Physical Gradients Research Articles

Viral niche-partitioning: Comparative genomics of giant viruses across environmental gradients in a High Arctic freshwater-saltwater lake

Abstract Giant viruses (GVs; Nucleocytoviricota) impact the biology and ecology of a wide range of eukaryotic hosts, with implications for global biogeochemical cycles. Here, we investigated GV niche separation in highly stratified Lake A at the northern coast of Ellesmere Island, Nunavut, Canada. This lake is composed of a layer of ice-covered freshwater that overlies saltwater derived from the ancient Arctic Ocean, and it therefore provides a broad gradient of environmental conditions and ecological habitats, each with a distinct protist community and rich assemblages of associated GVs. The upper layer (mixolimnion) had measurable light and oxygen, and contained diverse GVs linked to photosynthetic protists, indicating adaptation to surface biotic and abiotic conditions. In contrast, the saline lower layer (monimolimnion), lacking oxygen and light, hosted GVs associated with predicted heterotrophic protists, some of which are known for a predatory lifestyle, and with several viral genes suggesting adaptation to deep-water anaerobic conditions. Our observations underscore the coupling between physical and chemical gradients, microeukaryotes and their associated GVs in Lake A, and provide insight into the potential for GVs to directly and indirectly impact host metabolism. There were similarities between the genetic composition of GVs and the metabolic processes of their potential hosts, implying co-evolution and niche-adaptation within the lake habitats. Notably, we found a greater presence of viral rhodopsins in deeper water layers, suggesting an evolutionary relationship with potential hosts capable of supplementing their energetic needs to thrive in low energy, anoxic conditions.

Latitudinal gradients and ocean fronts strongly influence protist communities in the southern Pacific Ocean.

Protist communities in the southern Pacific Ocean make a major contribution to global biogeochemical cycling, but remain understudied due to their remote location. We therefore have limited understanding of how large-scale physical gradients (e.g. temperature) and mesoscale oceanographic features (e.g. fronts) influence microeukaryote diversity in this region. We performed a high-resolution examination of protist communities along a latitudinal transect (>3000km) at 150°W in the central southern Pacific Ocean that encompassed major frontal regions, including the sub-tropical front (STF), the sub-Antarctic front (SAF), and the polar front (PF). We identified distinct microbial communities along the transect that were primarily delineated by the positions of the STF and PF. Some taxa were not constricted by these environmental boundaries and were able to span frontal regions, such as the colonial haptophyte Phaeocystis. Our findings also support the presence of a Latitudinal Diversity Gradient (LDG) of decreasing diversity of the protist community with increasing latitude, although some individual taxa, notably the diatoms, do not adhere to this rule. Our findings show that oceanographic features and large-scale physical gradients have important impacts on marine protist communities in the southern Pacific Ocean that are likely to strongly influence their response to future environmental change.

Physical mechanism-based simulation methods for soil and ground heat flux from observations at multi-sites

It is important to obtain the soil heat flux at a specific depth (Gz) and at the ground’s surface (G0) to understand the surface energy balance and climate change. However, previous evaluations of Gz and G0 simulations based on multiple sites have been relatively insufficient. We evaluated the accuracy of using physical mechanism-based gradient and calorimetry methods to simulate Gz and changes in heat storage (ΔS) based on observations collected at 28 sites. Three methods for simulating G0 were also evaluated using triple collocation (TC) and extended triple collocation (ETC) metrics. The ability of the gradient method to simulate Gz was strong, particularly for the lower soil layer, with a median Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), and index of agreement for the evaluated sites of 0.76, 0.86, and 0.91, respectively. The accuracy of the calorimetry method for simulating ΔS was generally lower than that of the gradient method for determining Gz. Overall, the combination of gradient analysis and calorimetry showed the best performance in simulating G0. The TC evaluation showed that the combination of gradient and calorimetry had a strong ability to simulate G0 in most land cover types (root mean square error (RMSE) <4 W/m2) but this ability was lost in the snow cover type (RMSE > 10 W/m2). When evaluated by ETC, the three methods had strong effects only on alpine meadow, floodland, and shrub types (CC > 0.70). Appropriate evaluation indices were recommended based on the application of the model.

