Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method

Publisher Copyright: © 2023 The Author(s). Published by the Royal Society of Chemistry.Gas-phase oxygenated organic molecules (OOMs) can contribute significantly to both atmospheric new particle growth and secondary organic aerosol formation. Precursor apportionment of atmospheric OOMs connects them with volatile organic compounds (VOCs). Since atmospheric OOMs are often highly functionalized products of multistep reactions, it is challenging to reveal the complete mapping relationships between OOMs and their precursors. In this study, we demonstrate that the machine learning method is useful in attributing atmospheric OOMs to their precursors using several chemical indicators, such as O/C ratio and H/C ratio. The model is trained and tested using data acquired in controlled laboratory experiments, covering the oxidation products of four main types of VOCs (isoprene, monoterpenes, aliphatics, and aromatics). Then, the model is used for analyzing atmospheric OOMs measured in both urban Beijing and a boreal forest environment in southern Finland. The results suggest that atmospheric OOMs in these two environments can be reasonably assigned to their precursors. Beijing is an anthropogenic VOC dominated environment with ~64% aromatic and aliphatic OOMs, and the other boreal forested area has ~76% monoterpene OOMs. This pilot study shows that machine learning can be a promising tool in atmospheric chemistry for connecting the dots.Peer reviewe

Comparing research investment to United Kingdom institutions and published outputs for tuberculosis, HIV and malaria: A systematic analysis across 1997-2013

Background: The "Unfinished Agenda" of infectious diseases is of great importance to policymakers and research funding agencies that require ongoing research evidence on their effective management. Journal publications help effectively share and disseminate research results to inform policy and practice. We assess research investments to United Kingdom institutions in HIV, tuberculosis and malaria, and analyse these by numbers of publications and citations and by disease and type of science. Methods: Information on infection-related research investments awarded to United Kingdom institutions across 1997-2010 were sourced from funding agencies and individually categorised by disease and type of science. Publications were sourced from the Scopus database via keyword searches and filtered to include only publications relating to human disease and containing a United Kingdom-based first and/or last author. Data were matched by disease and type of science categories. Investment (United Kingdom pounds) and publications were compared to generate an 'investment per publication' metric; similarly, an 'investment per citation' metric was also developed as a measure of the usefulness of research. Results: Total research investment for all three diseases was £1.4 billion, and was greatest for HIV (£651.4 million), followed by malaria (£518.7 million) and tuberculosis (£239.1 million). There were 17,271 included publications, with 9,322 for HIV, 4,451 for malaria, and 3,498 for tuberculosis. HIV publications received the most citations (254,949), followed by malaria (148,559) and tuberculosis (100,244). According to UK pound per publication, tuberculosis (£50,691) appeared the most productive for investment, compared to HIV (£61,971) and malaria (£94,483). By type of science, public health research was most productive for HIV (£27,296) and tuberculosis (£22,273), while phase I-III trials were most productive for malaria (£60,491). According to UK pound per citation, tuberculosis (£1,797) was the most productive area for investment, compared to HIV (£2,265) and malaria (£2,834). Public health research was the most productive type of science for HIV (£2,265) and tuberculosis (£1,797), whereas phase I-III trials were most productive for malaria (£1,713). Conclusions: When comparing total publications and citations with research investment to United Kingdom institutions, tuberculosis research appears to perform best in terms of efficiency. There were more public health-related publications and citations for HIV and tuberculosis than other types of science. These findings demonstrate the diversity of research funding and outputs, and provide new evidence to inform research investment strategies for policymakers, funders, academic institutions, and healthcare organizations.Infectious Disease Research Networ

Prediction and Verification of the Major Ingredients and Molecular Targets of Tripterygii Radix Against Rheumatoid Arthritis

