Arbitrary Tables Research Articles

Ways to increase compliance in patients with acute otitis externa

Topicality: The problem of diagnosis and treatment of otitis externa (OE) has been posed to doctors for a long time, and until the discovery of antibiotics (AB) and antifungal drugs (AFD) remained unresolved. OE can affect up to 10% of people at different periods of their lives, manifesting itself in the form of acute OE (AOE) in 95% of cases after the age of 2 years. There is a reduction in the number of patients admittance to the ENT specialist with the onset of the COVID-19 pandemic while maintaining the structure of the pathology, where AOE - in the top three of frequency. At the same time, there are a reports of an increasing amount of AOE cases in patients with COVID-19. Aim: To evaluate the dynamics of pain, the need for additional analgesia, systemic antibacterial therapy (ABT) or topical antifungal therapy (TAT), the level of compliance in patients with AOE in empirical treatment with a complex drug «Candibiotic» in comparison with protocol treatment. Material and methods: The study included adults and children from 2 years of age with a diagnosis of AOE: 30 patients in the main (17 adults, 13 children) and 25 patients in the control (15 adults, 10 children) groups. Patients in the main group as a basic treatment received topical ear drops «Candibiotic». Patients in the control group received protocol treatment. If necessary, patients were additionally prescribed analgesic therapy (ibuprofen in the appropriate dose), systemic ABT or TAT. For subjective pain asses, a visual analog scale (VAS) was used. Compliance was assessed using the Morisky-Green questionnaire. The obtained results were processed by common statistical methods. Results and discussion: In the main group of patients receiving «Candibiotic», there were fewer needs for additional systemic analgesia, systemic ABT, no one needed additional TAT. Comparison of the obtained results (percentage) in the form of arbitrary tables using the chi-squared test established that the relationship between factor and performance traits is not statistically significant (p>0.05). The mean compliance score between the two study groups differed significantly as a whole and separately between the groups of adults and children (p<0.05). Conclusion: In patients of adult and pediatric age with a diagnosis of AOE at the initial empirical appointment of the drug «Candibiotic» there was less need for additional analgesia, the appointment of systemic ABT and AFT. Such results did not differ statistically significantly from the results in adult and pediatric patients who were prescribed protocol treatment of AOE. Patients with «Candibiotic» monotherapy had a significantly higher average compliance score compared with the control group, especially among parents of children with AOE. The complex composition of ear drops «Candibiotic» contributes to a high adherence to treatment in patients, and the lack of an absolute analogue makes it the means of choice for the initial empirical treatment of AOE in adults and children over 2 years.

Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies produced by cleavage of the proteome with a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false due to (1) heterogeneous proteoforms and (2) technical artifacts. Here we describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. PeCorA fits linear models to assess whether a peptide's change across treatment groups differs from all other peptides assigned to the same protein. PeCorA revealed that ∼15% of proteins in a mouse microglia stress data set contain at least one discordant peptide. Inspection of the discordant peptides shows the utility of PeCorA for the direct and indirect detection of regulated post-translational modifications (PTMs) and also for the discovery of poorly quantified peptides. The exclusion of poorly quantified peptides before protein quantity summarization decreased false-positives in a benchmark data set. Finally, PeCorA suggests that the inactive isoform of prothrombin, a coagulation cascade protease, is more abundant in plasma from COVID-19 patients relative to non-COVID-19 controls. PeCorA is freely available as an R package that works with arbitrary tables of quantified peptides.

Rule-based spreadsheet data transformation from arbitrary to relational tables

The paper discusses issues of rule-based data transformation from arbitrary spreadsheet tables to a canonical (relational) form. We present a novel table object model and rule-based language for table analysis and interpretation. The model is intended to represent a physical (cellular) and logical (semantic) structure of an arbitrary table in the transformation process. The language allows drawing up this process as consecutive steps of table understanding, i. e. recovering implicit semantics. Both are implemented in our tool for spreadsheet data canonicalization. The presented case study demonstrates the use of the tool for developing a task-specific rule-set to convert data from arbitrary tables of the same genre (government statistical websites) to flat file databases. The performance evaluation confirms the applicability of the implemented rule-set in accomplishing the stated objectives of the application.

Transforming arbitrary tables into logical form with TARTAR

The tremendous success of the World Wide Web is countervailed by efforts needed to search and find relevant information. For tabular structures embedded in HTML documents, typical keyword or link-analysis based search fails. The Semantic Web relies on annotating resources such as documents by means of ontologies and aims to overcome the bottleneck of finding relevant information. Turning the current Web into a Semantic Web requires automatic approaches for annotation since manual approaches will not scale in general. Most efforts have been devoted to automatic generation of ontologies from text, but with quite limited success. However, tabular structures require additional efforts, mainly because understanding of table contents requires the comprehension of the logical structure of the table on the one hand, as well as its semantic interpretation on the other. The focus of this paper is on the automatic transformation and generation of semantic (F-Logic) frames from table-like structures. The presented work consists of a methodology, an accompanying implementation (called TARTAR) and a thorough evaluation. It is based on a grounded cognitive table model which is stepwise instantiated by the methodology. A typical application scenario is the automatic population of ontologies to enable query answering over arbitrary tables (e.g. HTML tables).

Model-independent mean-field theory as a local method for approximate propagation of information

We present a systematic approach to mean-field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean-field equations derived here may serve as a local, and thus very simple, method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. Our approach is 'model-independent' in the sense that we do not assume a particular type of dependences; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependences. In general, there are multiple solutions to the mean-field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean-field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared with the exact results.

Model-independent mean-field theory as a local method for approximate propagation of information

We present a systematic approach to mean-field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean-field equations derived here may serve as a local, and thus very simple, method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. Our approach is ‘model-independent’ in the sense that we do not assume a particular type of dependences; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependences. In general, there are multiple solutions to the mean-field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean-field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared with the exact results.

The Calibration of Thermocouples by Freezing-Point Baths and Empirical Equations

A calibration system is described which is based on the use of a few precisely determined experimental values obtained from freezing-point baths. Characteristics of the individual thermocouples at intermediate points are obtained by passing empirical equations of prescribed form through the test values. A program is reviewed, by which a high-speed digital computer accomplishes the necessary conversions, curve fittings, comparisons of individual characteristics with arbitrary reference tables, and the printing out of a table of differences. Test results for a series of iron-constantan thermocouples, over the temperature range 32–1225 F, are presented to illustrate the use of the system and the uncertainties involved. Comparisons are drawn between these results and those obtained by other methods.