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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Proteomics reveals pathways linked to septoria canker resistance and susceptibility in Populus trichocarpa

A major threat to forest ecosystems and plantation forestry is the introduction of a non-native pathogen. Among non-domesticated populations with relatively high levels of genetic diversity, a measurable range of susceptibility to resistance can be expected. Identifying genetic determinants of resistant and susceptible individuals can inform the development of new strategies to engineer disease resistance. Here we describe pathogen-induced changes in the proteome of Populus trichocarpa stem tissue in response to Sphaerulia musiva (Septoria canker). This hemibiotrophic fungal pathogen causes stem canker disease in susceptible poplar genotypes. Proteomics analyses were performed on stem tissue harvested across 0-, 12-, 24- and 48-h post-inoculation with Septoria from three genotypes including one resistant (BESC-22) and two susceptible [BESC-801; Nisqually-1 (NQ-1)]. In total, 11,897 Populus proteins at FDR <0.01 were identified across all time points and genotypes. Analysis of protein abundances between genotypes revealed that the resistant poplar genotype (BESC-22) mounts a rapid and sustained defense response involving pattern recognition receptors, calcium signaling proteins, SAR inducers, transcriptional regulators, resistance proteins, and proteins involved with the hypersensitive response. One susceptible genotype (BESC-801) had a downregulated and delayed defense response whereas the second susceptible genotype (NQ-1) lacked a distinct pattern. Overall, the proteome-wide and protein-specific trends suggest that responses to the Septoria canker infection are genotype-specific for the naïve host, Populus trichocarpa .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

An Artificial Retina Processor for Track Reconstruction at the LHC Crossing Rate

The goal of the INFN-RETINA R&D project is to develop and implement a computational methodology that allows to reconstruct events with a large number (> 100) of charged-particle tracks in pixel and silicon strip detectors at 40 MHz, thus matching the requirements for processing LHC events at the full bunch-crossing frequency. Our approach relies on a parallel pattern-recognition algorithm, dubbed artificial retina, inspired by the early stages of image processing by the brain. In order to demonstrate that a track-processing system based on this algorithm is feasible, we built a sizable prototype of a tracking processor tuned to 3 000 patterns, based on already existing readout boards equipped with Altera Stratix III FPGAs. The detailed geometry and charged-particle activity of a large tracking detector currently in operation are used to assess its performances. We report on the test results with such a prototype.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

A Visual Atlas on Strategic Trade

Strategic trade concerns global trade flows of items with strategic military value. Items include goods with exclusive military use, but also dual-use items that can have both military and civil uses. Many countries require licenses for trade in strategic items as a measure of non-proliferation. For example, items listed for trade control by multilateral export control arrangements, such as the Nuclear Suppliers Group (NSG), typically are considered strategic goods. Analysts and officials involved in strategic trade control often lack quantitative global understanding of relevant trade patterns that would help them focus control and enforcement efforts, select companies for outreach and audit, target transactions for verification and inspection, and assess the potential economic impact of trade control policies. Many are unaware of available open source data on international trade that can inform these issues. Many do not know how to make use of such data or lack the tools necessary to do so. A book of charts dubbed a ‘Strategic Trade Atlas’ was developed to illustrate global trade flows of strategic goods and raise awareness of the availability and utility of open source trade data. The Atlas was prepared for the World Customs Organization (WCO) and its Member States to support their planning and execution of Strategic Trade Control Enforcement (STCE) operations. It offers macroscopic graphical representations of global trade flows classified under Harmonized System (HS) codes associated by the WCO with commodities described in its STCE Implementation Guide. While these HS codes are understood to also encompass many non-strategic items, they nevertheless provide a good overview of potentially strategic trade flows. These include HS codes covering nuclear and nuclear-related commodities listed by the NSG, such as uranium, heavy water, graphite, carbon fiber, nickel powder, beryllium, aluminum tubes, vacuum pumps, valves, machine tools, cameras, and many others. The Atlas uses trade data from the Base pour l’Analyse du Commerce International (BACI), a statistical elaboration of public UN Comtrade data. BACI data reconciles trade flows, taking into account asymmetries in import and export trade valuation. The data visualization approach facilitates rapid sense making, pattern recognition, and anomaly detection. Country-based profiles summarize vast amounts of data into readily understandable graphical representations of each country’s primary strategic imports and exports and their main trading partners. Commodity-based profiles provide a global view of major exporters, importers, and trade patterns for each commodity. These insights can inform the risk management approach employed by customs to allocate enforcement and compliance efforts.

customs controls↗
DOE OSTI2022

Artificial Intelligence for imaging Cherenkov detectors at the EIC

Abstract Imaging Cherenkov detectors form the backbone of particle identification (PID) at the future Electron Ion Collider (EIC). Currently all the designs for the first EIC detector proposal use a dual Ring Imaging CHerenkov (dRICH) detector in the hadron endcap, a Detector for Internally Reflected Cherenkov (DIRC) light in the barrel, and a modular RICH (mRICH) in the electron endcap. These detectors involve optical processes with many photons that need to be tracked through complex surfaces at the simulation level, while for reconstruction they rely on pattern recognition of ring images. This proceeding summarizes ongoing efforts and possible applications of AI for imaging Cherenkov detectors at EIC. In particular we will provide the example of the dRICH for the AI-assisted design and of the DIRC for simulation and particle identification from complex patterns and discuss possible advantages of using AI.

