Anthocyanins from Lycium ruthenicum Murray Mitigate Cadmium-Induced Oxidative Stress and Testicular Toxicity by Activating the Keap1/Nrf2 Signaling Pathway.

Cadmium (Cd) is a hazardous heavy metal environmental pollutant that has carcinogenic, teratogenic, and mutagenic properties. Excessive exposure to Cd can induce oxidative stress, which greatly harms the male reproductive system. Anthocyanins have remarkable antioxidative, anti-inflammatory, and anti-stress properties. In this study, we investigated the effects of anthocyanins and the underlying mechanisms through which anthocyanins mitigate Cd-induced reproductive damage. We isolated and purified Lycium ruthenicum Murray anthocyanin extract (LAE) and performed UHPLC-MS/MS to identify 30 different anthocyanins. We established an ICR mouse Cd injury model by administering 5 mg/kg/day CdCl2 for 28 consecutive days. LAE at 500 mg/kg/day effectively ameliorated testicular damage and preserved spermatogenesis. The mice in the LAE-treated group had elevated testosterone and inhibin B levels. Additionally, the treatment restored the activity of antioxidant enzymes, including T-SOD, CAT, and GR, and substantially increased the levels of the non-enzymatic antioxidant GSH. Research findings indicate that LAE can activate the SIRT1/Nrf2/Keap1 antioxidant pathway. This activation is achieved through the upregulation of both the SIRT1 gene and protein levels, leading to the deacetylation of Nrf2. Moreover, LAE reduces the expression of Keap1, alleviating its inhibitory effect on Nrf2. This, in turn, facilitates the uncoupling process, promoting the translocation of Nrf2 to the nucleus, where it governs downstream expression, including that of HO-1 and GPX1. LAE effectively mitigated toxicity to the reproductive system associated with exposure to the heavy metal Cd by alleviating oxidative stress in the testes.

Optoelectronic Torque Measurement System Based on SAPSO-RBF Algorithm.

The torque is a significant indicator reflecting the comprehensive operational characteristics of a power system. Thus, accurate torque measurement plays a pivotal role in ensuring the safety and stability of the system. However, conventional torque measurement systems predominantly rely on strain gauges adhered to the shaft, often leading to reduced accuracy, poor repeatability, and non-traceability due to the influence of strain gauge adhesion. To tackle the challenge, this paper introduces a photoelectric torque measurement system. Quadrants of photoelectric sensors are employed to capture minute deformations induced by torque on the rotational axis, converting them into measurable voltage. Subsequently, the system employs the radial basis function neural network optimized by simulated annealing combined with particle swarm algorithm (SAPSO-RBF) to establish a correlation between measured torque values and standard references, thereby calibrating the measured values. Experimental results affirm the system's capability to accurately determine torque measurements and execute calibration, minimizing measurement errors to 0.92%.

Quantitative Analysis of Morphology and Function in the Fetal Heart with Severe Tricuspid Regurgitation by Speckle Tracking Imaging.

Morphology and function in a fetal heart with severe tricuspid regurgitation remains challenging. The aim of this study was to assess cardiac morphology and function in fetuses with severe tricuspid regurgitation by fetal heart quantification (HQ) and to assess the practical value of fetal HQ. Clinical information was analyzed for 63 pregnant women who underwent fetal cardiac ultrasonography. The women were divided into those who had a fetus with severe tricuspid regurgitation (n = 20) and those with a normal fetus (n = 40). The global sphericity index (GSI), fractional area change (FAC), and global longitudinal strain (GLS) of both ventricles and the sphericity index (SI) and fractional shortening (FS) of 24 segments were quantified by fetal HQ using speckle tracking imaging. Fetuses with severe tricuspid regurgitation had a significantly lower GSI (1.14 ± 0.10 vs. 1.26 ± 0.08, p < 0.001) and a higher GSI Z-score (-0.98 ± 1.01 vs. 0.25 ± 0.87, p < 0.001) as well as a significantly lower right ventricular FAC (36.50 ± 7.34% vs. 45.19 ± 3.39%, p < 0.001), FAC Z-score (-1.02 ± 1.41 vs. 0.49 ± 0.74, p < 0.001), and GLS (-21.01 ± 5.66% vs. 45.19 ± 3.49%, p < 0.001). The SI and SI Z-score were significantly lower in segments 1-18 of the right ventricle in fetuses with severe tricuspid regurgitation (p < 0.05); furthermore, FS of segments 1-12 and 19-24 and the FS Z-score of segments 18-24 were significantly lower in fetuses with severe tricuspid regurgitation (p < 0.05). Fetal HQ is useful for evaluation of cardiac morphology and function in fetuses with severe tricuspid regurgitation and can provide important reference information for both clinical diagnosis and treatment.

