APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Design of Refined Quaternary Electrolyte LiF-LiCl-LiBr-LiI Used for the Liquid Metal Battery.

The advanced electrolyte of liquid metal battery should have low melting point, low ionic solubility, low viscosity, high electric and thermal conductivities, and a suitable density between anode and cathode for declining the operating temperature and realizing the goal of saving-energy. In this study, an excellent quaternary LiF-LiCl-LiBr-LiI (9.1 : 30.0 : 21.7 : 39.2) electrolyte is refined by using thermodynamic models to balance various properties of LiX (X=F, Cl, Br, I) and meet the requirement of advanced electrolyte of liquid metal battery. The refined properties of electrolyte correspond to 2.398 g/cm3 for density, 0.286 mol% for solubility, 4.486 Ohm-1 cm-1 for ionic conductivity, and 0.609 W m-1 for thermal conductivity. The measured melting point is 609.1 K, which is lower than the current operating temperature of 723 K for the lithium-based liquid metal battery. The refined electrolyte consisted by quaternary halide molten-salt provides important references for preparing the advanced liquid metal battery.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides.

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

Aerogels for Thermal Protection and Their Application in Aerospace.

With the continuous development of the world's aerospace industry, countries have put forward higher requirements for thermal protection materials for aerospace vehicles. As a nano porous material with ultra-low thermal conductivity, aerogel has attracted more and more attention in the thermal insulation application of aerospace vehicles. At present, the summary of aerogel used in aerospace thermal protection applications is not comprehensive. Therefore, this paper summarizes the research status of various types of aerogels for thermal protection (oxide aerogels, organic aerogels, etc.), summarizes the hot issues in the current research of various types of aerogels for thermal protection, and puts forward suggestions for the future development of various aerogels. For oxide aerogels, it is necessary to further increase their use temperature and inhibit the sintering of high-temperature resistant components. For organic aerogels, it is necessary to focus on improving the anti-ablation, thermal insulation, and mechanical properties in long-term aerobic high-temperature environments, and on this basis, find cheap raw materials to reduce costs. For carbon aerogels, it is necessary to further explore the balanced relationship between oxidation resistance, mechanics, and thermal insulation properties of materials. The purpose of this paper is to provide a reference for the further development of more efficient and reliable aerogel materials for aerospace applications in the future.

Generation of novel Diels-Alder reactions using a generative adversarial network.

Deep learning has enormous potential in the chemical and pharmaceutical fields, and generative adversarial networks (GANs) in particular have exhibited remarkable performance in the field of molecular generation as generative models. However, their application in the field of organic chemistry has been limited; thus, in this study, we attempt to utilize a GAN as a generative model for the generation of Diels-Alder reactions. A MaskGAN model was trained with 14 092 Diels-Alder reactions, and 1441 novel Diels-Alder reactions were generated. Analysis of the generated reactions indicated that the model learned several reaction rules in-depth. Thus, the MaskGAN model can be used to generate organic reactions and aid chemists in the exploration of novel reactions.

Transformer-based multitask learning for reaction prediction under low-resource circumstances.

Recently, effective and rapid deep-learning methods for predicting chemical reactions have significantly aided the research and development of organic chemistry and drug discovery. Owing to the insufficiency of related chemical reaction data, computer-assisted predictions based on low-resource chemical datasets generally have low accuracy despite the exceptional ability of deep learning in retrosynthesis and synthesis. To address this issue, we introduce two types of multitask models: retro-forward reaction prediction transformer (RFRPT) and multiforward reaction prediction transformer (MFRPT). These models integrate multitask learning with the transformer model to predict low-resource reactions in forward reaction prediction and retrosynthesis. Our results demonstrate that introducing multitask learning significantly improves the average top-1 accuracy, and the RFRPT (76.9%) and MFRPT (79.8%) outperform the transformer baseline model (69.9%). These results also demonstrate that a multitask framework can capture sufficient chemical knowledge and effectively mitigate the impact of the deficiency of low-resource data in processing reaction prediction tasks. Both RFRPT and MFRPT methods significantly improve the predictive performance of transformer models, which are powerful methods for eliminating the restriction of limited training data.

Virtual data augmentation method for reaction prediction.