Instruments & Instrumentation↗
DOE OSTI2026

Optimal invariant sets for atomistic machine learning

The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques

We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Single Molecule and Nanoparticle Imaging in Biophysical, Surface, and Photocatalysis Studies

A differential interference contrast (DIC) polarization anisotropy is reported that was successfully used for rotational tracking of gold nanorods attached onto a kinesin-driven microtubule. A dual-wavelength detection of single gold nanorods rotating on a live cell membrane is described. Both transverse and longitudinal surface plasmon resonance (SPR) modes were used for tracking the rotational motions during a fast dynamic process under a DIC microscope. A novel method is presented to determine the full three-dimensional (3D) orientation of single plasmonic gold nanorods rotating on live cell membranes by combining DIC polarization anisotropy with an image pattern recognition technique. Polarization- and wavelength-sensitive DIC microscopy imaging of 2- m long gold nanowires as optical probes in biological studies is reported. A new method is demonstrated to track 3D orientation of single gold nanorods supported on a gold film without angular degeneracy. The idea is to use the interaction (or coupling) of gold nanorods with gold film, yielding characteristic scattering patterns such as a doughnut shape. Imaging of photocatalytic activity, polarity and selectivity on single Au-CdS hybrid nanocatalysts using a high-resolution superlocalization fluorescence imaging technique is described.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges

Characterization of a parallel application’s communication patterns can be useful for performance analysis, debugging, and system design. However, obtaining and interpreting a characterization can be difficult. AChax implements an approach that uses search and a library of known communication patterns to automatically characterize communication patterns. Our approach has some limitations that reduce its effectiveness for the patterns and pattern combinations used by some real-world applications. By viewing AChax’s pattern recognition problem as an image recognition problem, it may be possible to use deep learning to address these limitations. In this position paper, we present our current ideas regarding the benefits and challenges of integrating deep learning into AChax and our conclusion that a hybrid approach combining deep learning classification, regression, and the existing AChax approach may be the best long-term solution to the problem of parameterizing recognized communication patterns.

Roth, Philip↗
DOE OSTI2010

Rhythmic Dynamics and Synchronization via Dimensionality Reduction: Application to Human Gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a highdimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankleknee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
DOE OSTI2020

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2010

NMR-based metabonomic analysis of the hepatotoxicity induced by combined exposure to PCBs and TCDD in rats

A metabonomic approach using {sup 1}H NMR spectroscopy was adopted to investigate the metabonomic pattern of rat urine after oral administration of environmental endocrine disruptors (EDs) polychlorinated biphenyls (PCBs) and 2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD) alone or in combination and to explore the possible hepatotoxic mechanisms of combined exposure to PCBs and TCDD. {sup 1}H NMR spectra of urines collected 24 h before and after exposure were analyzed via pattern recognition by using principal component analysis (PCA). Serum biochemistry and liver histopathology indicated significant hepatotoxicity in the rats of the combined group. The PCA scores plots of urinary {sup 1}H NMR data showed that all the treatment groups could be easily distinguished from the control group, so could the PCBs or TCDD group and the combined group. The loadings plots of the PCA revealed remarkable increases in the levels of lactate, glucose, taurine, creatine, and 2-hydroxy-isovaleric acid and reductions in the levels of 2-oxoglutarate, citrate, succinate, hippurate, and trimethylamine-N-oxide in rat urine after exposure. These changes were more striking in the combined group. The changed metabolites may be considered possible biomarker for the hepatotoxicity. The present study demonstrates that combined exposure to PCBs and TCDD induced significant hepatotoxicity in rats, and mitochondrial dysfunction and fatty acid metabolism perturbations might contribute to the hepatotoxicity. There was good conformity between changes in the urine metabonomic pattern and those in serum biochemistry and liver histopathology. These results showed that the NMR-based metabonomic approach may provide a promising technique for the evaluation of the combined toxicity of EDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Hepatic lipid profiling of deer mice fed ethanol using {sup 1}H and {sup 31}P NMR spectroscopy: A dose-dependent subchronic study

