Scalable and robust DNA-based storage via coding theory and deep learning
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Development of accessible and scalable maize pollen storage technology
The inherent short lifespan of Zea mays (maize, corn) pollen hinders crop improvement and challenges the hybrid seed production required to produce food, fuel, and feed. Decades of scientific effort on maize pollen storage technology have been unable to deliver a widely accessible protocol that works for liters of pollen at a hybrid seed production scale. Here we show how suppressing the pollen cellular respiration rate through refrigeration and optimizing gas exchange within the storage environment are the critical combination of factors for maintaining pollen viability in storage. The common practice of preserving maize pollen by mixing the pollen with talcum powder is critically examined using pollen tube germination testing, electron microscopy of pollen-silk (stigma) interaction, and test pollinations in production environments. These techniques lead to mixing maize pollen collected for storage with anti-clumping carrier compounds, including microcrystalline cellulose. These carriers improve stored pollen flowability during pollination and enable increased seed sets to be obtained from stored pollen. Field testing in maize seed production demonstrates that a wide range of pollen volumes can be stored for up to seven days using low-cost, globally available materials and that stored pollen can achieve seed-set equivalency to fresh pollen.
Predictive learning as the basis of the testing effect
A prominent learning phenomenon is the testing effect, meaning that testing enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive learning posits that learning occurs based on the contrast (error) between a prediction and the feedback on that prediction (prediction error). Here, we propose that in testing (but not studying) scenarios, participants predict potential answers, and its contrast with the subsequent feedback yields a prediction error, which facilitates testing-based learning. To investigate this, we developed an associative memory network incorporating Hebbian and/or predictive learning, together with an experimental design where human participants studied or tested English-Swahili word pairs followed by recognition. Three behavioral experiments (N = 80, 81, 62) showed robust testing effects when feedback was provided. Model fitting (of 10 different models) suggested that only models incorporating predictive learning can account for the breadth of data associated with the testing effect. Our data and model suggest that predictive learning underlies the testing effect.
EV DNA from pancreatic cancer patient-derived cells harbors molecular, coding, non-coding signatures and mutational hotspots
DNA packaged into cancer cell-derived EV is not well appreciated. Here, we uncovered signatures of EV DNA secreted by pancreatic cancer cells. The cancer cells and non-cancer counterparts exhibit distinct low vs. high molecular weight (LMW vs. HMW) EV DNA fragments distribution, respectively. Genome sequencing and Single Nucleotide Variants analysis revealed that 95% of reads and 94% of SNVs map to noncoding regions of the genome. Given that ~1% of the human genome represents coding regions, the 5% mapping rate to coding regions suggests a non-random enrichment of certain coding regions and mutations. The LMW DNA fragments not only set cancer cells apart, but also harbor cancer specific enrichment of unique coding regions, the top nine being FAM135B, COL22A1, TSNARE1, KCNK9, ZFAT, JRK, MROH5, GSDMD, and MIR3667HG. Additionally, the cancer cells’ LMW DNA fragments exhibit dense centromeric mapping more strikingly on chromosomes 3, 7, 9, 10, 11, 13, 17, and 20. Mutational profiling turned up close to 200 mutations specific for the cancer cells. Altogether, our analyses suggest that centromeric regions might hold clues to EV DNA content from pancreatic cancer, the molecular, mutational signatures thereof, and rationalizes the need for a new approach to DNA biomarker research.
Understanding learning through uncertainty and bias
Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms that improve predictions under uncertainty. Drawing on normative learning models, we illustrate how learning should be adjusted to different sources of uncertainty, including perceptual uncertainty, risk, and uncertainty due to environmental changes. Such models explain many hallmarks of human learning in terms of specific statistical considerations that come into play when updating predictions under uncertainty. However, humans also display systematic learning biases that deviate from normative models, as studied in computational psychiatry. Some biases can be explained as normative inference conditioned on inaccurate prior assumptions about the environment, while others reflect approximations to Bayesian inference aimed at reducing cognitive demands. These biases offer insights into cognitive mechanisms underlying learning and how they might go awry in psychiatric illness.
Community composition and physiological plasticity control microbial carbon storage across natural and experimental soil fertility gradients
Many microorganisms synthesise carbon (C)-rich compounds under resource deprivation. Such compounds likely serve as intracellular C-storage pools that sustain the activities of microorganisms growing on stoichiometrically imbalanced substrates, making them potentially vital to the function of ecosystems on infertile soils. We examined the dynamics and drivers of three putative C-storage compounds (neutral lipid fatty acids [NLFAs], polyhydroxybutyrate [PHB], and trehalose) across a natural gradient of soil fertility in eastern Australia. Together, NLFAs, PHB, and trehalose corresponded to 8.5–40% of microbial C and 0.06–0.6% of soil organic C. When scaled to “structural” microbial biomass (indexed by polar lipid fatty acids; PLFAs), NLFA and PHB allocation was 2–3-times greater in infertile soils derived from ironstone and sandstone than in comparatively fertile basalt- and shale-derived soils. PHB allocation was positively correlated with belowground biological phosphorus (P)-demand, while NLFA allocation was positively correlated with fungal PLFA : bacterial PLFA ratios. A complementary incubation revealed positive responses of respiration, storage, and fungal PLFAs to glucose, while bacterial PLFAs responded positively to PO43-. By comparing these results to a model of microbial C-allocation, we reason that NLFA primarily served the “reserve” storage mode for C-limited taxa (i.e., fungi), while the variable portion of PHB likely served as “surplus” C-storage for P-limited bacteria. Thus, our findings reveal a convergence of community-level processes (i.e., changes in taxonomic composition that underpin reserve-mode storage dynamics) and intracellular mechanisms (e.g., physiological plasticity of surplus-mode storage) that drives strong, predictable community-level microbial C-storage dynamics across gradients of soil fertility and substrate stoichiometry.
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