A Corpus of Czech Essays from the Turn of the 1900s

A literary essay is an interesting unit for language analyses, as its stylistic means often exceed the boundaries of the genre of an artistic essay. The article presents a new corpus of Czech literary essays covering approximately fifty years from 1890 to 1940. Along with the characterisation of the corpus and its annotation, the paper focuses on the TxM corpus tool: In the second part of the study, we use selected texts to conduct an analysis of seven various authors through multidimensional cluster analysis, factorial correspondence analysis and a specificity score. The main parameter of the analyses was usage of parts of speech in texts by individual authors. At present, the Corpus of Czech Essays contains 40 essayist titles written by 15 authors covering various topics (music, visual arts, theatre, literature, etc.).

Human Identification with their VOC Distribution Through CMS – SEN Model

Smell printing or odor printing is a novel morphological characteristic that an object can be defined by its odor. Human body odor is one such biological trait that yields less error rate of 15% among other biometrics. The human odor printing or smell printing possesses significance against the world towards screening of security checkpoint, searching for survivals under rubbles, investigating criminals, and many more. Cogno-monitoring system (CMS) is a specific prototype to furnish two essential processes -odor analysis and odor encoding through the Sensing-Encoding-Notifying (SEN) model to give the sensitivity and specificity score among the individuals. Human body odor can be interpreted as the alliance of various volatile organic compounds (VOCs) and they are recognized, classified in the encoding process. This article exhibits a detailed analysis of the traditional detection methods including bioanalysis concerning the human body human body odor experimented with 6 people. By applying principal component analysis along with random forest classifier, the VOCs distribution of the individuals is measured. This work calculates that 18.7% of VOCs are having a match with all the individuals which become the plinth for the identification of humans.

A Machine Learning Approach as an Aid for Early COVID-19 Detection

The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19.

ELIHKSIR Web Server: Evolutionary Links Inferred for Histidine Kinase Sensors Interacting with Response Regulators

Two-component systems (TCS) are signaling machinery that consist of a histidine kinases (HK) and response regulator (RR). When an environmental change is detected, the HK phosphorylates its cognate response regulator (RR). While cognate interactions were considered orthogonal, experimental evidence shows the prevalence of crosstalk interactions between non-cognate HK–RR pairs. Currently, crosstalk interactions have been demonstrated for TCS proteins in a limited number of organisms. By providing specificity predictions across entire TCS networks for a large variety of organisms, the ELIHKSIR web server assists users in identifying interactions for TCS proteins and their mutants. To generate specificity scores, a global probabilistic model was used to identify interfacial couplings and local fields from sequence information. These couplings and local fields were then used to construct Hamiltonian scores for positions with encoded specificity, resulting in the specificity score. These methods were applied to 6676 organisms available on the ELIHKSIR web server. Due to the ability to mutate proteins and display the resulting network changes, there are nearly endless combinations of TCS networks to analyze using ELIHKSIR. The functionality of ELIHKSIR allows users to perform a variety of TCS network analyses and visualizations to support TCS research efforts.

Evaluation of Two Chemiluminescent and Three ELISA Immunoassays for the Detection of SARS-CoV-2 IgG Antibodies: Implications for Disease Diagnosis and Patients’ Management

The estimation of anti-SARS-CoV-2 IgG antibodies is possibly the best approach to accurately establish the number of infected individuals and the seroprevalence of COVID-19 within a population. Thus, several commercial immunoassays have recently been developed. The purpose of our study was to assess the performance of five commonly used immunoassays in Greece (3 ELISA, namely Euroimmun SARS-CoV-2, GA GENERIC SARS-CoV-2 and Vircell COVID-19; and 2 chemiluminescent, namely ABBOTT SARS-CoV-2 and ROCHE Elecsys Anti-SARS-CoV-2 test) for the detection of anti-SARS-CoV-2 IgG antibodies. Sera specimens derived from 168 individuals were utilized to assess the specificity and sensitivity score of each assay. Among them, we included 99 COVID-19 patients (29 asymptomatic, 36 with symptom onset 4 to 14 days before serum sampling, and 34 with symptom initiation ≥ 15 days ago), and 69 volunteers with sera specimens collected prior to the SARS-CoV-2 outbreak and maintained at −80°C. We demonstrated that chemiluminescent immunoassays exhibit a significantly higher specificity score but a lower sensitivity, compared to ELISA immunoassays. Moreover, immunoassays detecting IgG antibodies against SARS-CoV-2 N protein instead of S protein alone are more reliable, considering both specificity and sensitivity scores. Interestingly, all asymptomatic patients displayed anti-SARS-CoV-2 IgG antibodies, confirmed by at least two immunoassays. We suggest that chemiluminescent assays could be used as screening methods for the detection of anti-SARS-CoV-2 antibodies to evaluate the possible prevalence of disease in the general population, while ELISA assays would be more reliable to evaluate, and follow-up confirmed COVID-19 patients.

