Identifying Computer-Translated Paragraphs using Coherence Features

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.Comment: 9 pages, PACLIC 201

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security. While most approaches focus on the image rendering phase, this paper presents a method based on increasing the naturalness of CG facial images from the perspective of spoofing detectors. The proposed method is implemented using a convolutional neural network (CNN) comprising two autoencoders and a transformer and is trained using a black-box discriminator without gradient information. Over 50% of the transformed CG images were not detected by three state-of-the-art spoofing detectors. This capability raises an alarm regarding the reliability of facial authentication systems, which are becoming widely used in daily life.Comment: Accepted to be Published in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) 2018, San Diego, US

Cloning and expression of gene FanC-2NT encoding K99-2NT fimbrial antigen of enterotoxigenic Escherichia coli from diarrheic post-weaning piglets

Background and Purpose: The K99 (F5) is one pilus adhesin that mediates the attachment of enterotoxigenic E. coli (ETEC) strains to small intestines to cause to diarrhea in piglets, lambs and newborn calves. In this work, we carried out cloning and expression of the mature peptide of FanC subunit, K99 fimbriae, one of the most common adhesive antigens in E. coli. Materials and Methods: E. coli 2NT strain was isolated from fecal samples of post-weaning piglets with diarrhea. The coding sequence of the mature peptide of K99-2NT subunit was isolated by PCR amplification and cloned into pGEM®-T Easy vector for sequencing using fluorescent dideoxy-terminator method. Expression of K99-2NT protein which was inserted into pET200/D-TOPO vector induced with IPTG. The PCR product and expression level of protein was examined by agarose gel electrophoresis and sodium dodecyl sulfate-polyacrylamide gel electrophoresis, respectively. Results and Conclusions: We cloned and expressed successfully the mature peptide of K99 subunit with molecular weight of approximately 17.5 kDa from E. coli 2NT strain (named K99-2NT). Nucleotide sequence of the K99-2NT subunit coding region of fanC-2NT gene is 477 bp in length and is 99% similarity with that of fanC gene (accession no: M35282). Highest expression level occurred after 12 h of induction with 0.75 mM IPTG at 37oC. This subunit antigen will be tested for immune response of rat in the next time

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Innovative Integration of Butina Clustering with Ensemble Learning Techniques for the Refined Pharmacophore Modeling of Apelin Receptor Agonists: A High-Impact Computational Approach

3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in Drug Design. Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The ensemble learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by receiver operating characteristic, enrichment factor, Güner-Henry score, and F-measure. Although one of the high-scoring models achieved statistically superior results in each dataset, the ensemble learning method including Voting and Stacking method balanced the shortcomings of each model and passed with close performance measures

Nitrogen removal efficiency of some bacterial strains isolated from seawater in Thua Thien Hue, Vietnam

Background: Nitrifying bacteria in aquaculture environments are capable of removing toxic nitrogen compounds such as ammonium and nitrite. Using these indigenous microbial resources can improve shrimp production.Methods: Screening method was used to isolate aerobic strains of nitrifying bacteria. Species identification for these isolates was done by biomolecular method based on 16S rDNA gene sequence. Ammonium, nitrite and nitrate concentrations from the culture were determined by spectrophotometry at the appropriate wavelength. Temperature, pH, dissolved oxygen and salinity were measured by specialized equipment. Formation and development of flocs during shrimp culture were determined based on their volume and weight. A trial of shrimp nursery was carried out on a small scale with 0.5 m3 tanks containing diluted seawater to 16-18‰ salinity at a density of 400 individual/m3 for 24 days on April 2019. Results: This study isolated two strains of Pseudomonas (BF01 and BF03) and one strain of Cupriavidus oxalaticus BF02 from seawater in Thua Thien Hue province, Vietnam. These bacterial isolates have shown ability to remove nitrogen compounds such as ammonium, nitrite and nitrate in culture medium. Formation and development of flocs were found in trials of shrimp nursery with diluted seawater containing the isolates. Some water quality parameters (temperature, pH, dissolved oxygen, salinity, ammonium and nitrite) were kept at a safe level and juvenile shrimp grown normally during culture.Conclusion: The observations on the water quality and basic growth parameters of juvenile shrimp in the two treatments, diluted seawater and diluted seawater with commercial microbial products, showed that there were no significant differences between them with p = 0.05. This proves that three isolates have played an important role in shrimp nursery.   Keywords: Cupriavidus oxalaticus; Floc; Litopenaeus vanamei; Nitrifying-denitrifying bacteria; Pseudomonas sp.

