Andresengrady0178

These embeddings are further integrated with the KG triplet embeddings via a gating mechanism, thus enriching the KG representations and alleviating the inherent structure sparsity. Experiments on benchmark datasets show that our method significantly outperforms several state-of-the-art methods.Background There is growing interest in the connection between the gut microbiome and human health and disease. Conventional approaches to analyse microbiome data typically entail dimensionality reduction and assume linearity of the observed relationships, however, the microbiome is a highly complex ecosystem marked by non-linear relationships. In this study, we use topological data analysis (TDA) to explore differences and similarities between the gut microbiome across several countries. Methods We used curated adult microbiome data at the genus level from the GMrepo database. The dataset contains OTU and demographical data of over 4,400 samples from 19 studies, spanning 12 countries. We analysed the data with tmap, an integrative framework for TDA specifically designed for stratification and enrichment analysis of population-based gut microbiome datasets. Results We find associations between specific microbial genera and groups of countries. Specifically, both the USA and UK were significantly co-enriched with the proinflammatory genera Lachnoclostridium and Ruminiclostridium, while France and New Zealand were co-enriched with other, butyrate-producing, taxa of the order Clostridiales. Conclusion The TDA approach demonstrates the overlap and distinctions of microbiome composition between and within countries. This yields unique insights into complex associations in the dataset, a finding not possible with conventional approaches. It highlights the potential utility of TDA as a complementary tool in microbiome research, particularly for large population-scale datasets, and suggests further analysis on the effects of diet and other regionally varying factors.Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features low frequency power (LF 0.03-0.15 Hz), movement frequency power (MF 0.15-0.5 Hz), high frequency power (HF 0.5-1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.The current study aimed to explore the linguistic analysis of neologism related to Coronavirus (COVID-19). Recently, a new coronavirus disease COVID-19 has emerged as a respiratory infection with significant concern for global public health hazards. However, with each passing day, more and more confirmed cases are being reported worldwide which has alarmed the global authorities including the World Health Organization (WHO). In this study, the researcher uses the term neologism which means the coinage of new words. Neologism played a significant role throughout the history of epidemic and pandemic. The focus of this study is on the phenomenon of neologism to explore the creation of new words during the outbreak of COVID-19. The theoretical framework of this study is based on three components of neologism, i.e. word formation, borrowing, and lexical deviation. The researcher used the model of neologism as a research tool which is presented by Krishnamurthy in 2010. The study is also compared with the theory of onomasiology by Pavol Stekauer (1998). The secondary data have been used in this study. The data were collected from articles, books, Oxford Corpus, social media, and five different websites and retrieved from January 2020 to April 2020. The findings of this study revealed that with the outbreak of COVID-19, the majority of the people on social media and state briefings, the word-formation is utilized in the form of nouns, adjectives, and verbs. The abbreviations and acronyms are also used which are related to the current situation of COVID-19. No doubt, neologisms present colorful portrayals of various social and cultural practices of respective societies the rationale behind them all remains the same.

Management of immunocompromised COVID-19 patients is the object of current debate. Accumulating evidence suggest that treatment with high-titer COVID-19 convalescent plasma (CCP) may be effective in this characteristic clinical scenario.

A 52-years old immunocompromised female patient, previously treated with rituximab for low grade B-cell lymphoma, showed prolonged SARS-CoV-2 shedding and a long-term course of signs of severe COVID-19. A first cycle of treatment with remdesivir, a nucleotide analogue prodrug effective in inhibiting SARS-CoV-2 replication, did not provide fully and sustained clinical remission. A second hospitalization was deemed necessary after 10days from the first hospital discharge due to recrudescence of symptoms of severe COVID-19 and the evidence of bilateral interstitial pneumonia at the chest-CT scan. Clinical and radiological findings completely disappeared after CCP administration. The viral culture confirmed the absence of SARS-CoV-2-related cytopathic effect. The clinical eva response in immunocompromised patients with SARS-CoV-2 infection is required.

High rates of vaccination worldwide are required to establish a herd immunity and stop the current COVID-19 pandemic evolution. Vaccine hesitancy is a major barrier in achieving herd immunity across different populations. This study sought to conduct a systematic review of the current literature regarding attitudes and hesitancy to receiving COVID-19 vaccination worldwide.

A systematic literature search of PubMed and Web of Science was performed on July 5th, 2021, using developed keywords. Inclusion criteria required the study to (1) be conducted in English; (2) investigate attitudes, hesitancy, and/or barriers to COVID-19 vaccine acceptability among a given population; (3) utilize validated measurement techniques; (4) have the full text paper available and be peer-reviewed prior to final publication.

Following PRISMA guidelines, 209 studies were included. The Newcastle Ottawa (NOS) scale for cross-sectional studies was used to assess the quality of the studies.Overall, vaccine acceptance rates ranged cf-identified as a racial/ethnic minority.

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing.

We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany (

=135), Spain (

=133), Switzerland (

=20) and the United States (

=147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID-19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS).

We describe a potential association of HLA-C*0401 with severe clinical course of COVID-19. Carriers of HLA-C*0401 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted

-value=0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*0401 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles.

HLA-C*0401 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2.

Funded by Roche Sequencing Solutions, Inc.

Funded by Roche Sequencing Solutions, Inc.B cell depleting therapies (BCDTs) are widely used as immunomodulating agents for autoimmune diseases such as multiple sclerosis. Their possible impact on development of immunity to severe acute respiratory syndrome virus-2 (SARS-CoV-2) has raised concerns with the coronavirus disease 2019 (COVID-19) pandemic. We here evaluated the frequency of COVID-19-like symptoms and determined immunological responses in participants of an observational trial comprising several multiple sclerosis disease modulatory drugs (COMBAT-MS; NCT03193866) and in eleven patients after vaccination, with a focus on BCDT. SC79 Almost all seropositive and 17.9% of seronegative patients on BCDT, enriched for a history of COVID-19-like symptoms, developed anti-SARS-CoV-2 T cell memory, and T cells displayed functional similarity to controls producing IFN-γ and TNF. Following vaccination, vaccine-specific humoral memory was impaired, while all patients developed a specific T cell response. These results indicate that BCDTs do not abrogate SARS-CoV-2 cellular memory and provide a possible explanation as to why the majority of patients on BCDTs recover from COVID-19.The exponential growth of scientific literature yields the need to support users to both effectively and efficiently analyze and understand the some body of research work. This exploratory process can be facilitated by providing graphical abstracts-a visual summary of a scientific publication. Accordingly, previous work recently presented an initial study on automatic identification of a central figure in a scientific publication, to be used as the publication's visual summary. This study, however, have been limited only to a single (biomedical) domain. This is primarily because the current state-of-the-art relies on supervised machine learning, typically relying on the existence of large amounts of labeled data the only existing annotated data set until now covered only the biomedical publications. In this work, we build a novel benchmark data set for visual summary identification from scientific publications, which consists of papers presented at conferences from several areas of computer science. We couple this contribution with a new self-supervised learning approach to learn a heuristic matching of in-text references to figures with figure captions.