Atom-ProteinQA: Atom-level protein model quality assessment through fine-grained joint learning.

MOTIVATION: Protein model quality assessment (ProteinQA) is a fundamental task that is essential for biologically relevant applications, i.e., protein structure refinement, protein design, etc. Previous works aimed to conduct ProteinQA only on the global structure or per-residue level, ignoring potentially usable and precise cues from a fine-grained per-atom perspective. In this study, we propose an atom-level ProteinQA model, named Atom-ProteinQA, in which two innovative modules are designed to extract geometric and topological atom-level relationships respectively. Specifically, on the one hand, a geometric perception module exploits 3D sparse convolution to capture the geometric features of the input protein, generating fine-grained atom-level predictions. On the other hand, natural chemical bonds are utilized to construct an atom-level graph, then message passing from a topological perception module is applied to output residue-level predictions in parallel. Eventually, through a cross-model aggregation module, features from different modules mutually interact, enhancing performance on both the atom and residue levels. RESULTS: Extensive experiments show that our proposed Atom-ProteinQA outperforms previous methods by a large margin, regardless of residue-level or atom-level assessment. Concretely, we achieved state-of-the-art performance on CATH-2084, Decoy-8000, public benchmarks CASP13 & CASP14, and the CAMEO. AVAILABILITY: The repository of this project is released on: https://github.com/luyfcandy/Atom_ProteinQA.

The Compatibility of COFs Cathode and Optimized Electrolyte for Ultra-Long Lifetime Rechargeable Aqueous Zinc-Ion Battery.

Rechargeable aqueous zinc-ion batteries (RAZIBs) are attractive due to their affordability, safety, and eco-friendliness. However, their potential is limited by the lack of high-capacity cathodes and compatible electrolytes needed for reliable performance. Herein, we have presented a compatibility strategy for the development of a durable and long-lasting RAZIBs. The covalent organic frameworks (COFs) based on anthraquinone (DAAQ-COF) is created and utilized as the cathode, with zinc metal serving as the anode. The electrolyte is made up of an aqueous solution containing zinc salts at various concentrations. The COF cathode has been designed to be endowed with a rich array of redox-active groups, enhancing its electrochemical properties. Meanwhile, the electrolyte is formulated using triflate anions, which have exhibited superiority over sulfate anions. This strategy lead to the development of an optimized COF cathode with fast charging capability, high Coulombic efficiency (nearly 100 %) and long-term cyclability (retention rate of nearly 100 % at 1 A g-1 after 10000 cycles). Moreover, through experimental analysis, a co-insertion mechanism involving Zn2+ and H+ in this cathode is discovered for the first time. These findings represent a promising path for the advancement of organic cathode materials in high-performance and sustainable RAZIBs.

Generation of Nano-Bubbles by NaHCO3 for Improving the FO Membrane Performance.

Thin-film composite (TFC) polyamide membranes have a wide range of applications in forward osmosis, but tuning the water flux remains a significant challenge due to concentration polarization. The generation of nano-sized voids within the polyamide rejection layer can change the roughness of the membrane. In this experiment, the micro-nano structure of the PA rejection layer was adjusted by adding sodium bicarbonate to the aqueous phase to generate nano-bubbles, and the changes of its roughness with the addition of sodium bicarbonate were systematically demonstrated. With the enhanced nano-bubbles, more and more blade-like and band-like features appeared on the PA layer, which could effectively reduce the reverse solute flux of the PA layer and improve the salt rejection of the FO membrane. The increase in roughness raised the area of the membrane surface, which led to a larger area for concentration polarization and reduced the water flux. This experiment demonstrated the variation of roughness and water flux, providing an effective idea for the preparation of high-performance FO membranes.

Cobalt mediated perovskite as efficient Fenton-like catalysts for the tetracycline removal over a neutral condition: The importance of superoxide radical.

Cobalt mediated perovskite oxides (Ca-Fe-Co-x) were prepared for heterogeneous Fenton-like, which exhibited excellent tetracycline (TC) degradation efficiency and wider pH suitability (3-11). Experimental results showed that Ca-Fe-Co-1.0 sample displayed the highest degradation rate could reach 80.5% under neutral conditions, and maintain at around 80% after four cycles. The analysis of degradation mechanism showed that the redox of Fe2+/Fe3+ and Co2+/Co3+ significant enhanced the activation of H2O2 to superoxide radical (∙O2-). Meanwhile, the hydroxyl radical (∙OH) was also detected by ESR analysis. In addition, the possible degradation pathway and mechanism of TC were deduced via UPLC-QTOF/MS analysis and density functional theory (DFT) calculations. The toxicity of TC and its intermediates were also evaluated by the ECOSAR software. The Ca-Fe-Co-1.0/nanocellulose aerogel (NCA) displayed highly removal efficiency of TC wastewater in the long-term operation conduction. This study provided a feasible method to design and synthesis heterogeneous Fenton-like catalysts for antibiotic degradation.

