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Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.

\"Play It Forward details the remarkable journey of Joan Barnes, founder and former CEO of Gymboree, and how she learned to align her inner life with outward success. Forty years ago, Joan Barnes founded a play center in a church basement with $3,000. Determined to enable women to achieve personal and entrepreneurial success, Barnes led Gymboree to become an innovative leader in a new industry: activity-based early childhood development. The company eventually became a global billion-dollar brand. But this dramatic entrepreneurial memoir is also a cautionary tale and redemption story. When Gymboree's IPO became a phenomenal success story, Barnes was nowhere near Wall Street. She had left the company because of an eating disorder that threatened to destroy her and everything she built. Barnes overcame the disorder, charting a path that replaced demons with an enduring sense of worth and hope. She eventually resumed her business career on healthier terms with a line of yoga studios in an inspiring example of how women can triumph through reinvention. Published to coincide with Gymboree's 40th anniversary, Play It Forward offers readers a deeply honest perspective of the challenges of business building and seeking a work-life balance in tune with personal values\"-- Provided by publisher.

The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes. Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups. These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable.

Eighteen-year-old Francesca comes to England to achieve her life-long ambition of learning to fly. But when her instructor's actions lead Francesca to question his motives, she starts to lose her confidence.

Does overall thermal equilibrium exist between ions and electrons in a weakly collisional, magnetized, turbulent plasma? And, if not, how is thermal energy partitioned between ions and electrons? This is a fundamental question in plasma physics, the answer to which is also crucial for predicting the properties of far-distant astronomical objects such as accretion disks around black holes. In the context of disks, this question was posed nearly two decades ago and has since generated a sizeable literature. Here we provide the answer for the case in which energy is injected into the plasma via Alfvénic turbulence: Collisionless turbulent heating typically acts to disequilibrate the ion and electron temperatures. Numerical simulations using a hybrid fluid-gyrokinetic model indicate that the ion–electron heating-rate ratio is an increasing function of the thermal-to-magnetic energy ratio, β i: It ranges from ∼0:05 at β i = 0:1 to at least 30 for β i ≳ 10. This energy partition is approximately insensitive to the ion-to-electron temperature ratio T i/T e. Thus, in the absence of other equilibrating mechanisms, a collisionless plasma system heated via Alfvénic turbulence will tend toward a nonequilibrium state in which one of the species is significantly hotter than the other, i.e., hotter ions at high β i and hotter electrons at low β i. Spectra of electromagnetic fields and the ion distribution function in 5D phase space exhibit an interesting new magnetically dominated regime at high β i and a tendency for the ion heating to be mediated by nonlinear phase mixing (“entropy cascade”) when β i ≲ 1 and by linear phase mixing (Landau damping) when β i ≫ 1.

In this study, we aimed to determine the impact of U.S. government stimulus payments on family health during the COVID-19 pandemic. We hypothesized that receiving stimulus checks is associated with better family health and the effect of stimulus check receipt differs by income level. Additionally, we hypothesized that spending on immediate needs and paying off loans is associated with worse family health, and the effects of this spending differ by income level. Participants included 456 registered Amazon Mechanical Turk (mTurk) users, stratified by income, marital status, and parental status. We used the Family Health Scale – Long Form to measure family health constructs: social-emotional health, healthy lifestyle, health resources, and social support. For all statistical analyses, we used SAS Studio 3.8. We performed an exploratory factor analysis to determine six spending profiles: loans, savings, housing, household supplies, durable goods, and medical costs. After adjustment, our multiple linear regression model found that mean family health and social-emotional health scores were higher among individuals who received all three checks, but this did not differ by income category. Mean family health and social-emotional health were lower among individuals who spent more significant portions of their stimulus checks on housing, household supplies, and medical costs. Spending greater portions of checks on medical costs was associated with lower scores among every family health construct except family healthy lifestyle. Among mid-to-high-income participants, family health scores were significantly lower, with more spending on housing, household supplies, durable goods, and medical costs, with similar results in the subscale scores. The reduction of family health scores with spending on medical costs and durable goods were more pronounced among the mid-to-high-income group than the low-income group. Stimulus payments may be a promising family policy method for improving overall family health; however, more research should address the differences between income groups and government assistance.

To maximise the clinical benefits of machine learning algorithms, we need to rethink our approach to explanation, argue David Watson and colleagues