Coordinated Expression of Cytosolic and Chloroplastic Glutamine Synthetase During Reproductive Stage and Its Impact in GS1 RNAi Transgenic Rice

To understand the reallocation of organic nitrogen from leaf to the flower head of rice, the role of glutamine synthetase (GS) was investigated by characterizing GS1 RNAi transgenic rice, which revealed a significant reduction in panicle number and number of seeds per panicle. We observed the expression of GS isotypes at transcriptional and protein levels in flag leaves, leaf sheaths and panicles at three different flower development stages. The mRNA expression of GS1;1 was clearly suppressed in flag leaves, especially at the flowering stage. GS1 protein was barely detectable in flag leaves until the flowering stage, while GS1 protein was compromised in the leaf sheath and panicle, with transient expression of GS2 protein at the flowering stage. The glutamine level in transgenic plants was significantly reduced in both flag leaves and panicles, but ammonium was highly accumulated. The level of other amino acids, including aspartate and asparagine, tended to be higher in RNAi transgenic plants than the wild type plants during the reproductive stage. In addition, accumulation of toxic ammonium in panicles with low glutamine level might have caused low seed-setting in the transgenic rice. These results indicated that nitrogen reallocation was critical for panicle development, and that multiple GS isotypes functioned cooperatively to complete the rice life cycle when leaf nitrogen was remobilized to the developing reproductive organs. Keywords: ammonium, grain yield, RNA interference, panicle development, nitrogen reallocation, rice, glutamine synthase, flowering stag

Innovative Virtual Screening of PD-L1 Inhibitors: The Synergy of Molecular Similarity, Neural Networks, and GNINA Docking

Immune checkpoint inhibitors have garnered significant attention in oncological research over recent years. A plethora of studies have elucidated that inhibitors targeting the Programmed Death-Ligand 1 (PD-L1) play a pivotal role in circumventing the evasion mechanisms of cancer cells against the immune system. This study aimed to develop an integrated screening model combining an Artificial Neural Network (ANN), Molecular Similarity (MS) assessments, and GNINA 1.0 molecular docking, targeting PD-L1 inhibitors. A database of 2044 substances with known PD-L1 inhibitory activity was compiled from Google Patents and used to enhance molecular similarity evaluations and train the machine learning model. For retrospective validation of the docking procedure, the human PD-L1 protein, with the Protein Data Bank (PDB) ID: 5N2F, was employed as a control. In this phase of the study, 15,235 compounds from the DrugBank database were subjected to a series of screening processes: initially through medicinal chemistry filters, followed by MS assessments, the ANN model, and culminating with molecular docking using GNINA 1.0. The decoy generation yielded promising outcomes, evidenced by an AUC-ROC 1NN value of 0.52 and Doppelganger scores with a mean of 0.24 and a maximum of 0.346, indicating a high resemblance of the decoys to the active set. For MS, the AVALON emerged as the most effective fingerprint for similarity searching, demonstrating an Enrichment Factor (EF) of 1% at 10.96%, an AUC-ROC of 0.963, and an optimal similarity threshold of 0.32. The ANN model demonstrated superior performance in cross-validation, achieving an average precision of 0.863±0.032 and an F1 score of 0.745±0.039, outperforming both the Support Vector Classifier (SVC) and Random Forest (RF) models, albeit not significantly. In external validation, the ANN model maintained its superiority with an average precision of 0.851 and an F1 score of 0.790. GNINA 1.0, employed for molecular docking, was validated through redocking and retrospective control, achieving an AUC of 0.975, with a critical cnn_pose_score threshold of 0.73. From the initial 15,235 compounds, 128 were shortlisted using the MS and ANN models. Further screening through GNINA 1.0 identified 22 potential candidates, among which (3S)-1-(4-acetylphenyl)-5-oxopyrrolidine-3-carboxylic acid emerged as the most promising, with a cnn_pose_score of 0.79, a PD-L1 inhibitory probability of 70.5%, and a Tanimoto coefficient of 0.35