Extremely Soft, Stretchable, and Self-Adhesive Silicone Conductive Elastomer Composites Enabled by a Molecular Lubricating Effect.

Softness, adhesion, stretchability, and fast recovery from large deformations are essential properties for conductive elastomers that play an important role in the development of high-performance soft electronics. However, it remains an ongoing challenge to obtain conductive elastomers that combine these properties. We have fabricated a super soft (Young's modulus 2.3-12 kPa), highly stretchable (up to 1500% strain), and underwater adhesive silicone conductive elastomer composite (SF-C-PDMS) by incorporating dimethyl silicone oil as a lubricating agent in a cross-linked molecular network. The resultant SF-C-PDMS not only exhibits superior softness but also can readily recover after a strain of 1000%. The initial resistance only decreases by 8% after 100000 cycles of tensile fatigue test (100% strain, 0.5 Hz, 15 mm/s). This multifunctional silicone conductive elastomer composite is obtained in a one-step preparation at room temperature using commercially available materials. Moreover, we illustrate the capabilities of this composite in motion sensing.

Prior knowledge facilitates low homologous protein secondary structure prediction with DSM distillation.

MOTIVATION: Protein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignment (MSA). Unfortunately, many proteins lack homologous sequences, which results in the low quality of MSA and poor performance. In this article, we propose the novel dynamic scoring matrix (DSM)-Distil to tackle this issue, which takes advantage of the pretrained BERT and exploits the knowledge distillation on the newly designed DSM features. Specifically, we propose the DSM to replace the widely used profile and PSSM (position-specific scoring matrix) features. DSM could automatically dig for the suitable feature for each residue, based on the original profile. Namely, DSM-Distil not only could adapt to the low homologous proteins but also is compatible with high homologous ones. Thanks to the dynamic property, DSM could adapt to the input data much better and achieve higher performance. Moreover, to compensate for low-quality MSA, we propose to generate the pseudo-DSM from a pretrained BERT model and aggregate it with the original DSM by adaptive residue-wise fusion, which helps to build richer and more complete input features. In addition, we propose to supervise the learning of low-quality DSM features using high-quality ones. To achieve this, a novel teacher-student model is designed to distill the knowledge from proteins with high homologous sequences to that of low ones. Combining all the proposed methods, our model achieves the new state-of-the-art performance for low homologous proteins. RESULTS: Compared with the previous state-of-the-art method 'Bagging', DSM-Distil achieves an improvement about 5% and 7.3% improvement for proteins with MSA count ≤30 and extremely low homologous cases, respectively. We also compare DSM-Distil with Alphafold2 which is a state-of-the-art framework for protein structure prediction. DSM-Distil outperforms Alphafold2 by 4.1% on extremely low-quality MSA on 8-state secondary structure prediction. Moreover, we release a large-scale up-to-date test dataset BC40 for low-quality MSA structure prediction evaluation. AVAILABILITY AND IMPLEMENTATION: BC40 dataset: https://drive.google.com/drive/folders/15vwRoOjAkhhwfjDk6-YoKGf4JzZXIMC. HardCase dataset: https://drive.google.com/drive/folders/1BvduOr2b7cObUHy6GuEWk-aUkKJgzTUv. Code: https://github.com/qinwang-ai/DSM-Distil.

Compact, fiber-coupled, single-ended laser-absorption-spectroscopy sensors for high-temperature environments.

The design and demonstration of a compact single-ended laser-absorption-spectroscopy sensor for measuring temperature and H2O in high-temperature combustion gases is presented. The primary novelty of this work lies in the design, demonstration, and evaluation of a sensor architecture that uses a single lens to provide single-ended, alignment-free (after initial assembly) measurements of gas properties in a combustor without windows. We demonstrate that the sensor is capable of sustaining operation at temperatures up to at least 625 K and is capable of withstanding direct exposure to high-temperature (≈1000 K) flame gases for long durations (at least 30 min) without compromising measurement quality. The sensor employs a fiber bundle and a 6 mm diameter antireflection-coated lens mounted in a 1/8'' NPT-threaded stainless-steel body to collect laser light that is backscattered off native surfaces. Distributed-feedback tunable diode lasers (TDLs) with a wavelength near 1392 nm and 1343 nm were used to interrogate well-characterized H2O absorption transitions using wavelength-modulation-spectroscopy techniques. The sensor was demonstrated with measurements of gas temperature and H2O mole fraction in a propane-air burner with a measurement bandwidth up to 25 kHz. In addition, this work presents an improved wavelength-modulation spectroscopy spectral-fitting technique that reduces computational time by a factor of 100 compared to previously developed techniques.