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Abstract Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Abstract Background Automated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models have the potential to outperform multi-class classification approaches. Methods We systematically characterize the factors that contribute to the accuracy and efficiency of several approaches to sequence-to-sequence machine learning through extensive studies of alternative methods and hyperparameter selections. We not only identify the best-performing systems and parameters across a wide variety of ontologies but also provide insights into the widely varying resource requirements and hyperparameter robustness of alternative approaches. Analysis of the strengths and weaknesses of such systems suggest promising avenues for future improvements as well as design choices that can increase computational efficiency with small costs in performance. Results Bidirectional encoder representations from transformers for biomedical text mining (BioBERT) for span detection along with the open-source toolkit for neural machine translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies annotated in the CRAFT Corpus. This approach uses substantially fewer computational resources, including hardware, memory, and time than several alternative approaches. Conclusions Machine translation is a promising avenue for fully machine-learning-based concept recognition that achieves state-of-the-art results on the CRAFT Corpus, evaluated via a direct comparison to previous results from the 2019 CRAFT shared task. Experiments illuminating the reasons for the surprisingly good performance of sequence-to-sequence methods targeting ontology identifiers suggest that further progress may be possible by mapping to alternative target concept representations. All code and models can be found at: https://github.com/UCDenver-ccp/Concept-Recognition-as-Translation .

Abstract Background Coreference resolution is the task of finding strings in text that have the same referent as other strings. Failures of coreference resolution are a common cause of false negatives in information extraction from the scientific literature. In order to better understand the nature of the phenomenon of coreference in biomedical publications and to increase performance on the task, we annotated the Colorado Richly Annotated Full Text (CRAFT) corpus with coreference relations. Results The corpus was manually annotated with coreference relations, including identity and appositives for all coreferring base noun phrases. The OntoNotes annotation guidelines, with minor adaptations, were used. Interannotator agreement ranges from 0.480 (entity-based CEAF) to 0.858 (Class-B3), depending on the metric that is used to assess it. The resulting corpus adds nearly 30,000 annotations to the previous release of the CRAFT corpus. Differences from related projects include a much broader definition of markables, connection to extensive annotation of several domain-relevant semantic classes, and connection to complete syntactic annotation. Tool performance was benchmarked on the data. A publicly available out-of-the-box, general-domain coreference resolution system achieved an F-measure of 0.14 (B3), while a simple domain-adapted rule-based system achieved an F-measure of 0.42. An ensemble of the two reached F of 0.46. Following the IDENTITY chains in the data would add 106,263 additional named entities in the full 97-paper corpus, for an increase of 76% percent in the semantic classes of the eight ontologies that have been annotated in earlier versions of the CRAFT corpus. Conclusions The project produced a large data set for further investigation of coreference and coreference resolution in the scientific literature. The work raised issues in the phenomenon of reference in this domain and genre, and the paper proposes that many mentions that would be considered generic in the general domain are not generic in the biomedical domain due to their referents to specific classes in domain-specific ontologies. The comparison of the performance of a publicly available and well-understood coreference resolution system with a domain-adapted system produced results that are consistent with the notion that the requirements for successful coreference resolution in this genre are quite different from those of the general domain, and also suggest that the baseline performance difference is quite large.

The global push towards a hydrogen economy fuels hydrogen production from various sources. A crucial step in enriching hydrogen and reducing CO in syngas derived from carbon-based hydrogen production is the water-gas shift reaction (WGSR). Given the equilibrium-limited nature of WGSR, low temperatures are necessary to reduce carbon monoxide concentrations to the desired level. Traditionally, iron‑chromium (Fe/Cr) and copper‑zinc (Cu/Zn) catalysts have been widely used at high and low temperatures, respectively. Numerous studies have focused on developing optimal WGS catalysts with the desired characteristics and efficiency. This review extensively discusses various catalysts for different stages of WGSR, including low, medium, high-temperature, and sour WGS catalysts. However, understanding the contrast between the redox and associative mechanisms and the nature of intermediates in the WGS pathway remains unclear. A detailed study of the WGSR pathway is imperative to develop highly active and stable catalysts. Various experimental kinetic values and models have also been reported to elucidate the WGSR mechanism at different temperatures. The primary deactivation sources of WGS catalysts have been discussed to highlight recent advances to improve catalyst performance. The contribution of computational methods such as Density Functional Theory (DFT) to developing WGS catalysts is also explored. Furthermore, the review addresses the challenges encountered in the WGSR, and recommendations and conclusions are drawn to guide future research efforts.

