CoGANPPIS: Coevolution-enhanced Global Attention Neural Network for Protein-Protein Interaction Site Prediction

Protein-protein interactions are essential in biochemical processes. Accurate prediction of the protein-protein interaction sites (PPIs) deepens our understanding of biological mechanism and is crucial for new drug design. However, conventional experimental methods for PPIs prediction are costly and time-consuming so that many computational approaches, especially ML-based methods, have been developed recently. Although these approaches have achieved gratifying results, there are still two limitations: (1) Most models have excavated some useful input features, but failed to take coevolutionary features into account, which could provide clues for inter-residue relationships; (2) The attention-based models only allocate attention weights for neighboring residues, instead of doing it globally, neglecting that some residues being far away from the target residues might also matter. We propose a coevolution-enhanced global attention neural network, a sequence-based deep learning model for PPIs prediction, called CoGANPPIS. It utilizes three layers in parallel for feature extraction: (1) Local-level representation aggregation layer, which aggregates the neighboring residues' features; (2) Global-level representation learning layer, which employs a novel coevolution-enhanced global attention mechanism to allocate attention weights to all the residues on the same protein sequences; (3) Coevolutionary information learning layer, which applies CNN & pooling to coevolutionary information to obtain the coevolutionary profile representation. Then, the three outputs are concatenated and passed into several fully connected layers for the final prediction. Application on two benchmark datasets demonstrated a state-of-the-art performance of our model. The source code is publicly available at https://github.com/Slam1423/CoGANPPIS_source_code

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.Comment: CVPR 2023 accep

Viable but nonculturable bacteria and their resuscitation: implications for cultivating uncultured marine microorganisms

Culturing has been the cornerstone of microbiology since Robert Koch first successfully cultured bacteria in the late nineteenth century. However, even today, the majority of microorganisms in the marine environment remain uncultivated. There are various explanations for the inability to culture bacteria in the laboratory, including lack of essential nutrients, osmotic support or incubation conditions, low growth rate, development of micro-colonies, and the presence of senescent or viable but nonculturable (VBNC) cells. In the marine environment, many bacteria have been associated with dormancy, as typified by the VBNC state. VBNC refers to a state where bacteria are metabolically active, but are no longer culturable on routine growth media. It is apparently a unique survival strategy that has been adopted by many microorganisms in response to harsh environmental conditions and the bacterial cells in the VBNC state may regain culturability under favorable conditions. The resuscitation of VBNC cells may well be an important way to cultivate the otherwise uncultured microorganisms in marine environments. Many resuscitation stimuli that promote the restoration of culturability have so far been identified; these include sodium pyruvate, quorum sensing autoinducers, resuscitation-promoting factors Rpfs and YeaZ, and catalase. In this review, we focus on the issues associated with bacterial culturability, the diversity of bacteria entering the VBNC state, mechanisms of induction into the VBNC state, resuscitation factors of VBNC cells and implications of VBNC resuscitation stimuli for cultivating these otherwise uncultured microorganisms. Bringing important microorganisms into culture is still important in the era of high-throughput sequencing as their ecological functions in the marine environment can often only be known through isolation and cultivation

The influence of H2O or/and O2 introduction during the low-temperature gas-phase sulfation of organic COS + CS2 on the conversion and deposition of sulfur-containing species in the sulfated CeO2-OS catalyst for NH3-SCR

Herein, the typical components of blast furnace gas, including H O and O , were introduced to improve the NH -SCR activity of the sulfated CeO -OS catalyst during the gas-phase sulfation of organic COS + CS at 50 °C. The characterization results demonstrate that the introduction of O or H O during gas-phase sulfation enhances the conversion of organic COS + CS on a cubic fluorite CeO surface and reduces the formation of sulfur and sulfates in the catalyst, but decreases the BET surface area and pore volume of the sulfated CeO -OS catalyst. However, the introduction of O or H O during the gas-phase sulfation increases the molar ratios of Ce /(Ce + Ce ) and O /(O + O + O ) on the sulfated CeO -OS catalyst surface, thus promoting the formation of surface oxygen vacancies and chemisorbed oxygen, and these properties of the catalyst are further enhanced by the co-existence of O and H O. Furthermore, the reduction of sulfates formed under the action of O or H O decreases the weak acid sites of the sulfated CeO -OS catalyst, but the few and highly dispersive sulfates present stronger reducibility, and the proportion of medium-strong acid sites of the catalyst increases. These factors help to improve the NH -SCR activity of the sulfated CeO -OS catalyst. Thus, there exists a synergistic effect of H O and O introduction during gas-phase sulfation on the physical-chemical properties and catalytic performance of the sulfated CeO -OS catalyst by organic COS + CS at 50 °C