Utilizing porous dielectric material to enhance the performance of capacitive MEMS accelerometers for shaft monitoring

This research investigates the optimization of the performance of a capacitive-based microelectromechanical systems (MEMS) shaft monitoring accelerometer using a porous soft dielectric material with high polarization capability and low Young’s modulus. The nonlinear governing equations of a microbeam with a proof mass coupled with the squeeze motion equation of the polymeric dielectric material excited by shaft vibrations are extracted and solved in both the spatial and temporal domains. The studied model considers the stiffness, dissipative, and inertial effects of the polymeric material and incorporates porosity as a displacement-dependent variable, which introduces an additional nonlinearity. After discretizing the coupled nonlinear differential equations using the Galerkin method, the response in the frequency domain is obtained by applying an energy balance method, where the coefficients of the Fourier expansion of the response are found by arc-length continuation through a physical gradient descent learning-based approach method. The occurrence of secondary and primary resonances in different harmonic responses is studied for different harmonic acceleration inputs. The equivalent rigidity of the design is investigated for different shaft frequency magnitudes and different proof mass geometries. The effect of the initial porosity volume fraction of the elastomeric dielectric material on the dynamic response of the accelerometer is also examined. The sensor’s sensitivity is dependent on its geometry and properties and can vary based on the structure’s material parameters. Consequently, the selection of different geometries and materials may result in either an improvement or a deterioration of the sensor’s performance.

Ecological patterns of benthic foraminiferal communities driven by seasonal and spatial environmental gradients in an Arctic fjord

AbstractArctic fjords, being transitional areas between glacier‐covered land and the ocean, are characterized by strong environmental gradients. The seasonal melting of glaciers generates strong turbidity and primary production antagonist gradients, which can affect benthic habitats. Two sampling campaigns were carried out in Kongsfjorden (Svalbard, Arctic Ocean) in May and August 2021 to investigate seasonal changes in benthic foraminifera spatial distribution and ecosystem functioning along a longitudinal transect of 10 km from the Kronebreen tidewater glacier front. Concurrently, organic matter quantity and biochemical composition, sediment grain size, and physical parameters of the water masses were investigated as possible driving factors of benthic ecosystem responses. In a previous study, three statistically determined foraminiferal biozonations (glacier proximal, medial, and distal) were observed on the basis of their species content within the closest 10 km from the glacier front, presenting similar assemblages in both seasons. Our results indicate that foraminiferal distribution at the local scale is mainly driven by physical and geochemical gradients induced by melting waters and sediment discharges from the tidewater glacier occurring during summer. Due to the climate change, the melting season is expected to last longer and increasing global temperature will much probably accelerate the melting processes. Our findings strongly support the use of foraminifera as bioindicators to monitor the effects of ongoing climate change on the benthic ecosystems of Arctic fjords and, accessorily, as proxies for reconstructing glacier front positions in the recent past.

Patterns and drivers in the functional diversity decomposition of invaded stream fish communities

AbstractAimThe assembly of real‐world ecological communities in human‐modified landscapes is influenced by a complex interplay of spatial, temporal, environmental and invasion gradients. However, understanding the relative importance of these drivers and their interactions in shaping functional assembly remains elusive. Our study aimed to investigate the relative influence of these drivers on the functional assembly of a stream fish metacommunity.LocationStreams of the Lake Balaton catchment, Hungary.MethodsWe analysed a long‐term (18‐year) dataset of the stream fish metacommunity, focusing on changes in functional diversity (Q), redundancy (R) and species dominance (D). Ternary diagrams were utilized to decompose functional diversity into Q, R and D components and to visualize diversity patterns. Linear mixed‐effect regression and separate structural equation models were employed to identify significant drivers of Q, R and D.ResultsNative fish communities exhibited low functional diversity (Q) but high redundancy (R) and dominance (D), indicating functional convergence and dominance. Stream habitat size, network position and associated spatial, physical and chemical gradients emerged as consistently significant drivers of D and R. Changes in Q were additionally linked to non‐native community properties and subtle shifts in land use and within‐stream habitat characteristics.Main ConclusionsOur findings suggest that both environmental filtering and interspecies interactions, particularly trait similarity between invaders and natives shape functional assembly of stream fish metacommunities. Despite minimal temporal directional changes, environmental drivers predominantly influence long‐term diversity patterns of native fish communities, overshadowing invasion effects. Our findings underscore the importance of considering both environmental filtering mechanisms and interspecies interactions in understanding functional assembly. Additionally, the joint application of diversity decomposition frameworks with predictive modelling provides comprehensive insight into patterns of functional diversity and assembly across ecological communities.

Solid-state polymer-particle hybrid electrolytes: Structure and electrochemical properties.