Abstract Density functional theory is the workhorse of materials simulations. Unfortunately, the quality of results often varies depending on the specific choice of the exchange-correlation functional, which significantly limits the predictive power of this approach. Coupled cluster theory, including single, double, and perturbative triple particle-hole excitation operators, is widely considered the ‘gold standard' of quantum chemistry as it can achieve chemical accuracy for non-strongly correlated applications. Because of the high computational cost, the application of coupled cluster theory in materials simulations is rare, and this is particularly true if finite-temperature properties are of interest for which molecular dynamics simulations have to be performed. By combining recent progress in machine learning models with low data requirements for energy surfaces and in the implementation of coupled cluster theory for periodic materials, we show that chemically accurate simulations of materials are practical and could soon become significantly widespread. As an example of this numerical approach, we consider the calculation of the enthalpy of adsorption of CO2 in a porous material.

The work deals with the removal of two dyes, namely methylene blue (MB) and methyl orange (MO), from polluted water by adsorption onto CuO nanoparticles synthesized with a green synthesis procedure, starting from plant resources. Adsorption isotherms are determined at different temperatures aiming at investigating the adsorption mechanisms of the two dyes. The experimental results indicate that, for both MB and MO, the adsorption capacity increases with increasing temperature, with slight differences in the case of MO. Comparatively, the CuO nanoparticles show a higher MB adsorption capacity with respect to MO.A modelling analysis is carried out with a multilayer model derived from statistical physics, selected among a group of models, each hypothesizing a different number of adsorbed molecules layers. The analysis of model parameters allows determining that the adsorbate molecules exhibit a non-parallel orientation on the surface of biosynthesized CuO nanoparticles and each functional group of the adsorbent binds multiple molecules, simultaneously.The model also allows determining the number of dye molecule layers formed on adsorbent surface, in all the cases resulting higher than three, also confirming the effect of temperature on the maximum adsorption capacity.Specifically, the total number of dye layers formed on biosynthesized CuO nanoparticles surface exhibited a range of 4.17–4.55 for MB dye and of 3.01–3.51 for MO dye.Finally, the adsorption energies reveal that adsorption likely involves physical forces (all resulting all below 22 kJ/mol), i.e. hydrogen bonding and van der Waals forces. The adsorption energies for the interactions between dye molecules are lower than those calculated for the interactions between the dye molecules and the adsorbent surface.

Abstract Silanols are key players in the application performance of zeolites, yet, their localization and hydrogen bonding strength need more studies. The effects of post-synthetic ion exchange on nanosized chabazite (CHA), focusing on the formation of silanols, were studied. The significant alteration of the silanols of the chabazite nanozeolite upon ion exchange and their effect on the CO2 adsorption capacity was revealed by solid-state nuclear magnetic resonance (NMR), Fourier-transform infrared (FTIR) spectroscopy, and periodic density functional theory (DFT) calculations. Both theoretical and experimental results revealed changing the ratio of extra-framework cations in CHA zeolites changes the population of silanols; decreasing the Cs+/K+ ratio creates more silanols. Upon adsorption of CO2, the distribution and strength of the silanols also changed with increased hydrogen bonding, thus revealing an interaction of silanols with CO2 molecules. To the best of our knowledge, this is the first evidence of the interplay between alkali-metal cations and silanols in nanosized CHA.

