Burroughs Wellcome Fund

In 2009, the world was introduced to a new monetary concept that would dramatically change the way individuals interacted with finance with the release of Bitcoin, the first successful cryptocurrency.Over time, a vast array of these cryptocurrencies have continued to arise, gaining significant transactions and eliciting uncertainty regarding how these new currencies will affect traditional currencies, especially the United States dollar which is currently the international reserve currency.Given the monetary unrest caused by the rise of these digital currencies, which pose an alleged threat to socioeconomic stability and international trade, this research paper examines what could happen to the value of the dollar if the US Government were to issue an official cryptocurrency.

In Reinforcement learning (RL), a subfield of machine learning, we train systems to perform complex tasks through trial and error. In RL, an agent interacts with an environment, taking actions that generate a cumulative reward. If a series of actions generates a high cumulative reward, those actions will be favorable in the future. Some applications of RL include improving the performance of self-driving cars, improving the performance of large language models, and playing games like Go. While playing games might not have a direct impact on the real world, systems like AlphaGo have helped improve our understanding of RL that aids in more real-world applications. This study uses game completion as a test bed to better understand the underlying mechanisms behind RL, specifically the effects of tuning hyperparameters on a model’s performance. A Deep Q-Learning (DQN) model architecture was chosen for this analysis, and we tuned batch size, learning rate, exploration rate, and discount factor. We hypothesized optimizing these hyperparameters would increase the cumulative reward. These hyperparameters were tuned to maximize the score in the game of Atari Breakout. We found that altering the discount factor to be greater than or less than one results in a much less effective model, whereas tuning hyperparameters that were changed caused little change to the performance. The results of this study can be used to improve the performance of future RL models. All code to reproduce results in this study is available at: https://github.com/BobyWoby/Reinforcement-Learning

BackgroundCopper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB). ObjectivesThis study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations. MethodsMaternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort. ResultsThe maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 μg/mL and standard deviation of 0.43 μg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 μg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration. ConclusionsOur study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB.

The fungal kingdom represents an extraordinary diversity of organisms with profound impacts across animal, plant, and ecosystem health. Fungi simultaneously support life, by forming beneficial symbioses with plants and producing life-saving medicines, and bring death, by causing devastating diseases in humans, plants, and animals. With climate change, increased antimicrobial resistance, global trade, environmental degradation, and novel viruses altering the impact of fungi on health and disease, developing new approaches is now more crucial than ever to combat the threats posed by fungi and to harness their extraordinary potential for applications in human health, food supply, and environmental remediation. To address this aim, the Canadian Institute for Advanced Research (CIFAR) and the Burroughs Wellcome Fund convened a workshop to unite leading experts on fungal biology from academia and industry to strategize innovative solutions to global challenges and fungal threats. This report provides recommendations to accelerate fungal research and highlights the major research advances and ideas discussed at the meeting pertaining to 5 major topics: (1) Connections between fungi and climate change and ways to avert climate catastrophe; (2) Fungal threats to humans and ways to mitigate them; (3) Fungal threats to agriculture and food security and approaches to ensure a robust global food supply; (4) Fungal threats to animals and approaches to avoid species collapse and extinction; and (5) Opportunities presented by the fungal kingdom, including novel medicines and enzymes.

ABSTRACTGene regulation is essential to placental function and fetal development. We report a genome-scale transcriptional regulatory network (TRN) of the human placenta built using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out of sample R2value greater than 0.25 for 74% of target genes. We performed siRNA knockdowns of 4 TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for 3 of the 4 TFs. Our final model contained 113,158 interactions across 391 TFs and 7,712 target genes and is publicly available. We identified six TFs which were significantly enriched as regulators for genes previously associated with preterm birth.

ABSTRACTBackgroundPregnant women in Malawi are at risk of selenium deficiency, which can have adverse effects on pregnancy outcomes. Interventions for improving selenium status are needed.ObjectivesTo assess the effect of provision of small-quantity lipid-based nutrient supplements (SQ-LNSs) to Malawian women during pregnancy on their plasma selenium concentrations at 36 wk of gestation.MethodsPregnant women (≤20 wk of gestation) were randomly assigned to receive daily either: 1) iron and folic acid (IFA); 2) multiple micronutrients (MMN; 130 µg selenium per capsule); or 3) SQ-LNS (130 µg selenium/20 g). Plasma selenium concentrations were measured by inductively coupled plasma mass spectrometry at baseline and after ≥16 wk of intervention (at 36 wk of gestation) and compared by intervention group.ResultsAt 36 wk of gestation, median (quartile 1, quartile 3) plasma selenium concentrations (micromoles per liter) were 0.96 (0.73, 1.23), 0.94 (0.78, 1.18), and 1.01 (0.85, 1.28) in the IFA, MMN, and SQ-LNS groups, respectively. Geometric mean (GM) plasma selenium concentration was 5.4% (95% CI: 1.8%, 9.0%) higher in the SQ-LNS group than in the MMN group and tended to be higher than in the IFA group (+4.2%; 95% CI: 1.0%, 7.8%). The prevalence of adjusted plasma selenium concentrations <1 µmol/L was 55.1%, 57.8%, and 47.3% in the IFA, MMN, and SQ-LNS groups, respectively; it was lower in the SQ-LNS group than in the MMN group, OR = 0.44 (95% CI: 0.24, 0.83), and tended to be lower than in the IFA group, OR = 0.54 (95% CI: 0.29, 1.03). There was a significant interaction between baseline plasma selenium concentration and intervention group (P = 0.003). In the lowest tertile of baseline selenium concentrations, GM plasma selenium concentration was higher, and the prevalence of low values was lower in the SQ-LNS group compared with the MMN and IFA groups at 36 wk of gestation (P ≤ 0.007).ConclusionsProvision of SQ-LNS containing selenium to pregnant women can be an effective strategy for improving their selenium status.This trial was registered at clinicaltrials.gov (identifier: NCT01239693).

Heat shock proteins are involved in the response to stress including activation of the immune response. Elevated circulating heat shock proteins are associated with spontaneous preterm birth (SPTB). Intracellular heat shock proteins act as multifunctional molecular chaperones that regulate activity of nuclear hormone receptors. Since SPTB has a significant genetic predisposition, our objective was to identify genetic and transcriptomic evidence of heat shock proteins and nuclear hormone receptors that may affect risk for SPTB. We investigated all 97 genes encoding members of the heat shock protein families and all 49 genes encoding nuclear hormone receptors for their potential role in SPTB susceptibility. We used multiple genetic and genomic datasets including genome-wide association studies (GWASs), whole-exome sequencing (WES), and placental transcriptomics to identify SPTB predisposing factors from the mother, infant, and placenta. There were multiple associations of heat shock protein and nuclear hormone receptor genes with SPTB. Several orthogonal datasets supported roles for SEC63, HSPA1L, SACS, RORA, and AR in susceptibility to SPTB. We propose that suppression of specific heat shock proteins promotes maintenance of pregnancy, whereas activation of specific heat shock protein mediated signaling may disturb maternal–fetal tolerance and promote labor.