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Abstract The fms-related tyrosine kinase 3 (Flt3) and its ligand (Flt3lg) are important regulators of hematopoiesis and dendritic cell (DC) homeostasis with unsettled coevolution. Gene synteny and deduced amino acid sequence analyses identified conserved flt3 gene orthologs across all jawed vertebrates. In contrast, flt3lg orthologs were not retrieved in ray-finned fish, and the gene locus exhibited more variability among species. Interestingly, duplicated flt3/flt3lg genes were maintained in the allotetraploid Xenopus laevis. Comparison of modeled structures of X. laevis Flt3 and Flt3lg homoeologs with the related diploid Xenopus tropicalis and with humans indicated a higher conformational divergence between the homoeologous pairs than their respective counterparts. The distinctive developmental and tissue expression patterns of Flt3 and Flt3lg homoeologs in tadpoles and adult frogs suggest a subfunctionalization of these homoeologs. To characterize Flt3 cell surface expression, X. laevis–tagged rFlt3lg.S and rFlt3lg.L were produced. Both rFlt3lg.S and rFlt3lg.L bind in vitro Flt3.S and Flt3.L and can trigger Erk1/2 signaling, which is consistent with a partial overlapping function between homoeologs. In spleen, Flt3.S/L cell surface expression was detected on a fraction of B cells and a population of MHC class IIhigh/CD8+ leukocytes phenotypically similar to the recently described dual follicular/conventional DC-like XL cells. Our result suggests that 1) Flt3lg.S and Flt3lg.L are both involved in XL cell homeostasis and that 2) XL cells have hematopoietic origin. Furthermore, we detected surface expression of the macrophage/monocyte marker Csf1r.S on XL cells as in mammalian and chicken DCs, which points to a common evolutionary origin in vertebrate DCs. 

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles. 

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias–variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice. The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple enough to avoid fitting spurious patterns. However, in modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered overfitted, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This “double-descent” curve subsumes the textbook U-shaped bias–variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning. 

Remarkable recent success of deep neural networks has not been easy to analyze theoretically. It has been particularly hard to disentangle relative significance of architecture and optimization in achieving accurate classification on large datasets. On the flip side, shallow methods (such as kernel methods) have encountered obstacles in scaling to large data, despite excellent performance on smaller datasets, and extensive theoretical analysis. Practical methods, such as variants of gradient descent used so successfully in deep learning, seem to perform below par when applied to kernel methods. This difficulty has sometimes been attributed to the limitations of shallow architecture. In this paper we identify a basic limitation in gradient descent-based optimization methods when used in conjunctions with smooth kernels. Our analysis demonstrates that only a vanishingly small fraction of the function space is reachable after a polynomial number of gradient descent iterations. That drastically limits the approximating power of gradient descent leading to over-regularization. The issue is purely algorithmic, persisting even in the limit of infinite data. To address this shortcoming in practice, we introduce EigenPro iteration, a simple and direct preconditioning scheme using a small number of approximately computed eigenvectors. It can also be viewed as learning a kernel optimized for gradient descent. Injecting this small, computationally inexpensive and SGD-compatible, amount of approximate second-order information leads to major improvements in convergence. For large data, this leads to a significant performance boost over the state-of-the-art kernel methods. In particular, we are able to match or improve the results reported in the literature at a small fraction of their computational budget. 

Artificial smart skins that integrate sensing and adaptiveness present a novel platform in wearable electronics on epidermis or next‐generation robotics. Herein, a highly sensitive capacitive touch senor based on a self‐conformable bi‐stable electroactive polymer (BSEP) is developed. The device combines the properties of conformable polymers and touch sensors, which grants the sensor the ability to conform to the shape of various surfaces and in different working conditions. A spray‐coated silver nanowire (AgNW) is selected as the sensor electrode for high‐resolution patterning. The unique antenna‐shaped electrode pattern results in a capacitance change of 31% when in contact with ground at a baseline of 0.13 pF. The BSEP provides stiffness tunability via an embedded compliant heater. The heater combines interdigitated silver with carbon nanotubes delivering uniform and highly efficient heating to create a tunable device with stiffness between 100s of MPa and tens of kPa, providing a large working flexibility. The efficient resistive heater provides uniform and stable heating over an area of 40 by 40 mm with a rate of 48 °C min⁻¹at an input voltage as low as 7 V. This research merges intelligent polymeric systems and thin film electronics advancing conformable, skin‐like functional electronics.