Solid-state electrolytes (SSEs) are challenged by complex interfacial chemistry and poor ion transport through the interfaces they form with battery electrodes. Here, we investigate a class of SSE composed of micrometer-sized lithium oxide (Li2O) particles dispersed in a polymerizable 1,3-dioxolane (DOL) liquid. Ring-opening polymerization (ROP) of the DOL by Lewis acid salts inside a battery cell produces polymer-inorganic hybrid electrolytes with gradient properties on both the particle and battery cell length scales. These electrolytes sustain stable charge-discharge behavior in Li||NCM811 and anode-free Cu||NCM811 electrochemical cells. On the particle length scale, Li2O retards ROP, facilitating efficient ion transport in a fluid-like region near the particle surface. On battery cell length scales, gravity-assisted settling creates physical and electrochemical gradients in the hybrid electrolytes. By means of electrochemical and spectroscopic analyses, we find that Li2O particles participate in a reversible redox reaction that increases the effective CE in anode-free cells to values approaching 100%, enhancing battery cycle life.

Physical structure of the environment contributes to the development of diversity of microalgal assemblages

Aquatic macrophytes form a three dimensional complex structure in the littoral zones of lakes, with many physical, chemical and biological gradients and interactions. This special habitat harbours a unique microalgal assemblage called metaphyton, that differs both from the phytoplankton of the pelagial and from the benthic assemblages whose elements are tightly attached to the substrates. Since metaphytic assemblages significantly contribute to the diversity of lakes’ phytoplankton, it is crucial to understand and disentangle those mechanisms that ensure their development. Therefore, we focused on the question of how a single solid physical structure contribute to maintaining metaphytic assemblages. Using a laboratory experiment we studied the floristic and functional differences of microalgal assemblages in microcosms that simulated the conditions that an open water, a complex natural macrophyte stand (Utricularia vulgaris L.), or an artificial substrate (cotton wool) provide for them. We inoculated the systems with a species rich (> 326 species) microalgal assemblage collected from a eutrophic oxbow lake, and studied the diversity, trait and functional group composition of the assemblages in a 24 day long experimental period. We found that both natural and artificial substrates ensured higher species richness than the open water environment. Functional richness in the open water environment was lower than in the aquaria containing natural macrophyte stand but higher than in which cotton wool was placed. This means that the artificial physical structure enhanced functional redundancy of the resident functional groups. Elongation measures of microalgal assemblages showed the highest variation in the microcosms that simulated the open water environment. Our results suggest that assembly of metaphytic algal communities is not a random process, instead a deterministic one driven by the niche characteristics of the complex three dimensional structure created by the stands of aquatic macrophytes.

Variation in metabolic rate during low tide aerial exposure in the Asian shore crab Hemigrapsus sanguineus

Intertidal animals deal with physical gradients daily that create stressful conditions across the shore. These physical gradients influence the physiological performance of organisms, requiring responses that may differ with height on the shore. We examined the respiratory response to aerial exposure in the invasive Asian shore crab Hemigrapsus sanguineus during periods of low tide emersion using two field experiments. The first experiment simultaneously measured respiration of individuals collected from different heights on the shore, which had therefore been emersed for different lengths of time. The second experiment measured respiration of individuals collected at different times from the same tidal height. Respiration rates of crabs in both experiments increased immediately after emersion, nearly doubling by and peaking at ~ 1.5 h of aerial exposure, before decreasing again over the next 1.5 h. These results suggest that the energetic cost of low tide exposure is greatest shortly after emersion during the first half of the typical low tide period, but then decreases thereafter. These respiration patterns facilitate the broad intertidal distribution of this species on rocky shores throughout its range.

Thermal-hydro coupling model of methane hydrate reformation in porous media

Methane hydrates are crystalline compounds found in marine sediments and permafrost regions. Methane hydrates remain stable under both low-temperature and high-pressure conditions. When a methane hydrate reservoir is heated or depressurized, methane hydrates become unstable and decompose into water and methane gases. The heat absorption process during the decomposition of methane hydrates influences the temperature field. Methane hydrate reformation occurs during the extraction process, significantly reducing the hydraulic conductivity of the reservoir and hindering the long-term stable extraction of methane hydrates. In this paper, a numerical model is established by coupling seepage and heat processes. The temperature variation owing to the heat absorption process of methane hydrate decomposition is quantified based on the proposed model. The effect of spatial lithology on methane hydrate conversion is also analyzed, and the results for hydrate, water, gas, and permeability in the model are summarized. The numerical model also reflects the heterogeneity of marine sediments. Finally, the sensitivities of different physical parameters (permeability and porosity) and pressure gradients to the reformation rate of methane hydrate reforming are discussed. The results of this study provide scientific data supporting the influence of secondary hydrate formation on long-term extraction efficiency in the actual engineering hydrate extraction process.

