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Objective: The reverse transcription–polymerase chain reaction test (RT-PCR) is the gold standard for the diagnosis of coronavirus disease 2019 (COVID-19), and antibody tests are useful as supplemental tools for diagnosis, for measuring the population’s immunity levels, and for checking infection in asymptomatic contacts. This study aimed to evaluate the reliability of five commercial antibody detection test kits. Materials and Methods: The reliability of the Colloidal Gold COVID-19 IgG/IgM Rapid Test Kit, Antibody Rapid Test Hotgen, Beijing Hotgen Biotech Co., Ltd., China), Abbott Chemiluminescent Microparticle Immunoassay (Illinois, USA), Roche Electrochemiluminescence Immunoassay (Roche Diagnostics, Switzerland), Siemens Chemiluminescence (Munich, Germany), and Euroimmun ELISA (Lübeck, Germany) for COVID-19 diagnosis was studied. The antibody-negative group included 50 sera from 2018, and the antibody-positive group included 98 patients with positive RT-PCR results from whom blood samples had been collected 3–9 weeks after hospital discharge. Statistical analysis was performed using SPSS version 23.0 (IBM Corporation, Armonk, NY, USA). The antibody tests’ validity and intra-assay reproducibility were examined, and the Cohen’s kappa coefficients were obtained. The disease prevalence was pegged at 10%. Results: The antibody tests’ sensitivity (69.12–72.46%) and positive predictive values (42.44–100.0%) were low, and their specificity (89.58–100%) and negative predictive values (96.31–97.03%) were high. Their accuracy rates varied from 87.54% to 97.25%, and their intra-assay coefficients of variation varied from 1% to 10%. Conclusion: The agreement between the results of the antibody detection test kits was higher when the kits were classified according to the targeted antigens. The time of blood sample collection, targeted antigens, and antibody types affected the results. Serological tests were found to be useful, and the commercial kits were found to be largely reliable, although, some parameters need to be improved.

CB2 is a multi-agent platform to study collaborative natural language interaction in a grounded task-oriented scenario. It includes a 3D game environment, a backend server designed to serve trained models to human agents, and various tools and processes to enable scalable studies. We deploy CB2 at https://cb2.ai as a system demonstration with a learned instruction following model., Comment: ACL 2023 Demo paper

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that: across 7 architectures trained with 4 algorithms on massive datasets, they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, CREPE, which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of a test dataset containing over $370K$ image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate $325K$, $316K$, and $309K$ hard negative captions for a subset of the pairs. To test productivity, CREPE contains $17K$ image-text pairs with nine different complexities plus $183K$ hard negative captions with atomic, swapping and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to $12\%$. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size., Comment: Updated figures and numbers

Background: Although anticancer chemotherapeutics are available in markets, side effects related to the drugs in clinical use lead to researchers to investigate new drug candidates which are more safe, potent and selective than others. Chalcones are popular with their anticancer activities with the several reported mechanisms including inhibition of angiogenesis, inhibition of tubulin polymerization, and induction of apoptosis etc. Objective: This study was focused on to synthesize of 1-(2,4/2,6-difluorophenyl)-3-(2,3/2,4/2,5/3,4dimethoxyphenyl)- 2-propen-1-ones (1-8) and investigate their cytotoxic properties with possible mechanism of action. Method: The compounds were synthesized by Claisen-Schmidt condensation. The chemical structures were confirmed by H-1 NMR, C-13 NMR, DEPT, COSY, HMQC, HMBC, F-19 NMR and HRMS. In vitro cytotoxic effects of the compounds against human tumour cell lines [gingival carcinoma (Ca9-22), oral squamous cell carcinoma (HSC-2)] and human normal oral cells [gingival fibroblasts (HGF), periodontal ligament fibroblasts (HPLF)] were evaluated via MTT test. Results: All compounds had higher cytotoxicity than reference compound 5-Fluorouracil (5-FU). The compounds 3-7 had higher potency selectivity expression values (PSE) than 5-FU and PSE values of the compounds were over 100. All chalcone derivatives seem good candidates for further studies according to very remarkable and high PSE values. Conclusion: It was clearly demonstrated that compound 7 can induce early apoptosis at a concentration of 10 mu M and dose-dependent late apoptosis starting at 10 mu M. Compound 7 induced cleavage of the apoptosis marker PARP. The results indicate that new chalcones reported here can promote apoptosis in human tumour cell lines., Ataturk University Research Found, Turkey [2015/078]; JSPS KAKENHI [26861748], This work was supported by Ataturk University Research Found, Turkey (Grant Number 2015/078) and in part of JSPS KAKENHI (Grant Number 26861748). The authors are thankful to Dr. Hong Sheng (Meikai Univercity, Japan) for technical assistance, Dr. Yusuf Ozkay (Anadolu University, Turkey) for HRMS spectra and Ataturk University, Faculty of Science, Department of Organic Chemistry, Turkey for NMRs spectra.