Tripterygii Radix exhibits good clinical efficacy and safety in rheumatoid arthritis (RA) patients, but its effective components and mechanism of action are still unclear. The purpose of this study was to explore and verify the major ingredients and molecular targets of Tripterygii Radix in RA using drug-compounds-biotargets-diseases network and protein-protein interaction (PPI) network analyses. The processes and pathways were derived from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The most important compounds and biotargets were determined based on the degree values. RA fibroblast-like synoviocytes (RA-FLS) were separated from RA patients and identified by hematoxylin and eosin (HE) staining and immunohistochemistry. The purity of RA-FLS was acquired by flow cytometry marked with CD90 or VCAM-1. RA-FLS were subjected to control, dimethyl sulfoxide (control), kaempferol, or lenalidomide treatment. Cell migration was evaluated by the transwell assay. The relative expression of biotarget proteins and cytokines was analyzed by western blotting and flow cytometry. In total, 144 chemical components were identified from Tripterygii Radix; kaempferol was the most active ingredient among 33 other components. Fourteen proteins were found to be affected in RA from 285 common biotargets. The tumor necrosis factor (TNF) signaling pathway was predicted to be one of the most latent treatment pathways. Migration of RA-FLS was inhibited and the expression of protein kinase B (AKT1), JUN, caspase 3 (CASP3), TNF receptor 1 and 2 (TNFR1 and TNFR2), interleukin-6 (IL-6), and TNF-α was significantly affected by kaempferol. Thus, this study confirmed kaempferol as the effective component of Tripterygii Radix against RA-FLS and TNF signaling pathway and its involvement in the regulation of AKT1, JUN, CASP3, TNFR1, TNFR2, IL-6, and TNF-α expression

Influence of Traffic Activity on Heavy Metal Concentrations of Roadside Farmland Soil in Mountainous Areas

Emission of heavy metals from traffic activities is an important pollution source to roadside farmland ecosystems. However, little previous research has been conducted to investigate heavy metal concentrations of roadside farmland soil in mountainous areas. Owing to more complex roadside environments and more intense driving conditions on mountainous highways, heavy metal accumulation and distribution patterns in farmland soil due to traffic activity could be different from those on plain highways. In this study, design factors including altitude, roadside distance, terrain, and tree protection were considered to analyze their influences on Cu, Zn, Cd, and Pb concentrations in farmland soils along a mountain highway around Kathmandu, Nepal. On average, the concentrations of Cu, Zn, Cd, and Pb at the sampling sites are lower than the tolerable levels. Correspondingly, pollution index analysis does not show serious roadside pollution owing to traffic emissions either. However, some maximum Zn, Cd, and Pb concentrations are close to or higher than the tolerable level, indicating that although average accumulations of heavy metals pose no hazard in the region, some spots with peak concentrations may be severely polluted. The correlation analysis indicates that either Cu or Cd content is found to be significantly correlated with Zn and Pb content while there is no significant correlation between Cu and Cd. The pattern can be reasonably explained by the vehicular heavy metal emission mechanisms, which proves the heavy metals’ homology of the traffic pollution source. Furthermore, the independent factors show complex interaction effects on heavy metal concentrations in the mountainous roadside soil, which indicate quite a different distribution pattern from previous studies focusing on urban roadside environments. It is found that the Pb concentration in the downgrade roadside soil is significantly lower than that in the upgrade soil while the Zn concentration in the downgrade roadside soil is marginally higher than in the upgrade soil; and the concentrations of Cu and Pb in the roadside soils with tree protection are significantly lower than those without tree protection. However, the attenuation pattern of heavy metal concentrations as a function of roadside distance within a 100 m range cannot be identified consistently

Prostaglandin E2 receptor 3 signaling is induced in placentas with unexplained recurrent pregnancy losses