Different profiles of soil phosphorous compounds depending on tree species and availability of soil phosphorus in a tropical rainforest in French Guiana

BackgroundThe availability of soil phosphorus (P) often limits the productivities of wet tropical lowland forests. Little is known, however, about the metabolomic profile of different chemical P compounds with potentially different uses and about the cycling of P and their variability across space under different tree species in highly diverse tropical rainforests.ResultsWe hypothesised that the different strategies of the competing tree species to retranslocate, mineralise, mobilise, and take up P from the soil would promote distinct soil 31P profiles. We tested this hypothesis by performing a metabolomic analysis of the soils in two rainforests in French Guiana using 31P nuclear magnetic resonance (NMR). We analysed 31P NMR chemical shifts in soil solutions of model P compounds, including inorganic phosphates, orthophosphate mono- and diesters, phosphonates, and organic polyphosphates. The identity of the tree species (growing above the soil samples) explained > 53% of the total variance of the 31P NMR metabolomic profiles of the soils, suggesting species-specific ecological niches and/or species-specific interactions with the soil microbiome and soil trophic web structure and functionality determining the use and production of P compounds. Differences at regional and topographic levels also explained some part of the the total variance of the 31P NMR profiles, although less than the influence of the tree species. Multivariate analyses of soil 31P NMR metabolomics data indicated higher soil concentrations of P biomolecules involved in the active use of P (nucleic acids and molecules involved with energy and anabolism) in soils with lower concentrations of total soil P and higher concentrations of P-storing biomolecules in soils with higher concentrations of total P.ConclusionsThe results strongly suggest “niches” of soil P profiles associated with physical gradients, mostly topographic position, and with the specific distribution of species along this gradient, which is associated with species-specific strategies of soil P mineralisation, mobilisation, use, and uptake.

A Combined Model of Diffusion Model and Enhanced Residual Network for Super-Resolution Reconstruction of Turbulent Flows

In this study, we introduce a novel model, the Combined Model, composed of a conditional denoising diffusion model (SR3) and an enhanced residual network (EResNet), for reconstructing high-resolution turbulent flow fields from low-resolution flow data. The SR3 model is adept at learning the distribution of flow fields. The EResNet architecture incorporates a long skip connection extending from the input directly to the output. This modification ensures the preservation of essential features learned by the SR3, while simultaneously enhancing the accuracy of the flow field. Additionally, we incorporated physical gradient constraints into the loss function of EResNet to ensure that the flow fields reconstructed by the Combined Model are consistent with the direct numerical simulation (DNS) data. Consequently, the high-resolution flow fields reconstructed by the Combined Model exhibit high conformity with the DNS results in terms of flow distribution, details, and accuracy. To validate the effectiveness of the model, experiments were conducted on two-dimensional flow around a square cylinder at a Reynolds number (Re) of 100 and turbulent channel flow at Re = 4000. The results demonstrate that the Combined Model can reconstruct both high-resolution laminar and turbulent flow fields from low-resolution data. Comparisons with a super-resolution convolutional neural network (SRCNN) and an enhanced super-resolution generative adversarial network (ESRGAN) demonstrate that while all three models perform admirably in reconstructing laminar flows, the Combined Model excels in capturing more details in turbulent flows, aligning the statistical outcomes more closely with the DNS results. Furthermore, in terms of L2 norm error, the Combined Model achieves an order of magnitude lower error compared to SRCNN and ESRGAN. Experimentation also revealed that SR3 possesses the capability to learn the distribution of flow fields. This work opens new avenues for high-fidelity flow field reconstruction using deep learning methods.

