises. The reason for applying CRW-wc is that it was shown to represent well animal movement in some cases33. Notably, we consider the simple cases of undirected movement pattern with a turning angle distribution centered at 0 (CRW), testing if the mean vector length of the trial’s sequence is higher than that expected under CRW. If true, that would be an indication for a directed movement pattern (i.e., BRW or BCRW), or an indication for more complex behaviors (discussed in Supplementary note 4).Statistics and reproducibilityQuantitative analyses are applied to directional trials, i.e., larval bearing sequences ((hat{theta })) that are significantly different from a uniform distribution based on the Rayleigh’s test8 (p  81, 162, 270). Trials with Nobs higher than the maximal Nobs were trimmed to contain the maximal Nobs per species, retaining the later-in-time data. For the scuba-following trials, the number of observations had to be Nobs  > 20 due to the sensitivity of the analysis to a low number of observations. In other words, a low number of observations limits the capacity of the quantitative analyses to distinguish between oriented and unoriented movement patterns (see Supplementary note 3, Supplementary Figure S3). Importantly, both methods were shown to be robust in terms of artifacts and biases55,56, and have been tested together demonstrating high consistency in larval orientation results16,48.Each orientation trial includes a sequence of larval swimming directions, termed bearings (θ) (Fig. 2a). For the DISC trials, θ are the cardinal directions of larval positions within the DISC’s chamber55. The angular differences between θ of consecutive time steps (t) are defined as Δθ (Δθt = θt-θt-1), such that for every θ sequence of a given length (N), there is a respective Δθ sequence of length N-1 (Fig. 2a). Directional precision with respect to external and internal cues is computed as the mean vector length of bearings (Rθ) and of turning angles (RΔθ), respectively54. Values of mean vector length (R) range from 0 to 1, with 0 indicating a uniform distribution of angles and 1 indicating that all angles are the same.We used two quantitative approaches to examine if larvae exhibit oriented movement: the Correlated Random Walk- von Mises and Correlated Random Walk- wrapped Cauchy (CRW-vm and CRW-wc) analyses and the CRW resampling (CRW-r) analysis. Both types of analyses are based on the assumption that trajectories of animals that strictly use internal cues for directional movement are characterized by a CRW pattern. Hence, their capacity for directional movement is exclusively dependent on the distribution of their turning angles (Δθ)57. In contrast, for an external-cues orienting animal, for which movement directions are correlated with an external fixed direction, the mean vector length of the observed bearings, ({hat{R}}_{theta }), is expected to exceed that of a CRW, ({R}_{{theta }_{0}})6. Both analyses compare ({hat{R}}_{theta }) against the expected ({R}_{{theta }_{0}}), but the first type computes ({R}_{{theta }_{0}^{{vm}}})and ({R}_{{theta }_{0}^{{wc}}})using theoretical von Mises and wrapped Cauchy distributions of Δθ, and the second type computes ({R}_{{theta }_{0}^{r}}) by producing 100 new θ sequences per individual trial (larva) by multiple resampling-without-replacement of the Δθ.A key principle for both analyses types stems from the fact that the mean vector length of bearings (Rθ) is inherently dependent on the mean vector length of turning angles (RΔθ)28. In other words, an animal with a high capacity for unoriented directional movement, i.e., a narrow distribution of Δθ, is likely to yield a high Rθ, even if it makes absolutely no use of external cues for oriented movement. Hence, in both analyses ({hat{R}}_{theta }) is gauged against a distribution of ({R}_{{theta }_{0}}), given its respective mean vector length of turning angles ({hat{R}}_{triangle theta }). The open-source software R58 with the package circular59 is used for all analyses in this study.Correlated Random Walk-von Mises (CRW-vm)In this analysis, we first generate the directional precision (R), expected for unoriented CRW movement using the theoretical von Mises distribution (({R}_{{theta }_{0}^{{vm}}})). The CRW bearings sequences (({theta }_{0}^{{vm}})) are generated by choosing a random initial bearing, followed by a series of Nobs-1 turning angles (({triangle theta }_{0}^{{vm}})) in bearing direction; drawn at random (with replacement) from a von Mises distribution (Nrep = 1000). The length of ({theta }_{0}^{{vm}}) sequence is according to the number of observations in our four types of experimental trials: Nobs = 21 for the scuba-following, and 90, 180 and 300 for the DISC (Table 1). The directional precision of the von Mises distribution is dependent on the concentration parameter, kappa. Kappa values ranging from 0 to 399 are applied at 1-unit increments to cover the entire range of directional precision from completely random (kappa = 0), to highly directional (kappa = 399). Next, the directional precision of the bearings (Rθ) and the turning angles (RΔθ) are computed for each simulated sequence of θ (Fig. 2a–c).These respective pairs of values (RΔθ, Rθ) provide the basis for generating the expected relationship between ({R}_{{theta }_{0}^{{vm}}}) and ({R}_{{triangle theta }_{0}^{{vm}}}). Then, for any given kappa value, the following quantiles are computed: 5th, 10th, 20th,….,90th, and 95th (grey vertical distributions in Fig. 2c). Next, smooth spline functions are fitted through all respective quantiles, generating the ({R}_{{theta }_{0}^{{vm}}})quantile contours, which represent the null expectation under CRW. This expected (RΔθ, Rθ) correspondence creates a phase diagram (Fig. 2c), based on which the observed θ patterns are gauged. The procedure is repeated four times to match the among-study differences in the number of θ observations per trial (i.e., Nobs = 21, 90, 180, and 300; see Table 1).To examine if the observed larval movement patterns differ from those expected for unoriented movement (CRW-vm), we compute RΔθ and Rθ for each individual trial (({hat{R}}_{triangle theta }) and ({hat{R}}_{theta })). We then place these values in the phase diagram and examine their positions with respect to ({R}_{{theta }_{0}^{{vm}}}) (Fig. 2c). Larvae with ({hat{R}}_{theta }) substantially higher than ({bar{R}}_{{theta }_{0}^{{vm}}}), are considered to have a higher tendency for a straighter movement than expected under CRW, suggesting oriented movement such as BRW and BCRW (Fig. 2b, c)6,28. Larvae with ({hat{R}}_{theta }) values substantially below ({bar{R}}_{{theta }_{0}^{{vm}}})indicate irregular patterns such as a one-sided drift (right or left). A larva is considered directional if the bearing sequence ((hat{theta })) is significantly different from a uniform distribution based on the Rayleigh’s test (p  More

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Scientists with visible and invisible disabilities take on adversity, helping themselves and others.Shigehiro Namiki always wanted to study insects. After his PhD research at the University of Tsukuba, he was a postdoctoral fellow, then a staff scientist at Janelia Research Campus. Among his projects, Namiki worked with others on a method to analyze how the few so-called descending neurons in fruit flies control a wide range of movements and behavior. These neurons run from the brain to the ventral nerve cord and branch out to circuits that control the insect’s neck, legs and wings. More

Honey bee colony loss and parasites across space and timeHoney bee colony loss strongly depends on spatio-temporal factors33,42, which in turn have to be jointly modeled with other stressors. Focusing on CONUS climatic regions, defined by the National Centers for Environmental Information40 (see Fig. 1), this is supported by the box plots in Fig. 2 which depict appropriately normalized honey bee colony loss (upper panel) and presence of V. destructor (lower panel) quarterly between 2015 and 2021. Specifically, Fig. 2a highlights that the first quarter generally accounts for a higher and more variable proportion of losses. Average losses are typically lower and less dispersed during the second quarter, and then tend to increase again during the third and fourth quarters. The Central region, which reports the highest median losses during the first quarter (larger than 20%) exemplifies this pattern, which is in line with existing studies that link overwintering with honey bee colony loss6,29,30,31,32,33,43. On the other hand, the West North Central region follows a different pattern, where losses are typically lower during the first quarter and peak during the third. This holds, albeit less markedly, also for Northwest and Southwest regions. These differing patterns are also depicted in Fig. 3, which shows the time series of normalized colony loss for each state belonging to Central and West North Central regions – with the smoothed conditional means highlighted in black and red, respectively. Figure 2b shows that also the presence of V. destructor tends to follow a specific pattern; in most regions it increases from the first to the third quarter, and then it decreases in the fourth – with the exception of the Southwest region, where it keeps increasing. This is most likely because most beekeepers try to get V. destructor levels low by fall, so that colonies are as healthy as possible going into winter, and also because of the population dynamics of V. destructor alongside honey bee colonies – i.e., their presence typically increases as the colony grows and has more brood cycles, since this parasite develops inside honey bee brood cells44,45. The West region (which encompasses only California since Nevada was missing in the honey bee dataset; see Data) reports high levels of V. destructor throughout the year, with very small variability. A comparison of Fig. 2a and b shows that honey bee colony loss and the presence of V. destructor tend to be higher than the corresponding medians during the third quarter, suggesting a positive association. This is further confirmed in Fig. 4, which shows a scatter plot of normalized colony loss against V. destructor presence, documenting a positive association in all quarters. Although with the data at hand we are not able to capture honey bee movement across states, as well as intra-quarter losses and honey production, these preliminary findings can be useful to support commercial beekeeper strategies and require further investigation.Figure 2Empirical distribution of honey bee (Apis mellifera) colony loss (a) and Varroa destructor presence (b) across quarters (the first one being January-March) and climatic regions; red dashed lines indicate the overall medians. (a) Box plots of normalized colony loss (number of lost colonies over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. At the contiguous United States level, this follows a stable pattern across the years, with higher and more variable losses during the first quarter (see Supplementary Figs. S2-S6), but some regions do depart from this pattern (e.g., West North Central). (b) Box plots of normalized V. destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. The maximum number of colonies is defined as the number of colonies at the beginning of a quarter, plus all colonies moved into that region during the same quarter.Full