The Specification of Air-to-Air Combat Tactics Using UML Sequence Diagram

Air force air-to-air combat tactics are occurring at a high speed in three-dimensional space. The specification of the tactics requires dealing with a quite amount of information, which makes it a challenge to accurately describe the maneuvering procedure of the tactics. The specification of air-to-air tactics using natural languages is not suitable because of the intrinsic ambiguity of natural languages. Therefore, this paper proposes an approach of using UML Sequence Diagram to describe air-to-air combat tactics. Since the current Sequence Diagram notation is not sufficient to express all aspects of the tactics, we extend the syntax of the Sequence Diagram to accommodate the required features of air-to-air combat tactics. We evaluate the applicability of the extended Sequence Diagram to air-to-air combat tactics using a case example, that is the manned-unmanned teaming combat tactic. The result shows that Sequence Diagram specification is more advantageous than natural language specification in terms of readability, conciseness, and accuracy. However, the expressiveness of the Sequence Diagram is evaluated to be less powerful than natural language, requiring further study to address this issue.

cDNA-detector: detection and removal of cDNA contamination in DNA sequencing libraries

Background Exogenous cDNA introduced into an experimental system, either intentionally or accidentally, can appear as added read coverage over that gene in next-generation sequencing libraries derived from this system. If not properly recognized and managed, this cross-contamination with exogenous signal can lead to incorrect interpretation of research results. Yet, this problem is not routinely addressed in current sequence processing pipelines. Results We present cDNA-detector, a computational tool to identify and remove exogenous cDNA contamination in DNA sequencing experiments. We demonstrate that cDNA-detector can identify cDNAs quickly and accurately from alignment files. A source inference step attempts to separate endogenous cDNAs (retrocopied genes) from potential cloned, exogenous cDNAs. cDNA-detector provides a mechanism to decontaminate the alignment from detected cDNAs. Simulation studies show that cDNA-detector is highly sensitive and specific, outperforming existing tools. We apply cDNA-detector to several highly-cited public databases (TCGA, ENCODE, NCBI SRA) and show that contaminant genes appear in sequencing experiments where they lead to incorrect coverage peak calls. Conclusions cDNA-detector is a user-friendly and accurate tool to detect and remove cDNA detection in NGS libraries. This two-step design reduces the risk of true variant removal since it allows for manual review of candidates. We find that contamination with intentionally and accidentally introduced cDNAs is an underappreciated problem even in widely-used consortium datasets, where it can lead to spurious results. Our findings highlight the importance of sensitive detection and removal of contaminant cDNA from NGS libraries before downstream analysis.

Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction

Molecular property prediction is an essential task in drug discovery. Most computational approaches with deep learning techniques either focus on designing novel molecular representation or combining with some advanced models together. However, researchers pay fewer attention to the potential benefits in massive unlabeled molecular data (e.g., ZINC). This task becomes increasingly challenging owing to the limitation of the scale of labeled data. Motivated by the recent advancements of pretrained models in natural language processing, the drug molecule can be naturally viewed as language to some extent. In this paper, we investigate how to develop the pretrained model BERT to extract useful molecular substructure information for molecular property prediction. We present a novel end-to-end deep learning framework, named Mol-BERT, that combines an effective molecular representation with pretrained BERT model tailored for molecular property prediction. Specifically, a large-scale prediction BERT model is pretrained to generate the embedding of molecular substructures, by using four million unlabeled drug SMILES (i.e., ZINC 15 and ChEMBL 27). Then, the pretrained BERT model can be fine-tuned on various molecular property prediction tasks. To examine the performance of our proposed Mol-BERT, we conduct several experiments on 4 widely used molecular datasets. In comparison to the traditional and state-of-the-art baselines, the results illustrate that our proposed Mol-BERT can outperform the current sequence-based methods and achieve at least 2% improvement on ROC-AUC score on Tox21, SIDER, and ClinTox dataset.

An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph

Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user’s preferences only depend on specific items in the current sequence and do not consider user’s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user’s different behaviors. In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user’s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user’s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user’s different behaviors. Through the activation unit, the user’s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.

cDNA-detector: Detection and removal of cDNA contamination in DNA sequencing libraries

Exogenous cDNA introduced into an experimental system, either intentionally or accidentally, can appear as added read coverage over that gene in next-generation sequencing libraries derived from this system. If not properly recognized and managed, this cross-contamination with exogenous signal can lead to incorrect interpretation of research results. Yet, this problem is not routinely addressed in current sequence processing pipelines. Here, we present cDNA-detector, a computational tool to identify and remove exogenous cDNA contamination in DNA sequencing experiments. We apply cDNA-detector to several highly-cited public databases (TCGA, ENCODE, NCBI SRA) and show that contaminant genes appear in sequencing experiments where they lead to incorrect coverage peak calls. Our findings highlight the importance of sensitive detection and removal of contaminant cDNA from NGS libraries before downstream analysis.

