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Plant trait variation drives plant function, community composition, and ecosystem processes. However, our current understanding of trait variation disproportionately relies on aboveground observations. Here we integrate root traits into the global framework of plant form and function. We developed and tested an overarching conceptual framework that integrates two recently identified root trait gradients with a well-established aboveground plant trait framework. We confronted our novel framework with published relationships between above- and belowground trait analogues and with multivariate analyses of aboveground and belowground traits of 2510 species. Our traits represent the leaf- and root conservation gradients (specific leaf area, leaf and root nitrogen concentration and root tissue density), the root collaboration gradient (root diameter and specific root length), and the plant size gradient (plant height and rooting depth). We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth. This perspective on the multi-dimensional nature of plant trait variation better encompasses plant function and influence on the surrounding environment.

High-resolution transmission electron microscopy is an invaluable tool for looking at the crystalline structures of many materials. However, the need for high beam doses, especially as a sample is rotated to find the crystal axes, can lead to damage, particularly in fragile materials. Zhang et al. combined a state-of-the-art direct-detection electron-counting camera with ways to limit the overall electron dose to analyze delicate materials such as metal organic frameworks. With this approach, they could see the benzene rings in a UiO-66 linker and the coexistence of ligand-free (metal-exposing) and ligand-capped surfaces in UiO-66 crystals. Science , this issue p. 675 A direct-detection camera allows for high-resolution transmission electron microscopy imaging of beam-sensitive materials. High-resolution imaging of electron beam–sensitive materials is one of the most difficult applications of transmission electron microscopy (TEM). The challenges are manifold, including the acquisition of images with extremely low beam doses, the time-constrained search for crystal zone axes, the precise image alignment, and the accurate determination of the defocus value. We develop a suite of methods to fulfill these requirements and acquire atomic-resolution TEM images of several metal organic frameworks that are generally recognized as highly sensitive to electron beams. The high image resolution allows us to identify individual metal atomic columns, various types of surface termination, and benzene rings in the organic linkers. We also apply our methods to other electron beam–sensitive materials, including the organic-inorganic hybrid perovskite CH 3 NH 3 PbBr 3 .

Optical anisotropy is a fundamental building block for linear and nonlinear optical components such as polarizers, wave plates, and phase-matching elements1–4. In solid homogeneous materials, the strongest optical anisotropy is found in crystals such as calcite and rutile5,6. Attempts to enhance anisotropic light–matter interaction often rely on artificial anisotropic micro/nanostructures (form birefringence)7–11. Here, we demonstrate rationally designed, giant optical anisotropy in single crystals of barium titanium sulfide (BaTiS3). This material shows an unprecedented, broadband birefringence of up to 0.76 in the mid- to long-wave infrared, as well as a large dichroism window with absorption edges at 1.6 μm and 4.5 μm for light with polarization along two crystallographic axes on an easily accessible cleavage plane. The unusually large anisotropy is a result of the quasi-one-dimensional structure, combined with rational selection of the constituent ions to maximize the polarizability difference along different axes.

Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as ‘axes’ of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease. Health data are being generated and collected at an unprecedented scale, but whether big data will truly revolutionize healthcare is still a matter of much debate.

Obesity and metabolic diseases tend to go together, and humans who become obese are also prone to type 2 diabetes and cardiovascular problems. Starting with the observation that offspring of germ-free mice tended to become obese on high-fat diets, Kimura et al. investigated how the presence of the microbiota might be protective in mice (see the Perspective by Ferguson). Short-chain fatty acids (SCFAs) from the microbiota are known to suppress insulin signaling and reduce fat deposition in adipocytes. Further experiments showed that SCFAs in the bloodstream were able to pass from a non–germ-free mother's gut microbiota across the placenta and into the developing embryos. The authors found that in the embryos, the SCFA propionate mediates not only insulin levels through GPR43 signaling but also sympathetic nervous system development through GPR41 signaling. A high-fiber diet promoted propionate production from the maternal microbiota, and maternal antibiotic treatment resulted in obese-prone offspring. Science , this issue p. eaaw8429 ; see also p. 978 The mother’s gut microbiota during pregnancy tunes energy homeostasis and sympathetic nervous system development in offspring. Antibiotics and dietary habits can affect the gut microbial community, thus influencing disease susceptibility. Although the effect of microbiota on the postnatal environment has been well documented, much less is known regarding the impact of gut microbiota at the embryonic stage. Here we show that maternal microbiota shapes the metabolic system of offspring in mice. During pregnancy, short-chain fatty acids produced by the maternal microbiota dictate the differentiation of neural, intestinal, and pancreatic cells through embryonic GPR41 and GPR43. This developmental process helps maintain postnatal energy homeostasis, as evidenced by the fact that offspring from germ-free mothers are highly susceptible to metabolic syndrome, even when reared under conventional conditions. Thus, our findings elaborate on a link between the maternal gut environment and the developmental origin of metabolic syndrome.

Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation. Synopsis Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration. The inferred latent factors represent the underlying principal axes of heterogeneity across the samples. Factors can be shared by multiple data modalities or can be data‐type specific. The model flexibly handles missing values and different data types. In an application to Chronic Lymphocytic Leukaemia, MOFA discovers a low dimensional space spanned by known clinical markers and underappreciated axes of variation such as oxidative stress. In an application to multi‐omics profiles from single‐cells, MOFA recovers differentiation trajectories and identifies coordinated variation between the transcriptome and the epigenome. Graphical Abstract Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.

The inferotemporal (IT) cortex is responsible for object recognition, but it is unclear how the representation of visual objects is organized in this part of the brain. Areas that are selective for categories such as faces, bodies, and scenes have been found 1 – 5 , but large parts of IT cortex lack any known specialization, raising the question of what general principle governs IT organization. Here we used functional MRI, microstimulation, electrophysiology, and deep networks to investigate the organization of macaque IT cortex. We built a low-dimensional object space to describe general objects using a feedforward deep neural network trained on object classification 6 . Responses of IT cells to a large set of objects revealed that single IT cells project incoming objects onto specific axes of this space. Anatomically, cells were clustered into four networks according to the first two components of their preferred axes, forming a map of object space. This map was repeated across three hierarchical stages of increasing view invariance, and cells that comprised these maps collectively harboured sufficient coding capacity to approximately reconstruct objects. These results provide a unified picture of IT organization in which category-selective regions are part of a coarse map of object space whose dimensions can be extracted from a deep network. Primate inferotemporal cortex contains a coarse map of object space consisting of four networks, identified using functional imaging, electrophysiology and deep networks.

By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars’ surface the SEIS ( S eismic E xperiment for I nternal S tructure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking’s Mars seismic monitoring by a factor of ∼ 2500 at 1 Hz and ∼ 200 000 at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars’ surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of M w ∼ 3 at 40 ∘ epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution.

This paper provides a state-of-the-art report on the up-to-date research on the emerging 3D concrete printing technology from the concrete materials perspective. It reviews the recent research focused on understanding and characterizing the rheological necessities of the concrete printing process and discusses how the researchers are tailoring compatible mix proportions for the 3D concrete printing process by using eco-friendly binders, waste aggregates, chemical admixtures, and nano-additives. This paper systematically evaluates anisotropic behavior in the mechanical properties of printed concrete and establishes an order for anisotropic behavior in the compressive, flexural, and tensile strengths along three different axes (X, Y, and Z axes) of printed concrete. It evaluates the ratio of flexural strength to the compressive strength of printed concrete along the above three axes. This article explains the influence of variation of printing process parameters on the mechanical properties and discusses reinforcement approaches used for increasing structural performance. The microstructure at the interface of adjacent layers and also at the interface of the reinforcement-cement matrix is discussed. The recent research on the durability performance of printed concrete is critically discussed and future research needs for 3D concrete printing are identified in this paper.

Transport coefficients of correlated electron systems are often useful for mapping hidden phases with distinct symmetries. Here we report a transport signature of spontaneous symmetry breaking in the magnetic Weyl semimetal cerium-aluminum-germanium (CeAlGe) system in the form of singular angular magnetoresistance (SAMR). This angular response exceeding 1000% per radian is confined along the high-symmetry axes with a full width at half maximum reaching less than 1° and is tunable via isoelectronic partial substitution of silicon for germanium. The SAMR phenomena is explained theoretically as a consequence of controllable high-resistance domain walls, arising from the breaking of magnetic point group symmetry strongly coupled to a nearly nodal electronic structure. This study indicates ingredients for engineering magnetic materials with high angular sensitivity by lattice and site symmetries.