YoloXT: A object detection algorithm for marine benthos

In recent years, the marine economy has developed rapidly, and human demand for marine resources has increased greatly. At present, target detection technology has a wide range of applications and prospects in seabed observation and ocean engineering. However, the accuracy and robustness of existing target detection methods are low due to the complex underwater environment, poor lighting, and poor quality of undersea images and videos. To solve these problems, this paper proposes YoloXT, a new quantitative identification method for marine benthos. YoloXT introduces the DECA (Deformable Coordinate Attention) module, which expands the spatial awareness in feature extraction and can learn image features more effectively. Meanwhile FPST-PAN (Feature Pyramid S2win Transformer, Improved Path Aggregation Network) was proposed to deal with the problem of marine benthic target diversity. It further integrates deep and shallow features through multi-scale skip-connection and Transformer and improves the model's ability to deal with complex and changeable marine environments. Finally, the positive and negative sample assignment strategy OAA (Optimal Anchor Assignment) applied to the detection head is proposed. It effectively avoids the problem of unbalanced distribution of positive and negative samples caused by traditional sample assignment methods and marine benthos image noise. Experiments on the IOC-URPC dataset show that the mAP of YoloXT is 3.9% higher than that of YoloX, reaching 70.9%. YoloXT has demonstrated excellent performance in quantitative identification task of marine organisms, which can effectively contribute to the exploitation and conservation of marine resources. The source code is publicly available at https://github.com/F1veZhang/YOLOXT.     

U-YOLOv7: A network for underwater organism detection

Detecting and monitoring underwater organisms is very important for sea aquaculture. The human eye struggles to quickly distinguish between aquatic species due to their variety and dense dispersion. In this paper, a deep learning object detection algorithm based on YOLOv7 is used to design a new network, called Underwater-YOLOv7 (U-YOLOv7), for underwater organism detection. This model satisfies the requirements with regards to both speed and accuracy. First, a network combining CrossConv and an efficient squeeze-excitation module is created. This network increases the extraction of channel information while reducing parameters and enhancing the feature fusion of the network. Second, a lightweight Content-Aware ReAssembly of FEatures (CARAFE) operator is used to obtain more semantic information about underwater images before feature fusion. A 3D attention mechanism is incorporated to improve the anti-interference ability of the model in underwater recognition. Finally, a decoupling head using hybrid convolution is designed to accelerate convergence and improve the accuracy of underwater detection. The results show that the network proposed in this paper obtains an improvement of 3.2% in accuracy, 2.3% in recall, and 2.8% in the mean average precision value and runs at up to 179 fps, far outperforming other advanced networks. Moreover, it has a higher estimation accuracy than the YOLOv7 network.     

A case study of utilizing YOLOT based quantitative detection algorithm for marine benthos

Current object detection algorithms suffer from low accuracy and poor robustness when used to detect marine benthos due to the complex environment and low light levels on the seabed. To solve these problems, the YOLOT (You Only Look Once with Transformer) algorithm, a quantitative detection algorithm based on the improved YOLOv4, is proposed for marine benthos in this paper. To improve the feature extraction capability of the neural network, the transformer mechanism is introduced in the backbone feature extraction network and feature fusion part of YOLOv4, which enhances the adaptability of the algorithm to targets in complex undersea environments. On the one hand, the self-attention unit is embedded into CSPDarknet-53, which improves the feature extraction capability of the network. On the other hand, it is transformer-based feature fusion rules that are introduced to enhance the extraction of contextual semantic information in the feature pyramid network. In addition, probabilistic anchor assignment based on Gaussian distribution is introduced to network training. The experimental validation shows that compared with the original YOLOv4, the YOLOT algorithm improves the recognition precision from 75.35% to 84.44% on the marine benthic dataset. The improvement reflects that YOLOT is suitable for the quantitative detection of marine benthos.     

Protein circuit design using de novo proteins

Protein-based biological circuits enable customized control of cellular functions, and de novo protein design enables circuit functionalities that are not possible by repurposing natural proteins. Here, I highlight recent progress in protein circuit design, including CHOMP, developed by Gao et al., and SPOC, developed by Fink et al.     

Variant-to-gene,gene-to-trait: finding new sleep genes through GWAS

Distilling insomnia genome-wide association study (GWAS) variants, Palermo and colleagues identified several genes that participate in sleep regulation in two different model organisms. This workflow sets off an innovative strategy to extract biological relevance from large human genomic databases.     

LigBind: Identifying Binding Residues for Over 1000 Ligands with Relation-Aware Graph Neural Networks

Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind’s efficacy on large-scale ligand-specific benchmark datasets, and it generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The web server and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.     

Effector protein structures: a tale of evolutionary relationship

Effector proteins are highly diverse, often lacking similarity in their protein sequences, making it challenging to determine their biological function. Using AlphaFold2 (AF2), Seong and Krasileva recently found that effector structures, but not sequences, share commonality. This helps further understanding of effector evolution across fungal species and reveals unique sequence-unrelated, structurally similar, effector families.     

