g-engineering in hybrid rotaxanes to create AB and AB2 electron spin systems: EPR spectroscopic studies of weak interactions between dissimilar electron spin qubits

Hybrid [2]rotaxanes and pseudorotaxanes are reported where the magnetic interaction between dissimilar spins is controlled to create AB and AB2 electron spin systems,allowing independent control of weakly interacting S =1=2 centers

Evidence for a disaggregation of the universe.

Combining the kinematical definitions of the two dimensionless parameters, the deceleration q(x) and the Hubble t 0 H(x), we get a differential equation (where x=t/t 0 is the age of the universe relative to its present value t 0). First integration gives the function H(x). The present values of the Hubble parameter H(1) [approximately t 0 H(1)≈1], and the deceleration parameter [approximately q(1)≈−0.5], determine the function H(x). A second integration gives the cosmological scale factor a(x). Differentiation of a(x) gives the speed of expansion of the universe. The evolution of the universe that results from our approach is: an initial extremely fast exponential expansion (inflation), followed by an almost linear expansion (first decelerated, and later accelerated). For the future, at approximately t≈3t 0 there is a final exponential expansion, a second inflation that produces a disaggregation of the universe to infinity. We find the necessary and sufficient conditions for this disaggregation to occur. The precise value of the final age is given only with one parameter: the present value of the deceleration parameter [q(1)≈−0.5]. This emerging picture of the history of the universe represents an important challenge, an opportunity for the immediate research on the Universe. These conclusions have been elaborated without the use of any particular cosmological model of the univers

Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation

<p>Abstract</p> <p>Background</p> <p>Evaluation of Word Sense Disambiguation (WSD) methods in the biomedical domain is difficult because the available resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We present a method that can be used to automatically develop a WSD test collection using the Unified Medical Language System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. We demonstrate the use of this method by developing such a data set, called MSH WSD.</p> <p>Methods</p> <p>In our method, the Metathesaurus is first screened to identify ambiguous terms whose possible senses consist of two or more MeSH headings. We then use each ambiguous term and its corresponding MeSH heading to extract MEDLINE citations where the term and only one of the MeSH headings co-occur. The term found in the MEDLINE citation is automatically assigned the UMLS CUI linked to the MeSH heading. Each instance has been assigned a UMLS Concept Unique Identifier (CUI). We compare the characteristics of the MSH WSD data set to the previously existing NLM WSD data set.</p> <p>Results</p> <p>The resulting MSH WSD data set consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203 ambiguous entities. For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from MEDLINE.</p> <p>We evaluated the reliability of the MSH WSD data set using existing knowledge-based methods and compared their performance to that of the results previously obtained by these algorithms on the pre-existing data set, NLM WSD. We show that the knowledge-based methods achieve different results but keep their relative performance except for the Journal Descriptor Indexing (JDI) method, whose performance is below the other methods.</p> <p>Conclusions</p> <p>The MSH WSD data set allows the evaluation of WSD algorithms in the biomedical domain. Compared to previously existing data sets, MSH WSD contains a larger number of biomedical terms/abbreviations and covers the largest set of UMLS Semantic Types. Furthermore, the MSH WSD data set has been generated automatically reusing already existing annotations and, therefore, can be regenerated from subsequent UMLS versions.</p

Evolutionary Events in a Mathematical Sciences Research Collaboration Network

This study examines long-term trends and shifting behavior in the collaboration network of mathematics literature, using a subset of data from Mathematical Reviews spanning 1985-2009. Rather than modeling the network cumulatively, this study traces the evolution of the "here and now" using fixed-duration sliding windows. The analysis uses a suite of common network diagnostics, including the distributions of degrees, distances, and clustering, to track network structure. Several random models that call these diagnostics as parameters help tease them apart as factors from the values of others. Some behaviors are consistent over the entire interval, but most diagnostics indicate that the network's structural evolution is dominated by occasional dramatic shifts in otherwise steady trends. These behaviors are not distributed evenly across the network; stark differences in evolution can be observed between two major subnetworks, loosely thought of as "pure" and "applied", which approximately partition the aggregate. The paper characterizes two major events along the mathematics network trajectory and discusses possible explanatory factors.Comment: 30 pages, 14 figures, 1 table; supporting information: 5 pages, 5 figures; published in Scientometric

