Nanoscale Insights into the Interaction Mechanism Underlying the Adsorption and Retention of Heavy Metal Ions by Humic Acid.

The mobility and distribution of heavy metal ions (HMs) in aquatic environments are significantly influenced by humic acid (HA), which is ubiquitous. A quantitative understanding of the interaction mechanism underlying the adsorption and retention of HMs by HA is of vital significance but remains elusive. Herein, the interaction mechanism between HA and different types of HMs (i.e., Cd(II), Pb(II), arsenate, and chromate) was quantitatively investigated at the nanoscale. Based on quartz crystal microbalance with dissipation tests, the adsorption capacities of Pb(II), Cd(II), As(V), and Cr(VI) ionic species on the HA surface were measured as ∼0.40, ∼0.25, ∼0.12, and ∼0.02 nmol cm-2, respectively. Atomic force microscopy force results showed that the presence of Pb(II)/Cd(II) cations suppressed the electrostatic double-layer repulsion during the approach of two HA surfaces and the adhesion energy during separation was considerably enhanced from ∼2.18 to ∼5.05/∼4.18 mJ m-2. Such strong adhesion stems from the synergistic metal-HA complexation and cation-π interaction, as evidenced by spectroscopic analysis and theoretical simulation. In contrast, As(V)/Cr(VI) oxo-anions could form only weak hydrogen bonds with HA, resulting in similar adhesion energies for HA-HA (∼2.18 mJ m-2) and HA-As(V)/Cr(VI)-HA systems (∼2.26/∼1.96 mJ m-2). This work provides nanoscale insights into quantitative HM-HA interactions, improving the understanding of HMs biogeochemical cycling.

Complex dynamics in nonlinear small time-delayed optoelectronic oscillator and application in fast reservoir computing and pulse generation.

We investigated a time-delayed optoelectronic oscillator (OEO) that displays a wide range of complex dynamic behavior under small time delay. The phase-space trajectory distributions in different dynamic regimes were compared which brings a new perspective on the underlying mechanism of the transition process. It was found that bifurcation is always possible no matter how small the time delay is even if the universal adiabatic approximation model is invalid. Hereby we proposed a versatile simple oscillator which has a potential capacity as memory carrier and high-dimensional state spatial mapping ability that brings 1000 times computing-efficiency improvements of reservoir computing over the large time delay one. Furthermore, we demonstrated a new approach for a tunable optoelectronic pulse generator (repetition rate at 0.2 MHz and 0.25 GHz) which depends critically on time-delayed input electrical pulse. The proposed oscillator is also a promising system for the applications of fast chaos-based communication.

Molecular interaction mechanism for humic acids fouling resistance on charged, zwitterion-like and zwitterionic surfaces.

Humic acids (HA) are ubiquitous in surface waters, leading to significant fouling challenges. While zwitterion-like and zwitterionic surfaces have emerged as promising candidates for antifouling, a quantitative understanding of molecular interaction mechanism, particularly at the nanoscale, still remains elusive. In this work, the intermolecular forces between HA and charged, zwitterion-like or zwitterionic monolayers in aqueous environments were quantified using atomic force microscope. Compared to cationic MTAC ([2-(methacryloyloxy)ethyl]trimethylammonium chloride), which exhibited an adhesion energy of âˆ¼1.342 mJ/m2 with HA due to the synergistic effect of electrostatic attraction and possible cation-π interaction, anionic SPMA (3-sulfopropyl methacrylate) showed a weaker adhesion energy (∼0.258 mJ/m2) attributed to the electrostatic repulsion. Zwitterion-like MTAC/SPMA mixture, driven by electrostatic attraction between opposite charges, formed a hydration layer that prevented the interaction with HA, thereby considerably reducing adhesion energy to âˆ¼0.123 mJ/m2. In contrast, zwitterionic MPC (2-methacryloyloxyethyl phosphorylcholine) and DMAPS ([2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) ammonium hydroxide) displayed ultralow adhesion energy (0.06-0.07 mJ/m2) with HA, arising from their strong dipole moments which could induce a tight hydration layer that effectively inhibited HA fouling. The pH-mediated electrostatic interaction resulted in the increased adhesion energy for MTAC but decreased adhesion energy for SPMA with elevated pH, while the adhesion energy for zwitterion-like and zwitterionic surfaces was independent of environmental pH. Density functional theory (DFT) simulation confirmed the strong binding capability of MPC and DMAPS with water molecules (∼-12 kcal mol-1). This work provides valuable insights into the molecular interaction mechanisms underlying humic-substance-fouling resistance of charged, zwitterion-like and zwitterionic materials at the nanoscale, shedding light on developing more effective strategy for HA antifouling in water treatment.

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention.

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

BACKGROUND: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients. OBJECTIVE: To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL). METHODS: Six models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses. RESULTS: Our study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST. CONCLUSIONS: Our results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.