Vertebrate–arthropod communication dictates tick development and pathogen transmission

Cross-species communication drives the coordination of diverse biological processes in complex systems. Rana et al. discovered that Ixodes scapularis, the tick vector of Lyme disease, produces a receptor that binds host interferon-gamma (IFNγ) in the blood meal, which orchestrates tick development, immunity, and vector competence.     

New tRNA-targeting transposons that hijack phage and vesicles

Genomic islands are hotspots for horizontal gene transfer (HGT) in bacteria, but, for Prochlorococcus, an abundant marine cyanobacterium, how these islands form has puzzled scientists. With the discovery of tycheposons, a new family of transposons, Hackl et al. provide evidence for elegant new mechanisms of gene rearrangement and transfer among Prochlorococcus and bacteria more broadly.     

Genetic engineering for enhanced biological nitrogen fixation in cereal crops

Enhancing biological nitrogen (N) fixation in cereal crops has been a long-sought objective. Recently, Yan et al. identified plant compounds that induce biofilm production of diazotrophic bacteria and then performed genetic engineering in order to improve nitrogen fixation in rice plants. These findings hold promise for sustainable agriculture.     

ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope of covered reactions and compounds, as well as in metrics for ranking and evaluation. In this work, we present a new computational resource called ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer. It consists of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 33′000 known and 390′000 novel reactions predicted with generalized enzymatic reactions rules and over 74′000 compounds, of which 19′000 are known to biochemical databases and 55′000 only to PubChem. Over 1′000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included into the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways and products of lignin degradation to predict valuable compound derivations. In line with the standards of open science, we have made the toolbox freely available to the scientific community on git (https://github.com/EPFL-LCSB/ARBRE) and we provide the web-version at http://lcsb-databases.epfl.ch/arbre/. We envision that ARBRE will provide the community with a new computational resource and comprehensive search tool to predict and rank pathways towards industrially important aromatic compounds.     

SARS-CoV-2 targets the liver and manipulates glucose metabolism

A recent publication by Barreto and colleagues showed that SARS-CoV-2 directly triggers hyperglycemia by infecting hepatocytes and inducing phosphoenolpyruvate carboxykinase (PEPCK)-dependent gluconeogenesis. Here, we discuss the biological importance of these findings, including the hepatic tropism of SARS-CoV-2. We also comment on the clinical implications of the bidirectional connection between COVID-19 and noncommunicable diseases.     

Ensembles of synthetic polymers mimic biological fluids

Recently a report by Ruan et al. in Nature described how relatively simple random heteropolymers can replicate the properties of biological fluids. These polymers capture the segmental-level interactions between proteins and could enhance folding of membrane proteins, improve stability, and enable DNA sequestration in a chemistry specific manner.     

Sideways: road to gene-by-gene functional screening in malaria parasites

Genome-wide screening in apicomplexan species has transformed our understanding of these parasitic protozoa. Kimmel et al. report a 'knock sideways' system and provide a powerful use case for its feasibility in a gene-by-gene screening in Plasmodium falciparum. Carefully deployed, a novel toolkit helps to dissect the biological uniqueness of an important parasite.     

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html     

Feeding the world sustainably: efficient nitrogen use

Globally, overuse of nitrogen (N) fertilizers in croplands is causing severe environmental pollution. In this context, Gu et al. suggest environmentally friendly and cost-effective N management practices and Hamani et al. highlight the use of microbial inoculants to improve crop yields, while reducing N-associated environmental pollution and N-fertilizer use.     

ParaSiteDB – A user-friendly database for managing a parasitological collection

ParaSiteDB is an application for arranging and managing a parasitological collection. It has been designed to provide a user-friendly, easily manageable and searchable site and is suitable for small to bigger collections.The source code of the application is available on GitHub: https://github.com/goobar4/aacrg.The demonstration version of the application is available on https://syrota.info/wormbasehttp://syrota.info/wormbase.     

Dual function of the CHS3-CSA1 immune receptor pair

Plant nucleotide-binding leucine-rich repeat (NLR) receptors mediate specific recognition of pathogen effectors to initiate effector-triggered immunity. Recently, studies by Schulze et al., Yang et al., and Gu et al. collectively show that the Arabidopsis (Arabidopsis thaliana) NLR pair CHS3-CSA1 acts through two distinct activation modes to recognize different pathogen effectors, thus revealing the dual function of the CHS3-CSA1 pair in plant disease resistance.     

Viral sponges sequester nucleotide signals to inactivate immunity

Bacteria synthesize specialized nucleotide signals to control anti-phage defense. Two papers – by Huiting et al. and Jenson et al. – now reveal that bacteriophages encode protein ‘sponges’ that sequester cyclic oligonucleotide immune signals and inactivate host antiviral immunity.