iASMP: An interpretable in-silico predictive tool focusing on species-specific antimicrobial peptides.

Antimicrobial peptides (AMPs), a crucial part of the innate immune system, have been exploited as promising candidates for antibacterial agents. Many researchers have been devoting their efforts to develop novel AMPs in recent decades. In this term, many computational approaches have been developed to identify potential AMPs accurately. However, finding peptides specific to a particular bacterial species is challenging. Streptococcus mutans is a pathogen with an apparent cariogenic effect, and it is of great significance to study AMP that inhibit S. mutans for the prevention and treatment of caries. In this study, we proposed a sequence-based machine learning model, namely iASMP, to exactly identify potential anti-S. mutans peptides (ASMPs). After collecting ASMPs, the performances of models were compared by utilizing multiple feature descriptors and different classification algorithms. Among the baseline predictors, the model integrating the extra trees (ET) algorithm and the hybrid features exhibited optimal results. The feature selection method was utilized to remove redundant feature information to improve the model performance further. Finally, the proposed model achieved the maximum accuracy (ACC) of 0.962 on the training dataset and performed on the testing dataset with an ACC of 0.750. The results demonstrated that iASMP had an excellent predictive performance and was suitable for identifying potential ASMP. Furthermore, we also visualized the selected features and rationally explained the impact of individual features on the model output.

GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences.

Although peptides are regarded as ideal therapeutic agents, only a small proportion of the marketed drugs are peptides. In the past decade, pharmacists have paid great attention to the development of peptide therapeutics. Except a few approved chemically/rationally designed peptides, most attempts failed due to unsatisfactory efficacy or safety. Luckily, computation methods, such as artificial intelligence, have been utilized to accelerate the discovery of therapeutic peptides by predicting the activity, toxicity, and absorption, distribution, metabolism, and excretion of polypeptides. Usually, a specific biological activity of a peptide could be accurately determined by an interest-oriented binary classification constructed of a positive set and another un-experimentally validated negative set regardless of other characteristics, which suggests that it could be challenging to realize the comprehensive evaluation of the research object in the early stage of drug research and development. Herein, we proposed an integrated method (GM-Pep) that contained a conditional variational autoencoder model (CVAE) and a positive sample training multiclassifier (Deep-Multiclassifier) to effectively generate a single bioactive peptide sequence without toxicity and referential side effects. The results showed that our Deep-Multiclassifier model gave a sequence accuracy of up to 96.41% [toxicity (94.48%), antifungal (96.58%), antihypertensive (97.18%), and antibacterial (96.91%), respectively]. The properties of Deep-Multiclassifier and CVAE were validated through 12 first synthesized antibacterial peptides or compared to random peptides. The source code and data sets are available at https://github.com/TimothyChen225/GM-Pep.

Transient mechanical interactions between cells and viscoelastic extracellular matrix.

During various physiological processes, such as wound healing and cell migration, cells continuously interact mechanically with a surrounding extracellular matrix (ECM). Contractile forces generated by the actin cytoskeleton are transmitted to a surrounding ECM, resulting in structural remodeling of the ECM. To better understand how matrix remodeling takes place, a myriad of in vitro experiments and simulations have been performed during recent decades. However, physiological ECMs are viscoelastic, exhibiting stress relaxation or creep over time. The time-dependent nature of matrix remodeling induced by cells remains poorly understood. Here, we employed a discrete model to investigate how the viscoelastic nature of ECMs affects matrix remodeling and stress profiles. In particular, we used explicit transient cross-linkers with varied density and unbinding kinetics to capture viscoelasticity unlike most of the previous models. Using this model, we quantified the time evolution of generation, propagation, and relaxation of stresses induced by a contracting cell in an ECM. It was found that matrix connectivity, regulated by fiber concentration and cross-linking density, significantly affects the magnitude and propagation of stress and subsequent matrix remodeling, as characterized by fiber displacements and local net deformation. In addition, we demonstrated how the base rate and force sensitivity of cross-linker unbinding regulate stress profiles and matrix remodeling. We verified simulation results using in vitro experiments performed with fibroblasts encapsulated in a three-dimensional collagen matrix. Our study provides key insights into the dynamics of physiologically relevant mechanical interactions between cells and a viscoelastic ECM.

Enhanced Interfacial Kinetics of Carbon Monolith Boosting Ultrafast Na-Storage.