Sequence to Point Learning Based on an Attention Neural Network for Nonintrusive Load Decomposition

Nonintrusive load monitoring (NILM) analyzes only the main circuit load information with an algorithm to decompose the load, which is an important way to help reduce energy usage. Recent research shows that deep learning has become popular for this problem. However, the ability of a neural network to extract load features depends on its structure. Therefore, more research is required to determine the best network architecture. This study proposed two deep neural networks based on the attention mechanism to improve the current sequence to point (s2p) learning model. The first model employs Bahdanau style attention and RNN layers, and the second model replaces the RNN layer with a self-attention layer. The two models are both based on a time embedding layer. Therefore, they can be better applied in NILM. To verify the effectiveness of the algorithms, we selected two open datasets and compared them with the original s2p model. The results show that attention mechanisms can effectively improve the model’s performance.

POI Recommendation Method Using Deep Learning in Location-Based Social Networks

To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location-based social networks is proposed. Firstly, a bidirectional long-short-term memory (Bi-LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long-term and short-term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi-LSTM-Attention to get the attention representation of the current POI check-in sequence, and the Top- N recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.

Long-read sequencing revealed cooccurrence, host range, and potential mobility of antibiotic resistome in cow feces

While it is well recognized that the environmental resistome is global, diverse, and augmented by human activities, it has been difficult to assess risk because of the inability to culture many environmental organisms, and it is difficult to evaluate risk from current sequence-based environmental methods. The four most important criteria to determine risk are whether the antibiotic-resistance genes (ARGs) are a complete, potentially functional complement; if they are linked with other resistances; whether they are mobile; and the identity of their host. Long-read sequencing fills this important gap between culture and short sequence-based methods. To address these criteria, we collected feces from a ceftiofur-treated cow, enriched the samples in the presence of antibiotics to favor ARG functionality, and sequenced long reads using Nanopore and PacBio technologies. Multidrug-resistance genes comprised 58% of resistome abundance, but only 0.8% of them were plasmid associated; fluroquinolone-, aminoglycoside-, macrolide-lincosamide-streptogramin (MLS)-, and β-lactam–resistance genes accounted for 2.7 to 12.3% of resistome abundance but with 19 to 78% located on plasmids. A variety of plasmid types were assembled, some of which share low similarity to plasmids in current databases. Enterobacteriaceae were dominant hosts of antibiotic-resistant plasmids; physical linkage of extended-spectrum β-lactamase genes (CTX-M, TEM, CMY, and CARB) was largely found with aminoglycoside-, MLS-, tetracycline-, trimethoprim-, phenicol-, sulfonamide-, and mercury-resistance genes. A draft circular chromosome of Vagococcus lutrae was assembled; it carries MLS-, tetracycline- (including tetM and tetL on an integrative conjugative element), and trimethoprim-resistance genes flanked by many transposase genes and insertion sequences, implying that they remain transferrable.

Structure-based protein function prediction using graph convolutional networks

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/.

Exploring the planetary-mass population in the Upper Scorpius association*

We aim at identifying very low-mass isolated planetary-mass member candidates in the nearest OB association to the Sun, Upper Scorpius (145 pc; 5–10 Myr), to constrain the form and shape of the luminosity function and mass spectrum in this regime. We conducted a deep multi-band (Y = 21.2, J = 20.5, Z = 22.0 mag) photometric survey of six square degrees in the central region of Upper Scorpius. We extend the current sequence of astrometric and spectroscopic members by about two magnitudes in Y and one magnitude in J, reaching potentially T-type free-floating members in the association with predicted masses below 5 Jupiter masses, well into the planetary-mass regime. We extracted a sample of 57 candidates in this area and present infrared spectroscopy confirming two of them as young L-type members with characteristic spectral features of 10 Myr-old brown dwarfs. Among the 57 candidates, we highlight 10 new candidates fainter than the coolest members previously confirmed spectroscopically. We do not see any obvious sign of decrease in the mass spectrum of the association, suggesting that star processes can form substellar objects with masses down to 4–5 Jupiter masses.