Chronic alcohol abuse is a 2nd major cause of liver disease resulting in significant morbidity and mortality. Alcoholic liver disease (ALD) is characterized by a wide spectrum of pathologies starting from fat accumulation (steatosis) in early reversible stage to inflammation with or without fibrosis and cirrhosis in later irreversible stages. Previously, we reported significant steatosis in the livers of hepatic alcohol dehydrogenase (ADH)-deficient (ADH{sup −}) vs. hepatic ADH-normal (ADH{sup +}) deer mice fed 4% ethanol daily for 2 months [Bhopale et al., 2006, Alcohol 39, 179–188]. However, ADH{sup −} deer mice fed 4% ethanol also showed a significant mortality. Therefore, a dose-dependent study was conducted to understand the mechanism and identify lipid(s) involved in the development of ethanol-induced fatty liver. ADH{sup −} and ADH{sup +} deer mice fed 1, 2 or 3.5% ethanol daily for 2 months and fatty infiltration in the livers were evaluated by histology and by measuring dry weights of extracted lipids. Lipid metabolomic changes in extracted lipids were determined by proton ({sup 1}H) and {sup 31}phosphorus ({sup 31}P) nuclear magnetic resonance (NMR) spectroscopy. The NMR data was analyzed by hierarchical clustering (HC) and principle component analysis (PCA) for pattern recognition. Extensive vacuolization by histology and significantly increased dry weights of total lipids found only in the livers of ADH{sup −} deer mice fed 3.5% ethanol vs. pair-fed controls suggest a dose-dependent formation of fatty liver in ADH{sup −} deer mouse model. Analysis of NMR data of ADH{sup −} deer mice fed 3.5% ethanol vs. pair-fed controls shows increases for total cholesterol, esterified cholesterol, fatty acid methyl esters (FAMEs), triacylglycerides and unsaturation, and decreases for free cholesterol, phospholipids and allylic and diallylic protons. Certain classes of neutral lipids (cholesterol esters, fatty acyl chain (-COCH{sub 2}-) and FAMEs) were also mildly increased in ADH{sup −} deer mice fed 1 or 2% ethanol. Only small increases were observed for allylic and diallylic protons, FAMEs and unsaturations in ADH{sup +} deer mice fed 3.5% ethanol vs. pair-fed controls. PCA of NMR data showed increased clustering by gradual separation of ethanol-fed ADH{sup −} deer mice groups from their respective pair-fed control groups and corresponding ethanol-fed ADH{sup +} deer mice groups. Our data indicate that dose of ethanol and hepatic ADH deficiency are two key factors involved in initiation and progression of alcoholic fatty liver disease. Further studies on characterization of individual lipid entities and associated metabolic pathways altered in our deer mouse model after different durations of ethanol feeding could be important to delineate mechanism(s) and identify potential biomarker candidate(s) of early stage ALD. -- Highlights: ► Dose-dependent ethanol-induced fatty liver was studied in deer mouse model. ► A NMR-based lipidomic approach with histology and dry lipid weights was used. ► We used principal component analysis (PCA) to analyze the NMR lipidomic data. ► Dose-dependent clustering patterns by PCA were compared among the groups.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations: CESM/CAM EVALUATION BY DECISION TREES

An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Determining Full Three-Dimensional Orientation of Single Anisotropic Nanoparticles by Differential Interference Contrast Microscopy

Keeping track: By combining differential interference contrast (DIC) image pattern recognition with DIC polarization anisotropy, the exact full three-dimensional angular information of individual tilted gold nanorods positioned in the focal plane of the objective lens can be readily determined. The angular rotational modes and kinetics of individual in-focus gold nanorods can thus be resolved dynamically.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Electroluminescence TPCs at the thermal diffusion limit

Here, the NEXT experiment aims at searching for the hypothetical neutrinoless double-beta decay from the ${}^{136}$Xe isotope using a high-purity xenon TPC. Efficient discrimination of the events through pattern recognition of the topology of primary ionisation tracks is a major requirement for the experiment. However, it is limited by the diffusion of electrons. It is known that the addition of a small fraction of a molecular gas to xenon reduces electron diffusion. On the other hand, the electroluminescence (EL) yield drops and the achievable energy resolution may be compromised. We have studied the effect of adding several molecular gases to xenon (CO${}_{2}$, CH${}_{4}$ and CF${}_{4}$) on the EL yield and energy resolution obtained in a small prototype of driftless gas proportional scintillation counter. We have compared our results on the scintillation characteristics (EL yield and energy resolution) with a microscopic simulation, obtaining the diffusion coefficients in those conditions as well. Accordingly, electron diffusion may be reduced from about 10 mm/$\sqrt{\mathrm{m}}$ for pure xenon down to 2.5 mm/$\sqrt{\mathrm{m}}$ using additive concentrations of about 0.05%, 0.2% and 0.02% for CO${}_{2}$, CH${}_{4}$ and CF${}_{4}$, respectively. Our results show that CF${}_{4}$ admixtures present the highest EL yield in those conditions, but very poor energy resolution as a result of huge fluctuations observed in the EL formation. CH${}_{4}$ presents the best energy resolution despite the EL yield being the lowest. The results obtained with xenon admixtures are extrapolated to the operational conditions of the NEXT-100 TPC. CO${}_{2}$ and CH${}_{4}$ show potential as molecular additives in a large xenon TPC, CH${}_{4}$ showing the best performance and stability to be used in the NEXT-100 TPC, with an extrapolated energy resolution of 0.4% at 2.45 MeV for concentrations below 0.4%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