In this study, a purified diatomite (PD) with a concentration of diatom frustules more than 92% SiO2 was utilized to synthesize a composite of MCM-41 silica under hydrothermal conditions. The as-synthesized PD/MCM-41 composite was characterized and tested as an adsorbent for the removal of Cr(VI) and Mn(VII) ions from aqueous solution. Results of X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Fourier-transform infrared spectroscopy (FTIR) revealed that the diatom frustules of the PD were coated with MCM-41 mesoporous silica. Experimental isotherms of Cr(VI) and Mn(VII) adsorption were fitted to classical and advanced statistical physics models at 25°C–55°C and pH 3. The Langmuir model estimated monolayer adsorption capacities ranging from 144.1 to 162.2 mg/g for Cr(VI) and 166.2 to 177.0 mg/g for Mn(VII), which improved with increasing the solution temperature. Steric and energetic parameters obtained from a monolayer adsorption model with one adsorption site was utilized to explain the adsorption mechanism at a microscopic level. The number of Cr(VI) and Mn(VII) ions adsorbed on PD/MCM-41 active site (n) were 1.25–1.27 for Cr(VI) and 1.27–1.32 for Mn(VII), thus suggesting multi-interaction mechanisms. The density of PD/MCM-41 active sites (DM) was a key parameter to explain the adsorption of these heavy metals. The adsorbed quantities were maximum at 55°C, thus obtaining 102.8 and 110.7 mg/g for Cr(VI) and Mn(VII), respectively. Cr(VI) and Mn(VII) adsorption energies ranged from 18.48 to 26.70 kJ/mol and corresponded to an endothermic adsorption with physical forces. Entropy, free enthalpy, and internal energy associated to the adsorption of Cr(VI) and Mn(VII) ions were calculated, thus indicating that the removal of these pollutants was spontaneous. Overall, this article offers new interpretations for the Cr(VI) and Mn(VII) adsorption mechanisms on PD/MCM-41 composite, which are relevant to contribute to the development of effective water treatment processes.

A multiscale investigation including computational chemistry calculations and experimental studies was performed to elucidate and understand the methylene blue (MB) adsorption on polyaniline (PANI) from an aqueous solution. Static DFT and DFT-based ab initio molecular dynamics were used to characterize the intermolecular interactions of this dye molecule with nondoped and doped PANI. Experimental adsorption studies at different operating conditions were performed to complement the mechanism analysis of this adsorption system. Infrared spectroscopy studies and ab initio calculations showed the important role of π-π stacking and van der Waals interactions for the dye adsorption on PANI. Experimental results of MB adsorption on the PANI surface indicated that alkaline conditions were more favorable than acidic conditions where the MB adsorption capacity ranged from 9.91 mg/g at pH 1.8 to 23.16 mg/g at pH 10.9. Equilibrium adsorption studies with nondoped PANI revealed a fast removal of the dye molecules where the equilibrium adsorption was reached after 45 minutes. The kinetic parameters were calculated with the pseudo-second and pseudo-first order models, while the adsorption mechanism was analyzed using the intraparticle diffusion, Boyd, and Elovich models. Dye adsorption equilibrium was studied at pH 8 and 30 °C where Temkin, Freundlich, Langmuir, and Dubinin-Radushkevich (D-R) isotherm models as well as a statistical physics monolayer model were employed in data analysis. The saturation dye adsorption capacity was 40.2 mg/g where an inclined adsorption orientation of dye molecules on the PANI surface could be expected with an adsorption energy of 14.0 kJ/mol. This interaction energy clearly indicated that only physical interactions were involved in the MB dye adsorption mechanism, which was also confirmed by the calculations with the D-R isotherm model. These theoretical and experimental results are important to understand the dye adsorption properties of conductive polymers and to consolidate their application in the synthesis of new adsorbents and composites for water treatment.

Separation of itaconic acid from aqueous solution has been explored using various carbon-based adsorbents obtained from the pyrolysis and KOH activation of coconut shell biomass. The best preparation conditions to obtain a tailored adsorbent for itaconic acid purification were identified via a Taguchi experimental design, where its adsorption properties were maximized. The best activated carbon was obtained via coconut shell pyrolysis at 750 °C for 4 h plus an activation with 0.1 KOH and a final treatment at 800 °C for 2 h. This adsorbent showed an adsorption capacity of 4.31 mmol/g at 20 °C and pH 3 with a surface area of 466 m2/g. Itaconic acid separation was exothermic and pH-dependent where electrostatic forces and hydrogen bonding were the main adsorption interactions. Calculated adsorption rate constants for itaconic acid adsorption were 0.44–1.20 h-1. Results of adsorbent characterization analysis indicated the presence of a crystallization of itaconic acid molecules onto the activated carbon surface where 3–4 molecules could interact to form the clusters. This organic acid was recovered from the adsorbent surface via desorption with water or ethanol, thus facilitating its final purification. The best activated carbon obtained in this study is a promising alternative to perform sustainable and energy-efficient downstream separation and purification of itaconic acid produced via fermentation.

The analysis of thermodynamics and mechanism of the adsorption of cadmium, chromium, copper, and lead ions from aqueous solution with two keratin-based biomaterials, namely, human hair and sheep fur, is reported in this paper. The effect of initial ion concentration, temperature, pH, contact time, and biomaterial amount on the removal of these heavy metal ions using these keratinous adsorbents was studied. The adsorption of heavy metal ions was highly dependent on the operating parameters where pH and temperature showed the highest impact. Maximum adsorption capacities of these biomaterials were up to 1.33 and 1.40 mmol/g for chromium ions using human hair and sheep fur, respectively. Adsorption kinetic rates of tested heavy metal ions were calculated via a pseudo-second-order model, and they ranged from 0.054 to 0.261 g/mmol·min. A detailed thermodynamic analysis of lead ion adsorption was performed showing an endothermic removal of this adsorbate with both human hair and sheep fur with adsorption enthalpies of 84.5 and 97.1 kJ/mol, respectively. Statistical physics calculations demonstrated that this heavy metal ion was adsorbed via a multi-interaction mechanism especially for human hair. These keratinous biomaterials showed competitive adsorption capacities especially for chromium ion removal and can outperform commercial activated carbons and other adsorbents reported in literature.

Abstract Hierarchical zeolites are regarded as promising catalysts due to their well‐developed porosity, increased accessible surface area, and minimal diffusion constraints. Thus far, the focus has been on the creation of mesopores in zeolites, however, little is known about a microporosity upgrading and its effect on the diffusion and catalytic performance. Here the authors show that the “birth” of mesopore formation in faujasite (FAU) type zeolite starts by removing framework T atoms from the sodalite (SOD) cages followed by propagation throughout the crystals. This is evidenced by following the diffusion of xenon (Xe) in the mesoporous FAU zeolite prepared by unbiased leaching with NH4F in comparison to the pristine FAU zeolite. A new diffusion pathway for the Xe in the mesoporous zeolite is proposed. Xenon first penetrates through the opened SOD cages and then diffuses to supercages of the mesoporous zeolite. Density functional theory (DFT) calculations indicate that Xe diffusion between SOD cage and supercage occurs only in hierarchical FAU structure with defect‐contained six‐member‐ring separating these two types of cages. The catalytic performance of the mesoporous FAU zeolite further indicates that the upgraded microporosity facilitates the intracrystalline molecular traffic and increases the catalytic performance.

There have been several attempts to design innovative biomaterials as surface coatings to enhance the biological performance of biomedical implants. The objective of this study was to design multifunctional Cu/a-C:H thin coating depositing on the Ti-6Al-4V alloy (TC4) via magnetron sputtering in the presence of Ar and CH4 for applications in bone implants. Moreover, the impact of Cu amount and sp2/sp3 ratio on the interior stress, corrosion behavior, mechanical properties, and tribological performance and biocompatibility of the resulting biomaterial was discussed. X-ray photoelectron spectroscopy (XPS) revealed that the sp2/sp3 portion of the coating was enhanced for samples having higher Cu contents. The intensity of the interior stress of the Cu/a-C:H thin bio-films decreased by increase of Cu content as well as the sp2/sp3 ratio. By contrast, the values of Young's modulus, the H3/E2 ratio, and hardness exhibited no significant difference with enhancing Cu content and sp2/sp3 ratio. However, there was an optimum Cu content (36.8 wt.%) and sp2/sp3 ratio (4.7) that it is feasible to get Cu/a-C:H coating with higher hardness and tribological properties. Electrochemical impedance spectroscopy test results depicted significant improvement of Ti-6Al-4V alloy corrosion resistance by deposition of Cu/a-C:H thin coating at an optimum Ar/CH4 ratio. Furthermore, Cu/a-C:H thin coating with higher Cu contents showed better antibacterial properties and higher angiogenesis and osteogenesis activities. The coated samples inhibited the growth of bacteria as compared to the uncoated sample (p < 0.05). In addition, such coating composition can stimulate angiogenesis, osteogenesis and control host response, thereby increasing the success rate of implants. Moreover, Cu/a-C:H thin films encouraged development of blood vessels on the surface of titanium alloy when the density of grown blood vessels was increased with enhancing the Cu amount of the films. It is speculated that such coating can be a promising candidate for enhancing the osseointegration features.

Recently, tungsten has drawn worldwide attention considering its high supply risk and economic importance in the modern society. Skarns represent one of the most important types of tungsten deposits in terms of reserves. They contain fine-grained scheelite (CaWO4) associated with complex gangue minerals, i.e., minerals that display similar properties, particularly surface properties, compared to scheelite. Consistently, the froth flotation of scheelite still remains, in the twenty first century, a strong scientific, industrial, and technical challenge. Various reagents suitable for scheelite flotation (collectors and depressants, mostly) are reviewed in the present work, with a strong focus on the separation of scheelite from calcium salts, namely, fluorite, apatite, and calcite, which generally represent significant amounts in tungsten skarns. Albeit some reagents allow increasing significantly the selectivity regarding a mineral, most reagents fail in providing a good global selectivity in favor of scheelite. Overall, the greenest, most efficient, and cheapest method for scheelite flotation is to use fatty acids as collectors with sodium silicate as depressant, although this solution suffers from a crucial lack of selectivity regarding the above-mentioned calcium salts. Therefore, the use of reagent combinations, commonly displaying synergistic effects, is highly recommended to achieve a selective flotation of scheelite from the calcium salts as well as from calcium silicates.

In this study, density functional theory (DFT) calculations have been performed to investigate the adsorption mechanisms of toluene and water onto various cationic forms of Y zeolite (LiY, NaY, KY, CsY, CuY and AgY). Our computational investigation revealed that toluene is mainly adsorbed via π–interactions on alkalis exchanged Y zeolites, where the adsorbed toluene moiety interacts with a single cation for all cases with the exception of CsY, where two cations can simultaneously contribute to the adsorption of the toluene, hence leading to the highest interaction observed among the series. Furthermore, we find that the interaction energies of toluene increase while moving down in the alkaline series where interaction energies are 87.8, 105.5, 97.8, and 114.4 kJ/mol for LiY, NaY, KY and CsY, respectively. For zeolites based on transition metals (CuY and AgY), our calculations reveal a different adsorption mode where only one cation interacts with toluene through two carbon atoms of the aromatic ring with interaction energies of 147.0 and 131.5 kJ/mol for CuY and AgY, respectively. More importantly, we show that water presents no inhibitory effect on the adsorption of toluene, where interaction energies of this latter were 10 kJ/mol (LiY) to 47 kJ/mol (CsY) higher than those of water. Our results point out that LiY would be less efficient for the toluene/water separation while CuY, AgY and CsY would be the ideal candidates for this application.

Although dibenzyl disulfide (DBDS) is used as a mineral oil stabilizer, its presence in electrical transformer oil is associated as one of the major causes of copper corrosion and subsequent formation of copper sulfide. In order to prevent these undesirable processes, MY zeolites (with M = Li, Na, K, Cs, Cu or Ag) are proposed to adsorb molecularly DBDS. In this study, different MY zeolites are investigated at the DFT+D level in order to assess their ability in DBDS adsorption. It was found that CsY, AgY and CuY exhibit the best compromise between high interaction energies and limited S-S bond activation, thus emerging as optimal adsorbents for DBDS.

Motivation: Existing projects that focus on the semiautomatic addition of links between existing terms in the Open Biomedical Ontologies can take advantage of reasoners that can make new inferences between terms that are based on the added formal definitions and that reflect nonalignments between the linked terms. However, these projects require that these definitions be necessary and sufficient, a strong requirement that often does not hold. If such definitions cannot be added, the reasoners cannot point to the nonalignments through the suggestion of new inferences. Results: We describe a methodology by which we have identified over 1900 instances of nonredundant nonalignments between terms from the Gene Ontology (GO) biological process (BP), cellular component (CC) and molecular function (MF) ontologies, Chemical Entities of Biological Interest (ChEBI) and the Cell Type Ontology (CL). Many of the 39.8% of these nonalignments whose object terms are more atomic than the subject terms are not currently examined in other ontology-enrichment projects due to the fact that the necessary and sufficient conditions required for the inferences are not currently examined. Analysis of the ratios of nonalignments to assertions from which the nonalignments were identified suggests that BP–MF, BP–BP, BP–CL and CC–CC terms are relatively well-aligned, while ChEBI–MF, BP–ChEBI and CC–MF terms are relatively not aligned well. We propose four ways to resolve an identified nonalignment and recommend an analogous implementation of our methodology in ontology-enrichment tools to identify types of nonalignments that are currently not detected. Availability: The nonalignments discussed in this article may be viewed at http://compbio.uchsc.edu/Hunter_lab/Bada/nonalignments_2008_03_06.html. Code for the generation of these nonalignments is available upon request. Contact: mike.bada@uchsc.edu [ABSTRACT FROM AUTHOR]