Collocation analysis for UMLS knowledge-based word sense disambiguation

BACKGROUND: The effectiveness of knowledge-based word sense disambiguation (WSD) approaches depends in part on the information available in the reference knowledge resource. Off the shelf, these resources are not optimized for WSD and might lack terms to model the context properly. In addition, they might include noisy terms which contribute to false positives in the disambiguation results. METHODS: We analyzed some collocation types which could improve the performance of knowledge-based disambiguation methods. Collocations are obtained by extracting candidate collocations from MEDLINE and then assigning them to one of the senses of an ambiguous word. We performed this assignment either using semantic group profiles or a knowledge-based disambiguation method. In addition to collocations, we used second-order features from a previously implemented approach.Specifically, we measured the effect of these collocations in two knowledge-based WSD methods. The first method, AEC, uses the knowledge from the UMLS to collect examples from MEDLINE which are used to train a Naïve Bayes approach. The second method, MRD, builds a profile for each candidate sense based on the UMLS and compares the profile to the context of the ambiguous word.We have used two WSD test sets which contain disambiguation cases which are mapped to UMLS concepts. The first one, the NLM WSD set, was developed manually by several domain experts and contains words with high frequency occurrence in MEDLINE. The second one, the MSH WSD set, was developed automatically using the MeSH indexing in MEDLINE. It contains a larger set of words and covers a larger number of UMLS semantic types. RESULTS: The results indicate an improvement after the use of collocations, although the approaches have different performance depending on the data set. In the NLM WSD set, the improvement is larger for the MRD disambiguation method using second-order features. Assignment of collocations to a candidate sense based on UMLS semantic group profiles is more effective in the AEC method.In the MSH WSD set, the increment in performance is modest for all the methods. Collocations combined with the MRD disambiguation method have the best performance. The MRD disambiguation method and second-order features provide an insignificant change in performance. The AEC disambiguation method gives a modest improvement in performance. Assignment of collocations to a candidate sense based on knowledge-based methods has better performance. CONCLUSIONS: Collocations improve the performance of knowledge-based disambiguation methods, although results vary depending on the test set and method used. Generally, the AEC method is sensitive to query drift. Using AEC, just a few selected terms provide a large improvement in disambiguation performance. The MRD method handles noisy terms better but requires a larger set of terms to improve performance

Knowledge-based biomedical word sense disambiguation: comparison of approaches

<p>Abstract</p> <p>Background</p> <p>Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain.</p> <p>Methods</p> <p>We present research on existing WSD approaches based on knowledge bases, which complement the studies performed on statistical learning. We compare four approaches which rely on the UMLS Metathesaurus as the source of knowledge. The first approach compares the overlap of the context of the ambiguous word to the candidate senses based on a representation built out of the definitions, synonyms and related terms. The second approach collects training data for each of the candidate senses to perform WSD based on queries built using monosemous synonyms and related terms. These queries are used to retrieve MEDLINE citations. Then, a machine learning approach is trained on this corpus. The third approach is a graph-based method which exploits the structure of the Metathesaurus network of relations to perform unsupervised WSD. This approach ranks nodes in the graph according to their relative structural importance. The last approach uses the semantic types assigned to the concepts in the Metathesaurus to perform WSD. The context of the ambiguous word and semantic types of the candidate concepts are mapped to Journal Descriptors. These mappings are compared to decide among the candidate concepts. Results are provided estimating accuracy of the different methods on the WSD test collection available from the NLM.</p> <p>Conclusions</p> <p>We have found that the last approach achieves better results compared to the other methods. The graph-based approach, using the structure of the Metathesaurus network to estimate the relevance of the Metathesaurus concepts, does not perform well compared to the first two methods. In addition, the combination of methods improves the performance over the individual approaches. On the other hand, the performance is still below statistical learning trained on manually produced data and below the maximum frequency sense baseline. Finally, we propose several directions to improve the existing methods and to improve the Metathesaurus to be more effective in WSD.</p

Identification by Virtual Screening and In Vitro Testing of Human DOPA Decarboxylase Inhibitors

Dopa decarboxylase (DDC), a pyridoxal 5′-phosphate (PLP) enzyme responsible for the biosynthesis of dopamine and serotonin, is involved in Parkinson's disease (PD). PD is a neurodegenerative disease mainly due to a progressive loss of dopamine-producing cells in the midbrain. Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD. Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects. Therefore, the main goals of this study were (a) to use virtual screening to identify potential human DDC inhibitors and (b) to evaluate the reliability of our virtual-screening (VS) protocol by experimentally testing the “in vitro” activity of selected molecules. Starting from the crystal structure of the DDC-carbidopa complex, a new VS protocol, integrating pharmacophore searches and molecular docking, was developed. Analysis of 15 selected compounds, obtained by filtering the public ZINC database, yielded two molecules that bind to the active site of human DDC and behave as competitive inhibitors with Ki values ≥10 µM. By performing in silico similarity search on the latter compounds followed by a substructure search using the core of the most active compound we identified several competitive inhibitors of human DDC with Ki values in the low micromolar range, unable to bind free PLP, and predicted to not cross the blood-brain barrier. The most potent inhibitor with a Ki value of 500 nM represents a new lead compound, targeting human DDC, that may be the basis for lead optimization in the development of new DDC inhibitors. To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery

Adipokines: Linking metabolic syndrome, the immune system, and arthritic diseases

Metabolic syndrome (MetS) represents a cluster of metabolic and cardiovascular complications, including obesity and visceral adiposity, insulin resistance, dyslipidemia, hyperglycemia and hypertension, which directly increase the risk of cardiovascular diseases (CVD) and diabetes mellitus type 2 (DM2). Patients with arthritic diseases, such as rheumatoid arthritis and osteoarthritis, have a higher incidence of CVD. Although recent advances in the treatment of arthritic diseases, the incidence of CVD remains elevated, and MetS has been identified as a possible link between CVD and arthritic diseases. Chronic low-grade inflammation associated with obesity has been established as a significant contributing factor to the increased prevalence of MetS. Adipokines, which play important physiological roles in metabolic activities contributing to the pathogenesis of MetS, are also involved in the regulation of autoimmune and/or inflammatory processes associated with arthritic diseases. Therefore, MetS and dysregulated secretion of pro-inflammatory adipokines have been recognized as a molecular link between CVD and arthritis diseases. In the present paper, we review recent evidence supporting the role played by adipokines, in particular leptin, adiponectin, and lipocalin-2, in the modulation of the immune system, MetS and arthritic diseases. The underlying cellular and molecular mechanisms are discussed, as well as potential new therapeutic strategies.Acknowledgments: OG and FL are Staff Personnel of Xunta de Galicia (Servizo Galego de Saude, SERGAS) through a research-staff stabilization contract (ISCIII/SERGAS). VF is a “Sara Borrell” Researcher funded by ISCIII and FEDER (CD16/00111). RG is a “Miguel Servet” Researcher funded by Instituto de Salud Carlos III (ISCIII) and FEDER. CRF is a pre-doctoral research scholar funded by ISCIII and FEDER (Exp.18/00188). OG, MAGG, and RG are members of RETICS Programme, RD16/0012/0014 (RIER: Red de Investigación en Inflamación y Enfermedades Reumáticas) via Instituto de Salud Carlos III (ISCIII) and FEDER. FL is a member of CIBERCV (Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares). The work of OG and JP (PI17/00409), RG (PI16/01870 and CP15/00007) and FL (PI15/00681 PI18/00821 and CB16/11/00226) were funded by Instituto de Salud Carlos III and FEDER. OG is a beneficiary of a project funded by Research Executive Agency of the European Union in the framework of MSCA-RISE Action of the H2020 Programme (Project number 734899). RG is beneficiary of a project funded by Mutua Madrileña 2018. AM wishes to acknowledge financial support from the European Structural and Social Funds through the Research Council of Lithuania (Lietuvos Mokslo Taryba) according to the activity ‘Improvement of researchers’ qualification by implementing world-class R&D projects’ of Measure No. 09.3.3-LMT-K-712 (grant application code: 09.3.3-LMT-K-712-01-0157, agreement No. DOTSUT-215) and the new funding programme: Attracting Foreign Researchers for Research Implementation (2018–2022). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript

COVID-19 and RA share SPP1 myeloid pathway that drives PD-L1pos neutrophils and CD14pos monocytes

We explored the potential link between chronic inflammatory arthritis and COVID-19 pathogenic and resolving macrophage pathways and their role in COVID-19 pathogenesis. We found that BALF macrophage clusters FCN1pos and FCN1posSPP1pos predominant in severe COVID-19 were transcriptionally related to synovial tissue macrophage (STM) clusters CD48highS100A12pos and CD48posSPP1pos that drive Rheumatoid Arthritis (RA) synovitis. BALF macrophage cluster FABP4pos predominant in healthy lung was transcriptionally related to STM cluster TREM2pos that governs resolution of synovitis in RA remission. Plasma concentrations of SPP1 and S100A12 (key products of macrophage clusters shared with active RA) were high in severe COVID-19 and predicted the need for Intensive Care Unit transfer, and remained high in post-COVID-19 stage. High plasma levels of SPP1 were unique to severe COVID-19 when compared to other causes of severe pneumonia, and immunohistochemistry localized SPP1pos macrophages in the alveoli of COVID-19 lung. Investigation into SPP1 mechanisms of action revealed that it drives pro-inflammatory activation of CD14pos monocytes and development of PD-L1pos neutrophils, both hallmarks of severe COVID-19. In summary, COVID-19 pneumonitis appears driven by similar pathogenic myeloid cell pathways as those in RA, and their mediators such as SPP1 might be an upstream activator of the aberrant innate response in severe COVID-19 and predictive of disease trajectory including post-COVID-19 monitoring