Local physical gradient-based coupling information interpolate method and application on double-wall turbine blade multidisciplinary analysis

A novel generalization interpolate modification method based on local gradient is presented. The main idea was to remove the sample points located in the gradient direction away from the unknown points compared to the sample points located in the perpendicular direction. This method first uses the Taylor expansion to construct a set of equations and solves the local physical gradient. Subsequently, a scale vector is derived to modify the interpolate space. Three classical interpolation methods were modified: Inverse Distance Weighted (IDW), Radial Basis Function (RBF), and Kriging method. The results of the test functions prove that the proposed method can effectively decrease the interpolate error regardless of low or high dimensional interpolate problem and irrespective of the sample point size. Surface and volume interpolate validations demonstrate that the local gradient generates a large interpolate error, and the modified method can essentially diminish this error with a maximum reduction of 9.94 %. Subsequently, this method is applied to implement a multidisciplinary analysis of a practical double-wall cooling blade with a large physical gradient. The results showed that the pressure and temperature interpolate errors could be decreased by 5.18 % and 14.5 %, respectively, with only an additional 115 s, at the fillet and film hole area where the local gradient was high. When the strength analysis was implemented, the maximum stress error reduction was 25.04 MPa at the stress concentration region.

Time to anoxia: Observations and predictions of oxygen drawdown following coastal flood events

The coastal terrestrial-aquatic interface (TAI) is a highly dynamic system characterized by strong physical, chemical, and biological gradients. In particular, shifting soil redox conditions and consumption of terminal electron acceptors, due in part to dynamic hydrologic conditions, is a strong driver of carbon availability and transformations across TAIs. However, while redox dynamics are well described, our ability to quantitatively forecast rates ofoxic to anoxic shifts in soils with different characteristics and inundation regimes is limited. We integrated field measurements, laboratory incubations, and model simulations to improve mechanistic understanding of oxygen consumption dynamics in coastal soils. Continuous in situ monitoring unexpectedly revealed that flooding caused temporary spikes in subsurface dissolved oxygen followed by rapid consumption in the wetlands. To further investigate these mechanisms in a controlled setting, we performed laboratory incubations using surface and subsurface soils from a TAI gradient (defined here as upland forest to transitional forest to wetland) in Western Lake Erie to measure oxygen consumption rates in TAI soils during flood events. In our experiments, wetland soils reached anoxia the fastest, in ∼ 9 h on average, whereas upland soils turned anoxic in ∼ 18 h. Subsurface upland soils did not turn anoxic even after two weeks of saturation in the lab, and their oxygen consumption patterns suggested carbon and/or nutrient limitation. These results are consistent with in-situ groundwater redox and oxygen measurements in the field, where wetland soils exhibited the highest rates of oxygen consumption along the TAI. Model simulations of oxygen consumption suggested that oxygen consumption had stronger abiotic controls in wetland soils but stronger biotic controls in upland soils, providing a useful framework for future incubation experiments. Microbial activity is a strong driver of oxygen consumption in TAI soils, although it is constrained by the availability of dissolved carbon in subsurface soils.

Fabrication of gradient hydrogels using a thermophoretic approach in microfluidics

The extracellular matrix presents spatially varying physical cues that can influence cell behavior in many processes. Physical gradients within hydrogels that mimic the heterogenous mechanical microenvironment are useful to study the impact of these cues on cellular responses. Therefore, simple and reliable techniques to create such gradient hydrogels are highly desirable. This work demonstrates the fabrication of stiffness gradient Gellan gum (GG) hydrogels by applying a temperature gradient across a microchannel containing hydrogel precursor solution. Thermophoretic migration of components within the precursor solution generates a concentration gradient that mirrors the temperature gradient profile, which translates into mechanical gradients upon crosslinking. Using this technique, GG hydrogels with stiffness gradients ranging from 20 to 90 kPa over 600 µm are created, covering the elastic moduli typical of moderately hard to hard tissues. MC3T3 osteoblast cells are then cultured on these gradient substrates, which exhibit preferential migration and enhanced osteogenic potential toward the stiffest region on the gradient. Overall, the thermophoretic approach provides a non-toxic and effective method to create hydrogels with defined mechanical gradients at the micron scale suitable for in vitro biological studies and potentially tissue engineering applications.