Although an inflammatory microenvironment is required for successful implantation, an inflammatory overreaction is one of the causes of unexplained recurrent pregnancy losses (uRPL). Prostaglandin E-2 (PGE(2)) plays a pivotal role in regulating immune balance during early pregnancy, and it can stimulate inflammatory reactions via prostaglandin E-2 receptor 3 (EP3). However, the role of PGE, receptor signaling in the uRPL remains unknown. We aimed to investigate whether EP3 signaling is involved in the mechanism of uRPL. Via immunohistochemistry we could show that the expression of cyclooxygenase-2, EP3 and G protein alpha inhibitor 1 (G(i1)) was enhanced in the decidua of the uRPL group in comparison to the control group in first-trimester placentas. In vitro, we demonstrated that sulprostone (an EP1/EP3 agonist) inhibited the secretion of beta-hCG and progesterone in JEG-3 cells and the secretion of beta-hCG in HTR-8/SVneo cells while it induced the expression of plasminogen activator inhibitor type 1 in JEG-3 cells. In addition, PGE(2)/sulprostone was able to stimulate the expression of G o , phosphorylated-extracellular signal-regulated kinases 1/2 (p-ERK1/2) and p53. L-798,106 (an EP3-specific antagonist) suppressed the expression of EP3 and p-ERK1/2 without affecting the secretion of beta-hCG. Elevated activation of EP3 signaling in first-trimester placentas plays an important role in regulating the inflammatory microenvironment, the hormone secretion of extravillous trophoblasts and the remodeling of extracellular matrix in the fetal-maternal interface. L-798,106 might be a 'potential therapeutic candidate' for the treatment of uRPL

Age of onset correlates with clinical characteristics and prognostic outcomes in neuromyelitis optica spectrum disorder

ObjectiveNeuromyelitis optica spectrum disorder (NMOSD) is an inflammatory disease preferentially affects the optic nerve and the spinal cord. The first attack usually occurs in the third or fourth decade, though patients with disease onset in the fifties or later are not uncommon. This study aimed to investigate the clinical characteristics and prognosis in patients with different age of onset and to explore the correlations between age of onset and clinical characteristics and prognostic outcomes.MethodWe retrospectively reviewed the medical records of 298 NMOSD patients diagnosed according to the 2015 updated version of diagnostic criteria. Patients were divided into early-onset NMOSD (EO-NMOSD) (<50 years at disease onset) and late-onset NMOSD (LO-NMOSD) (≥50 years at disease onset) based on the age of disease onset. LO-NMOSD patients were divided into two subgroups: relative-late-onset NMOSD (RLO-NMOSD) (50~70 years at disease onset) and very-late-onset NMOSD (≥70 years at disease onset). Clinical characteristics, laboratory findings, neuroimaging features, and prognostic outcomes were investigated.ResultsCompared to EO-NMOSD patients, patients with LO-NMOSD showed more frequent transverse myelitis (TM) (58.20% vs. 36.00%, p = 0.007) while less frequent optic neuritis (ON) (23.10% vs. 34.80%, p = 0.031) and brainstem/cerebral attacks (7.50% vs. 18.30%, p = 0.006) as the first attack. Patients with LO-NMOSD showed less frequent relapses, higher Expanded Disability Status Scale (EDSS) score at the last follow-up, fewer NMOSD-typical brain lesions, and longer segments of spinal cord lesions. Patients with older onset age showed a higher proportion of increased protein levels in cerebrospinal fluid during the acute phase of attacks. Age at disease onset positively correlated with length of spinal cord lesions at first attack and at last follow-up, negatively correlated with ARR-1 (ARR excluding the first attack, calculated from disease onset to final follow-up), irrespective of AQP4-IgG serostatus. Patients with older age at disease onset progressed to severe motor disability sooner, and age of onset positively correlated with EDSS score at the last follow-up, irrespective of AQP4-IgG serostatus.ConclusionAge of disease onset affects clinical characteristics and prognosis outcomes of patients with NMOSD

Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems

The vegetation's response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of both the immediate and long-term plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e. droughts and heatwaves) and structural lags in the systems (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 12 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr-1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ-0.73, p<0.01 for NEE, ρ-0.67, p<0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual-site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems