Strain variation in Clostridioides difficile toxin activity associated with genomic variation at both PaLoc and non-PaLoc loci

Clinical disease from Clostridioides difficile infection can be mediated by two toxins and their neighboring regulatory genes encoded within the five-gene pathogenicity locus (PaLoc). We provide several lines of evidence that the toxin activity of C. difficile may be modulated by genomic variants outside of the PaLoc. We used a phylogenetic tree-based approach to demonstrate discordance between toxin activity and PaLoc evolutionary history, an elastic net method to show the insufficiency of PaLoc variants alone to model toxin activity, and a convergence-based bacterial genome-wide association study (GWAS) to identify correlations between non-PaLoc loci with changes in toxin activity. Combined, these data support a model of C. difficile disease wherein toxin activity may be strongly affected by many non-PaLoc loci. Additionally, we characterize multiple other in vitro phenotypes relevant to human infections including germination and sporulation. These phenotypes vary greatly in their clonality, variability, convergence, and concordance with genomic variation. Lastly, we highlight the intersection of loci identified by GWAS for different phenotypes and clinical severity. This strategy to identify the overlapping loci can facilitate the identification of genetic variation linking phenotypic variation to clinical outcomes.

An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.

The existence of epiphyte on thallus Eucheuma denticulatum (Rhodophyceae) in varying depths cultivated with vertical net method

The existence of macroepiphyte is one of the issues seaweed farmers often face. This research aims to explore the existence of macroepiphyte attached to seaweed Eucheuma denticulatum at varying depths using vertical net method. Research found that the highest and the lowest velocity of macroepiphyte on day -10 in the depth of 50 cm and 200 cm is 248,4 and 121,28 ind/m2/day. On day-20, in the depth of 100 cm and 200 cm is 333,54 and 270,01 ind/m2/day. The most dominating macroepiphyte in the attachment velocity is C. Crasa. Physical and chemical parameter showed around 29o-30oC. Current velocity 0,050-0,067 m/sec. Brightness 92%. Salinity 30-33‰. Nitrate 0,237-0,0416 mg/L. Phosphate 0,0015-0,0036 mg/L. Dissolved oxygen 5,7-6,2 mg/L. The obtained optimum environmental parameter and the type of the macroepiphyte attachment did not show any significant negative effect to the growth of E. denticulatum.

Co-existence of Epiphytes on Eucheuma Denticulatum (Rhodophyceae) in Varying Depths Cultivated with Verti Net Method

The existence of macroepiphytes is one of the issues seaweed farmers often face. This research aimed to explore the co-existence of macroepiphytes with seaweed Eucheuma denticulatum at varying depths using verti net method. Results showed that the highest and the lowest density of macroepiphyte were obtained on day -10 in the depth of 50 cm and 200 cm at 248,4 and 121,28 ind/m2/day, respectively. On day-20, in the depth of 100 cm and 200 cm the densities were 333,54 and 270,01 ind/m2/day, respectively. The most dominant macroepiphyte y is Chatomorpha crasa. Physical and chemical parameters showed a temperature of 29°-30°C, current velocity of 0,050-0,067 m/sec, brightness 92%, salinity 30-33‰, nitrate 0,237-0,0416 mg/L, phosphate 0,0015-0,0036 mg/L and dissolved oxygen 5,7-6,2 mg/L. The obtained optimum environmental parameters and the type of the macroepiphytes did not show any significant negative effect on the growth of E. denticulatum.

Lpnet: Reconstructing Phylogenetic Networks from Distances Using Integer Linear Programming

We present Lpnet, a variant of the widely used Neighbor-net method that approximates pairwise distances between taxa by a circular phylogenetic network. We use integer linear programming to replace a heurisristic part of the agglomeration procedure based on local information by an exact global solution. This approach achieves an improved approximation of the input distance for the clear majority of experiments that we have run for simulated and real data. We release an implementation in R that can handle up to 94 taxa and usually needs about one minute on a standard computer for 80 taxa.

DR-Net: dual-rotation network with feature map enhancement for medical image segmentation

To obtain more semantic information with small samples for medical image segmentation, this paper proposes a simple and efficient dual-rotation network (DR-Net) that strengthens the quality of both local and global feature maps. The key steps of the DR-Net algorithm are as follows (as shown in Fig. 1). First, the number of channels in each layer is divided into four equal portions. Then, different rotation strategies are used to obtain a rotation feature map in multiple directions for each subimage. Then, the multiscale volume product and dilated convolution are used to learn the local and global features of feature maps. Finally, the residual strategy and integration strategy are used to fuse the generated feature maps. Experimental results demonstrate that the DR-Net method can obtain higher segmentation accuracy on both the CHAOS and BraTS data sets compared to the state-of-the-art methods.

DNA methylation dynamics of long noncoding RNA during human fetal development

Aim: To determine whether the promoters of long noncoding RNAs (lncRNAs) undergo dynamic changes in DNA methylation during fetal development. Methods: ANOVA and the tissue specificity index were used to identify and validate tissue-specific methylation sites. Age-associated DNA methylation signatures were identified by applying the elastic net method. Results: The lncRNA methylome landscape was characterized in four types of fetal tissue and at three gestational time points, and specific characteristics relative to the tissue of origin and developmental age were identified. Higher levels of lncRNA methylation might be involved in tissue differentiation. LncRNAs harboring age-associated methylation signatures may participate in the fetal developmental process. Conclusion: This study provides novel insights into the role of lncRNA methylomes in fetal tissue specification and development.

BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.