size imageFigure 3Comparison of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) between Central and West North Central climatic regions for each quarter of 2015–2021 (the first quarter being January-March). (a) Trajectory of each state belonging to Central (yellow) and West North Central (blue) climatic regions. (b) Smoothed conditional means for each of the two sets of curves based on a locally weighted running line smoother where the width of the sliding window is equal to 0.2 and corresponding standard error bands are based on a 0.95 confidence level46.Full size imageFigure 4Scatter plot of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) against normalized Varroa destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each state and each quarter of 2015–2021 (the first quarter being January-March). Points are color-coded by quarter, and ordinary least squares fits (with corresponding standard error bands based on a 0.95 confidence level) computed by quarter are superimposed to visualize the positive association.Full size imageUp-scaling weather dataThe data sets available to us for weather related variables had a much finer spatio-temporal resolution (daily and on a (4 times 4) kilometer grid) than the colony loss data (quarterly and at the state level). Therefore, we aggregated the former to match the latter. For similar data up-scaling tasks, sums or means are commonly employed to summarize the variables available at finer resolution47. The problem with aggregating data in such a manner is that one only preserves information on the “center” of the distributions – thus losing a potentially considerable amount of information. To retain richer weather related information in our study, we considered additional summaries capturing more complex characteristics, e.g., the tails of the distributions or their entropy, to ascertain whether they may help in predicting honey bee colony loss. Within each state and quarter we therefore computed, in addition to means, indexes such as standard deviation, skewness, kurtosis, (L_2)-norm (or energy), entropy and tail indexes48. This was done for minimum and maximum temperatures, as well as precipitation data (see Data processing for details).Next, as a first way to validate the proposed weather data up-scaling approach, we performed a likelihood ratio test between nested models. Specifically, we considered a linear regression for colony loss (see Statistical model) and compared an ordinary least squares fit comprising all the computed indexes as predictors (the full model) against one comprising only means and standard deviations (the reduced model). The test showed that the use of additional indexes provides a statistically significant improvement in the fit (p-(text {value}=0.03)). This test, which can be replicated for other choices of models and estimation methods (see Supplementary Table S5), supports the use of our up-scaling approach.Figure 5 provides a spatial representation of (normalized) honey bee colony losses and of three indexes relative to the minimum temperature distribution; namely, mean, kurtosis and skewness (these all turn out to be relevant predictors based on subsequent analyses; see Table 1). For each of the four quantities, the maps are color-coded by state based on the median of first quarter values over the period 2015-2021 (first quarters typically have the highest losses, but similar patterns can be observed for other quarters; see Supplementary Figs. S12-S14). Notably, the indexes capture characteristics of the within-state distributions of minimum temperatures that do vary geographically. For example, considering minimum temperature, skewness is an index that (broadly speaking) provides information on whether the data tends to accumulate at one end or the other of the observed range of minimum temperatures (i.e., a positive/negative skewness indicates that the data accumulates towards the lower/upper range, respectively). On the other hand, kurtosis is an index that captures the presence of “extreme” values in the tails of the data (i.e., a low/high value of kurtosis indicates that the tail minimum temperatures are relatively close/very far from the typical minimum temperatures). With this in mind, going back to Fig. 5, we can see that minimum temperatures in states in the north-west present large kurtosis (a prevalence of extreme values in the tails) and negative skewness (a tendency to accumulate towards the upper values of the minimum temperature range), while the opposite is true for states in the south-east. More generally, the mean minimum temperature separates northern vs southern states, kurtosis is higher for states located in the central band of the CONUS, and skewness separates western vs eastern states.We further note that the states with lower losses during the first quarter (e.g., Montana and Wyoming) do not report extreme values in any of the considered indexes. Although these states are generally characterized by low minimum temperatures, these are somewhat “stable” (they do not show marked kurtosis or skewness in their distributions) – perhaps allowing honey bees and beekeepers to adapt to more predictable conditions. On the other hand, states with higher losses during the first quarter such as New Mexico have higher minimum temperatures as well as marked kurtosis, and thus higher chances of extreme minimum temperatures – which may indeed affect honey bee behavior and colony loss. Overall, across all quarters of the years 2015-2021, we found that normalized colony losses and mean minimum temperatures are negatively associated (the Pearson correlation is -0.17 with a p-(text {value} More

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