Dynamic Antimicrobial Poly(disulfide) Coatings Exfoliate Biofilms On Demand Via Triggered Depolymerization.

Bacterial biofilms are notoriously problematic in applications ranging from biomedical implants to ship hulls. Cationic, amphiphilic antibacterial surface coatings delay the onset of biofilm formation by killing microbes on contact, but they lose effectiveness over time due to non-specific binding of biomass and biofilm formation. Harsh treatment methods are required to forcibly expel the biomass and regenerate a clean surface. Here, a simple, dynamically reversible method of polymer surface coating that enables both chemical killing on contact, and on-demand mechanical delamination of surface-bound biofilms, by triggered depolymerization of the underlying antimicrobial coating layer, is developed. Antimicrobial polymer derivatives based on α-lipoic acid (LA) undergo dynamic and reversible polymerization into polydisulfides functionalized with biocidal quaternary ammonium salt groups. These coatings kill >99.9% of Staphylococcus aureus cells, repeatedly for 15 cycles without loss of activity, for moderate microbial challenges (≈105 colony-forming units (CFU) mL-1, 1 h), but they ultimately foul under intense challenges (≈107 CFU mL-1, 5 days). The attached biofilms are then exfoliated from the polymer surface by UV-triggered degradation in an aqueous solution at neutral pH. This work provides a simple strategy for antimicrobial coatings that can kill bacteria on contact for extended timescales, followed by triggered biofilm removal under mild conditions.

Investigation of the Potential Mechanism of Compound Dragon's Blood Capsule against Myocardial IschemiaBased on Network Pharmacology.

BACKGROUND: Dragon's blood is widely consumed in China, Vietnam and Laos to promote blood circulation. A Compound Dragon's blood capsule (CDC) is a patented medicine composed of dragon's blood, notoginseng, and borneol. This combination is purported to stabilize coronary heart disease and myocardial ischemia. However, the possible mechanisms and the characterization of its drug targets' relevance at the systemic level remain unclear. AIM: The present study aims to reveal the potential mechanisms of CDC's anti-myocardial ischemia effect. MATERIALS AND METHODS: The potential mechanisms were investigated by network pharmacology and qRT-PCR was used to verify the expression levels of key genes of PI3k-Akt pathway. RESULTS: S1PR2 and AGTR1 were the common targets, which involved 6 biological processes annotated by KEGG and GO analysis. The qRT-PCR results showed a remarkable increase in the expression of Pi3k, Pdk1, Akt, Mdm2, Bcl2, and mTOR. Results also showed a decline in the expression of P53 and Casp3 after CDC intervention. CONCLUSION: CDC has a significant anti-myocardial ischemia effect through the PI3k/Akt pathway, which demonstrates that CDC is a suitable adjuvant to treat CHD and provides a theoretical basis for its further clinical application.

Glycyrrhetinic Acid Receptor-Mediated Zeolitic Imidazolate Framework-8 Loaded Doxorubicin as a Nanotherapeutic System for Liver Cancer Treatment.