Why coastal lagoons are so productive? Physical bases of fishing productivity in coastal lagoons

Coastal lagoons are among the most productive marine ecosystems in the world. Annual primary production varies from 50 to > 500 g C m−2 year−1, being of the same order of magnitude as that of the upwelling areas. Many lagoons lie within the range of eutrophic (300–500 g C m−2 year−1) or hypereutrophic (> 500 g C m−2 year−1) conditions. The high productivity of coastal lagoons makes them subject of exploitation by many marine fishes and invertebrates, that use them as nursery areas and feeding grounds during their early life cycle phases, and most lagoons support important fisheries or maintain aquaculture exploitations. The high levels of their biological production can be explained by some of their common features as shallowness and the strong influence of terrestrial systems. Shallowness favors that the photic zone extends to the lagoon bottom and that wind can promote the resuspension of nutrients and organisms. The interaction with land also introduces significant amounts of nutrients. However, trophic variables can explain < 43 % of the fishing yields, and further than the trophic status of the lagoons, several works showed that the biological productivity of coastal lagoons can be explained by their geomorphological features such as the positive influence of shoreline development and the negative influence of depth. Using the Mar Menor lagoon as a case study, we propose that although nutrient inputs and light can be limiting factors for photosynthetic based productivity, increasing fishing yield up to a certain limit, the productivity of lagoons is mainly promoted by more general forces associated to physical and chemical gradients.

CO2 Storage Monitoring via Time-Lapse Full Waveform Inversion with Automatic Differentiation.

In the field of CO2 capture utilization and storage (CCUS), recent advancements in active-source monitoring have significantly enhanced the capabilities of time-lapse acoustical imaging, facilitating continuous capture of detailed physical parameter images from acoustic signals. Central to these advancements is time-lapse full waveform inversion (TLFWI), which is increasingly recognized for its ability to extract high-resolution images from active-source datasets. However, conventional TLFWI methodologies, which are reliant on gradient optimization, face a significant challenge due to the need for complex, explicit formulation of the physical model gradient relative to the misfit function between observed and predicted data over time. Addressing this limitation, our study introduces automatic differentiation (AD) into the TLFWI process, utilizing deep learning frameworks such as PyTorch to automate gradient calculation using the chain rule. This novel approach, AD-TLFWI, not only streamlines the inversion of time-lapse images for CO2 monitoring but also tackles the issue of local minima commonly encountered in deep learning optimizers. The effectiveness of AD-TLFWI was validated using a realistic model from the Frio-II CO2 injection site, where it successfully produced high-resolution images that demonstrate significant changes in velocity due to CO2 injection. This advancement in TLFWI methodology, underpinned by the integration of AD, represents a pivotal development in active-source monitoring systems, enhancing information extraction capabilities and providing potential solutions to complex multiphysics monitoring challenges.

Influence of Density Ratios on Richtmyer–Meshkov Instability with Non-Equilibrium Effects in the Reshock Process

The Richtmyer–Meshkov instability in a two-component system during the reshock process for various density ratios is studied through the discrete Boltzmann method. Detailed investigations are conducted on both hydrodynamic and thermodynamic non-equilibrium behaviors. Specifically, the analysis focuses on the density gradient, viscous stress tensor, heat flux strength, thermodynamic non-equilibrium intensity, and thermodynamic non-equilibrium area. It is interesting to observe the complex variations to non-equilibrium quantities with the changing shock front, rarefaction wave, transverse wave, and material interface. Physically, the non-equilibrium area is extended as the perturbed material interface grows after the passing of the shock wave or secondary impact. Moreover, the global non-equilibrium manifestation decreases when the transmitted shock front and transverse waves leave or when the reflected rarefaction wave weakens. Additionally, the global thermodynamic non-equilibrium effect is enhanced as the physical gradients or non-equilibrium area increase. Finally, the local non-equilibrium effect decreases when the fluid structure gradually disappears under the action of dissipation/diffusion.

Bacterial biogeography of the Indian Ocean.

Historically, our understanding of bacterial ecology in the Indian Ocean has been limited to regional studies that place emphasis on community structure and function within oxygen minimum zones. Thus, bacterial community dynamics across the wider Indian Ocean are largely undescribed. As part of Bio-GO-SHIP, we sequenced the 16S rRNA gene from 465 samples collected on sections I07N and I09N. We found that (i) there were 23 distinct bioregions within the Indian Ocean, (ii) the southeastern gyre had the largest gradient in bacterial alpha-diversity, (iii) the Indian Ocean surface microbiome was primarily composed of a core set of taxa, and (iv) bioregions were characterized by transitions in physical and geochemical conditions. Overall, we showed that bacterial community structure spatially delineated the surface Indian Ocean and that these microbially-defined regions were reflective of subtle ocean physical and geochemical gradients. Therefore, incorporating metrics of in-situ microbial communities into marine ecological regions traditionally defined by remote sensing will improve our ability to delineate warm, oligotrophic regions.

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders * *Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide

Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.