SPECS: A non-parameteric method to identify tissue-specific molecular features for unbalanced sample groups

To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all. We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.ugent.be/ceeverae/SPECs. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.

A new toolkit for natural text processing with the TXM platform and its application to a corpus for analysis of texts propagating extremist views

TXM platform provides a wide range of corpus analysis tools including correspondence analysis, clustering, lexical table construction, and parametrized subcorpus selection. The default structural unit of analysis for TXM is a token. The only TXM extension available by default is TreeTagger which performs automated morphological analysis and lemmatization during the corpus import process. However, it is possible to supply each token with a number of features enabling a more advanced text analysis. In this work we present a number of tools developed for even a more extensive, complex and flexible corpus analysis with TXM relying both on the tools previously developed by our team and on publicly available software libraries. We focus in particular on a stemming technique that uses a word structural pattern method and on noun phrase recognition that together make it possible to perform more sophisticated and powerful queries and analyses of the corpus not limited to word forms. The structural pattern stemming method is based on a set of specific language rules that allow separating a word stem from all affixes. The recognition of noun phrases is based on rules allowing the detection of subordination and coordination relations among nouns. These extensions result in the improvement of performance of statistical tools used by TXM, such as specificity scores and correspondence analysis. The new set of tools has been tested on a corpus including texts marked as «extremist» by experts along with «neutral» texts in similar domains. The corpus of approximately 900,000 words is divided into eight subcorpora: neutral texts oppose seven thematic subcorpora considered as extremist (namely aggressive, fascist, ideological, nationalistic, religious, separatist, and terroristic). The specificity analysis detects the words (or other structural units) that are significantly more or less frequent in a given subcorpus compared to the entire corpus. The specificity score for selected units can be compared across all the subcorpora in order to verify their difference or similarity. The correspondence analysis produces a chart where the subcorpora are represented as points in a twodimensional space based on their similarity as to the frequency of selected units. All tests demonstrated a significant difference between neutral texts, on one side, and marked, on the other. Two «extremist» subcorpora, religious and ideological, demonstrated similar results and can probably be merged. These facts encourage further research on fully automatic or computer-aided expert recognition of extremist texts.

Potential high-frequency off-target mutagenesis induced by CRISPR/Cas9 in Arabidopsis and its prevention

Specificity of CRISPR/Cas9 tools has been a major concern along with the reports of their successful applications. We report unexpected observations of high frequency off-target mutagenesis induced by CRISPR/Cas9 in T1 Arabidopsis mutants although the sgRNA was predicted to have a high specificity score. We also present evidence that the off-target effects were further exacerbated in the T2 progeny. To prevent the off-target effects, we tested and optimized two strategies in Arabidopsis, including introduction of a mCherry cassette for a simple and reliable isolation of Cas9-free mutants and the use of highly specific mutant SpCas9 variants. Optimization of the mCherry vectors and subsequent validation found that fusion of tRNA with the mutant rather than the original sgRNA scaffold significantly improves editing efficiency. We then examined the editing efficiency of eight high-specificity SpCas9 variants in combination with the improved tRNA-sgRNA fusion strategy. Our results suggest that highly specific SpCas9 variants require a higher level of expression than their wild-type counterpart to maintain high editing efficiency. Additionally, we demonstrate that T-DNA can be inserted into the cleavage sites of CRISPR/Cas9 targets with high frequency. Altogether, our results suggest that in plants, continuous attention should be paid to off-target effects induced by CRISPR/Cas9 in current and subsequent generations, and that the tools optimized in this report will be useful in improving genome editing efficiency and specificity in plants and other organisms.

Cultural Competency Training Requirements in Graduate Medical Education

Background Cultural competency is an important skill that prepares physicians to care for patients from diverse backgrounds. Objective We reviewed Accreditation Council for Graduate Medical Education (ACGME) program requirements and relevant documents from the ACGME website to evaluate competency requirements across specialties. Methods The program requirements for each specialty and its subspecialties were reviewed from December 2011 through February 2012. The review focused on the 3 competency domains relevant to culturally competent care: professionalism, interpersonal and communication skills, and patient care. Specialty and subspecialty requirements were assigned a score between 0 and 3 (from least specific to most specific). Given the lack of a standardized cultural competence rating system, the scoring was based on explicit mention of specific keywords. Results A majority of program requirements fell into the low- or no-specificity score (1 or 0). This included 21 core specialties (leading to primary board certification) program requirements (78%) and 101 subspecialty program requirements (79%). For all specialties, cultural competency elements did not gravitate toward any particular competency domain. Four of 5 primary care program requirements (pediatrics, obstetrics-gynecology, family medicine, and psychiatry) acquired the high-specificity score of 3, in comparison to only 1 of 22 specialty care program requirements (physical medicine and rehabilitation). Conclusions The degree of specificity, as judged by use of keywords in 3 competency domains, in ACGME requirements regarding cultural competency is highly variable across specialties and subspecialties. Greater specificity in requirements is expected to benefit the acquisition of cultural competency in residents, but this has not been empirically tested.