To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.e., in the data analysis strategy, the fake data as compounds with modified functional groups to increase the amount of data for reaction prediction. This approach was tested on five different reactions, and the results show improvements over other relevant techniques with increased model predictivity. Furthermore, we evaluated this method in different models, confirming the generality of virtual data augmentation. In summary, virtual data augmentation can be used as an effective measure to solve the problem of insufficient data and significantly improve the performance of reaction prediction.

From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.

Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration.

Self-Supervised Molecular Pretraining Strategy for Low-Resource Reaction Prediction Scenarios.

In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problems. Using a self-supervised pretraining strategy called MAsked Sequence to Sequence (MASS), the Transformer model can absorb the chemical information of about 1 billion molecules and then fine-tune on a small-scale reaction prediction. To further strengthen the predictive performance of our model, we combine MASS with the reaction transfer learning strategy. Here, we show that the average improved accuracies of the Transformer model can reach 14.07, 24.26, 40.31, and 57.69% in predicting the Baeyer-Villiger, Heck, C-C bond formation, and functional group interconversion reaction data sets, respectively, marking an important step to low-resource reaction prediction.

Reproducing the invention of a named reaction: zero-shot prediction of unseen chemical reactions.

While state-of-art models can predict reactions through the transfer learning of thousands of samples with the same reaction types as those of the reactions to predict, how to prepare such models to predict "unseen" reactions remains an unanswered question. We aimed to study the Transformer model's ability to predict "unseen" reactions through "zero-shot reaction prediction (ZSRP)", a concept derived from zero-shot learning and zero-shot translation. We reproduced the human invention of the Chan-Lam coupling reaction where the inventor was inspired by the Suzuki reaction when improving Barton's bismuth arylation reaction. After being fine-tuned with samples from these two "existing" reactions, the USPTO-trained Transformer could predict "unseen" Chan-Lam coupling reactions with 55.7% top-1 accuracy. Our model could also mimic the later stage of the history of this reaction, where the initial case of this reaction was generalized to more reactants and reagents via "one-shot/few-shot reaction prediction (OSRP/FSRP)" approaches.

Evaluation of L-Selenomethionine on Ameliorating Cardiac Injury Induced by Environmental Ammonia.

L-Selenomethionine is one of the important organic selenium sources. The supplementation of L-selenomethionine in diets is significant to improve the health of pigs. Ammonia is a major pollutant in the atmosphere and piggery, posing a threat to human and animal health. Although ammonia exposure can damage the heart, the mechanism of cardiac toxicity by ammonia is still unknown. In this study, we investigated the mechanism of cardiac injury induced by ammonia exposure in pigs and the protective effect of L-selenomethionine on its cardiotoxicity. The results showed that the blood ammonia content of pig increased significantly in ammonia group, the expressions of energy metabolism-related genes (LDHA, PDK4, HK2, and CPTIB) and the oxidative stress indexes were significantly changed (P < 0.05), the AMPK/PPAR-γ/NF-κB signaling pathways were activated, the chromatin edge aggregation and nuclear pyknosis were observed in ultrastructure, the apoptotic cells were significantly increased (P < 0.05), and the mRNA and protein expressions of apoptosis-related genes (Bcl-2, Bax, Cyt-c, caspase-3, and caspase-9) were significantly affected (P < 0.05). The above changes were significantly alleviated in ammonia + L-selenomethionine group, but there were still significant differences compared with the C group (P < 0.05). Our results indicated that ammonia exposure could cause energy metabolism disorder and oxidative stress and induce apoptosis of cardiomyocytes through AMPK/PPAR-γ/NF-κB pathways, which could lead to cardiac injury and affect cardiac function. L-Selenomethionine could effectively alleviate the cardiac damage caused by ammonia and antagonize the cardiotoxicity of ammonia.

AMPK/PPAR-γ/NF-κB axis participates in ROS-mediated apoptosis and autophagy caused by cadmium in pig liver.

The experiment was conducted to investigate the effects of Cadmium (Cd) on growth performance, blood biochemical parameters, oxidative stress, hepatocyte apoptosis and autophagy of weaned piglets. A total of 12 healthy weaned piglets were randomly assigned to the control and the Cd group, which were fed with a basal diet and the basal diet supplemented with 15 ± 0.242 mg/kg CdCl2 for 30 d, respectively. Our results demonstrated that Cd significantly decreased final body weight, average daily feed intake (ADFI), average daily gain (ADG) and increased feed-to-gain (F/G) ratio (P < 0.05). For blood biochemical parameters, Cd treatment significantly decreased the red blood cell (RBC), hemoglobin (HGB), hematocrit (HCT), total protein, albumin, copper content and iron content (P < 0.05). In addition, liver injury was observed in the Cd-exposed group. Our results also demonstrated that Cd exposure contributed to the production of ROS, activated the AMPK/PPAR-γ/NF-κB pathway (increasing the expressions of P-AMPK/AMPK, NF-κB, I-κB-ß, COX-2, and iNOS, decreasing the expressions of PPAR-γ and I-κB-α), finally induced autophagy (increasing the expressions of Beclin-1, the ratio of LC3-II/LC3-I and p62), and apoptosis (increasing the expressions of Bax, Bak, Caspase-9, and Caspase-3, decreasing the expression of Bcl-2). Overall, these findings revealed the vital role of AMPK/PPAR-γ/NF-κB pathway in Cd-induced liver apoptosis and autophagy, which provided deeper insights into a better understanding of Cd-induced hepatotoxicity.

Migratory Take-Off Behaviour of the Mongolian Grasshopper Oedaleus asiaticus.

Oedaleus asiaticus is one of the dominant species of grasshoppers in the rangeland on the Mongolian plateau, and a serious pest, but its migratory behavior is poorly known. We investigated the take-off behavior of migratory O. asiaticus in field cages in the inner Mongolia region of northern China. The species shows a degree of density-dependent phase polyphenism, with high-density swarming populations characterized by a brown morph, while low-density populations are more likely to comprise a green morph. We found that only 12.4% of brown morphs engaged in migratory take-off, and 2.0% of green morphs. Migratory grasshoppers took off at dusk, especially in the half hour after sunset (20:00-20:30 h). Most emigrating individuals did not have any food in their digestive tract, and the females were mated but with immature ovaries. In contrast, non-emigrating individuals rarely had empty digestive tracts, and most females were mated and sexually mature. Therefore, it seems clear that individuals prepare for migration in the afternoon by eliminating food residue from the body, and migration is largely restricted to sexually immature stages (at least in females). Furthermore, it was found that weather conditions (particularly temperature and wind speed at 15:00 h) in the afternoon had a significant effect on take-off that evening, with O. asiaticus preferring to take off in warm, dry and calm weather. The findings of this study will contribute to a reliable basis for forecasting migratory movements of this pest.

Chiral Amino Alcohol Accelerated and Stereocontrolled Allylboration of Iminoisatins: Highly Efficient Construction of Adjacent Quaternary Stereogenic Centers.

We have developed a highly efficient asymmetric allylboration of ketimines with nonchiral γ,γ-disubstituted allylboronic acids by using a chiral amino alcohol as the directing group, which is otherwise challenging. The amino alcohol not only serves as a cheap source of nitrogen and chirality, but also dramatically enhances the reactivity. The versatility of this method was demonstrated by its ability to access all four stereoisomers with adjacent quaternary carbon centers. A reaction model was proposed to explain the diastereoselectivity and the rate-accelerating effect.

Synergistic effects of suberoylanilide hydroxamic acid combined with cisplatin causing cell cycle arrest independent apoptosis in platinum-resistant ovarian cancer cells.

Histone deacetylase inhibitors (HDACIs) belong to an emerging class of anticancer compounds. It is increasingly recognized that their unique and complementary mode of action make HDACIs valuable agents in augmenting the cytotoxicity of conventional chemotherapeutics. We examined the potential for combined use of an approved HDACI, suberoylanilide hydroxamic acid (SAHA), with cisplatin (Cddp) in platinum-resistant ovarian cancer cells, OVCAR-3 and SKOV-3. The nature of the drug interaction following combinatory therapy was assessed using median effect analysis. We found that SAHA acted synergistically with Cddp over a wide range of concentrations in both cell types, resulting in favorable dose reductions of both compounds. In particular, in the more Cddp-resistant SKOV-3 cells, more than 8-fold dose reduction of Cddp was achieved with the simultaneous use of SAHA and Cddp as compared to the dose required to elicit similar cell kill effects using Cddp alone. More importantly, therapeutic selectivity for ovarian cancer cells over normal fibroblast cells were maintained with the combinatorial therapy. We further observed that the augmentation of Cddp-induced cell death was mediated by the net increase in apoptosis and was independent of cell cycle arrest. Overall, concurrent application of SAHA and Cddp yielded the most favorable cell kill, indicating that this combination is promising for treatment of platinum-resistant ovarian tumors.