Slow ion kinetics of negative electrode materials is the main factor of limiting fast charge and discharge of batteries. Sluggish Na+ kinetics property leads to large electrode polarization, resulting in poor rate and cyclic performances. Herein, an electrode of ultrasmall tin nanoparticles decorated in N, S codoped carbon monolith (TCM) with exceptional high-rate capability and ultrastable cycling behavior for Na-storage is reported. The resulted TCM electrode exhibits an extremely high retention of 96% initial charge capacity after 500 cycles at a current density of 500 mA g-1 . Significantly, when the current density is elevated to an ultrahigh rate of 5000 mA g-1 , a high reversible capacity of 228 mAh g-1 after the 2000th cycle is still maintained. More importantly, the stable and fast Na-storage of TCM is investigated and understood by experimental characterizations and kinetics calculations, including interfacial ion/electron transport behavior, ion diffusion, and quantitative pseudocapacitive analysis. These investigations elucidate that the TCM shows improved ion/electron conductivity and enhanced interfacial kinetics. An entirely new perspective to deep insights into the fast ion/electron transport mechanisms revealed by interfacial kinetics of sodiation/desodiation, which contributes to the profound understanding for developing fast charging/discharging and long-term stable electrodes in sodium-ion batteries, is provided.

Towards sustainable upcycling of side streams of purple bread wheat using dry fractionation: Enhancing bioactive compounds and reducing harmful elements.

Side streams from milling result in significant food wastage. While highly nutritious, their harmful elements raise concerns. To repurpose these side streams safely, this study designed a dry fractionation technique for anthocyanin-rich purple bread wheat. Four fractions - from inner to outer layers: flour, middlings, shorts and bran - alongside whole-wheat flour were obtained and examined by microstructure, antioxidant activity, anthocyanin profiles, and essential and harmful minerals. Across the four investigated cultivars, both anthocyanin content and antioxidant capacity increased from inner to outer layers. In comparison to flour, cyanidin-3-glucoside concentrations in middlings, shorts and bran were 2-5 times, 3-9 times, and 6-19 times, respectively. Concentrations of Cr, Ni, Sr and Ba progressively increased from inner to outer layers, Pb and Se exhibited uniform distribution, while Al was more concentrated in inner layers. These findings indicate that the fractionation technique is effective in deriving valuable ingredients from underexploited side streams, especially bran.

Value function assessment to different RL algorithms for heparin treatment policy of patients with sepsis in ICU.

Heparin is a critical aspect of managing sepsis after abdominal surgery, which can improve microcirculation, protect organ function, and reduce mortality. However, there is no clinical evidence to support decision-making for heparin dosage. This paper proposes a model called SOFA-MDP, which utilizes SOFA scores as states of MDP, to investigate clinic policies. Different algorithms provide different value functions, making it challenging to determine which value function is more reliable. Due to ethical restrictions, we cannot test all policies on patients. To address this issue, we proposed two value function assessment methods: action similarity rate and relative gain. We experimented with heparin treatment policies for sepsis patients after abdominal surgery using MIMIC-IV. In the experiments, TD(0) shows the most reliable performance. Using the action similarity rate and relative gain to assess AI policy from TD(0), the agreement rates between AI policy and "good" physician's actual treatment are 64.6% and 73.2%, while the agreement rates between AI policy and "bad" physician's actual treatment are 44.1% and 35.8%, the gaps are 20.5% and 37.4%, respectively. External validation using action similarity rate and relative gain based on eICU resulted in agreement rates of 61.5% and 69.1% with the "good" physician's treatment, and 45.2% and 38.3% with the "bad" physician's treatment, with gaps of 16.3% and 30.8%, respectively. In conclusion, the model provides instructive support for clinical decisions, and the evaluation methods accurately distinguish reliable and unreasonable outcomes.

Ultra-High Pyridinic N-Doped Porous Carbon Monolith Enabling High-Capacity K-Ion Battery Anodes for Both Half-Cell and Full-Cell Applications.

An ultrahigh pyridinic N-content-doped porous carbon monolith is reported, and the content of pyridinic N reaches up to 10.1% in overall material (53.4 ± 0.9% out of 18.9 ± 0.4% N content), being higher than most of previously reported N-doping carbonaceous materials, which exhibit greatly improved electrochemical performance for potassium storage, especially in term of the high reversible capacity. Remarkably, the pyridinic N-doped porous carbon monolith (PNCM) electrode exhibits high initial charge capacity of 487 mAh g-1 at a current density of 20 mA g-1 , which is one of the highest reversible capacities among all carbonaceous anodes for K-ion batteries. Moreover, the K-ion full cell is successfully assembled, demonstrating a high practical energy density of 153.5 Wh kg-1 . These results make PNCM promising for practical application in energy storage devices and encourage more investigations on a similar potassium storage system.