In this study, we designed and developed a DOX nanodrug delivery system (PEG-GA@ZIF-8@DOX) using ZIF-8 as the carrier and glycyrrhetinic acid (GA) as the targeting ligand. We confirmed that DOX was loaded and PEG-GA was successfully modified on the surface of the nanoparticles. The in vitro release profile of the system was investigated at pH 5.0 and 7.4. The cellular uptake, in vitro cytotoxicity, and lysosomal escape characteristics were examined using HepG2 cells. We established an H22 tumor-bearing mouse model and evaluated the in vivo antitumor activity. The results showed that the system had a uniform nanomorphology. The drug loading capacity was 11.22 ± 0.87%. In acidic conditions (pH 5.0), the final release rate of DOX was 57.73%, while at pH 7.4, it was 25.12%. GA-mediated targeting facilitated the uptake of DOX by the HepG2 cells. PEG-GA@ZIF-8@DOX could escape from the lysosomes and release the drug in the cytoplasm, thus exerting its antitumor effect. When the in vivo efficacy was analyzed, we found that the tumor inhibition rate of PEG-GA@ZIF-8@DOX was 67.64%; it also alleviated the loss of the body weight of the treated mice. This drug delivery system significantly enhanced the antitumor effect of doxorubicin in vitro and in vivo, while mitigating its toxic side effects.

Prognostic Role of Mitochondrial Transcription Termination Factor 3 in Thyroid Carcinoma.

Background: Studies have shown that the Mitochondrial Transcription Termination Factor 3 (MTERF3) negatively regulates mitochondrial gene expression and energy metabolism, and plays a significant role in many cancer types. Nevertheless, the expression and prognostic role of MTERF3 in patients with thyroid carcinoma (THCA) is still unclear. Thus, we investigated the expression, clinicopathological significance, and prognostic value of MTERF3 in THCA. Methods: The protein and mRNA expression levels of MTERF3 were, respectively, analyzed using immunohistochemistry (IHC) from THCA tissues and RNA-Seq data downloaded from The Cancer Genome Atlas. In addition, the relationships among the expression of MTERF3, the stemness feature, the extent of immune infiltration, drug sensitivity, the expression of ferroptosis, and N6-methyladenosine (m6A) methylation regulators, were evaluated as prognostic indicators for patients with THCA using the Kaplan-Meier plotter database. Results: The IHC and RNAseq results showed that the protein and mRNA expression levels of MTERF3 in adjacent nontumor tissues were significantly higher than in THCA tissues. The survival analysis indicated that decreased expression of MTERF3 was associated with a poorer prognosis. Furthermore, the expression of MTERF3 not only negatively correlated with the enhancement of the stemness of THCA and the reduction of drug sensitivity but also was implicated in ferroptosis and m6A methylation. Conclusion: The data from this study support the hypothesis that decreased expression of MTERF3 in THCA is associated with a poor prognosis.

Monovalent Omicron COVID-19 vaccine triggers superior neutralizing antibody responses against Omicron subvariants than Delta and Omicron bivalent vaccine.

The continuous evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses a challenge to determine the optimal updated composition of the coronavirus disease 2019 (COVID-19) vaccine. The present study aimed to investigate the immunogenicity of the Delta monovalent vaccine, the Omicron monovalent vaccine, and the Delta and Omicron BA.1 bivalent vaccine. Three COVID-19 vaccines were designed using the heterologous DNA prime-protein boost strategy, with each vaccine containing either Delta receptor-binding domain (RBD) of the spike protein, Omicron RBD, or both Delta and Omicron antigens. Temporal serum antibody binding titers and neutralizing antibody titers induced by the three vaccines in New Zealand White rabbits were analyzed. To further dissect the vaccine elicited antibodies (mAb) responses at the molecular level, a panel of rabbit monoclonal antibodies (RmAbs) was generated by a high-throughput single B cell sorting and discovery pipeline and further comprehensively characterized. The Omicron monovalent vaccine induced higher antibody binding titers and neutralization activities than the Delta and Omicron bivalent vaccine. Four RmAbs with robust neutralization capacity were isolated from rabbits immunized with the Omicron or Delta monovalent vaccine. Notably, 9E11 isolated from the Omicron monovalent vaccine group neutralized all the Omicron subvariants with an IC50 value ranging from 1.5 to 503.6 ng/mL; thus, this vaccine could serve as a prophylactic and therapeutic intervention. Given the increasing incidence of COVID-19 cases due to the Omicron variant, RBD from the Omicron strain could serve as a candidate immunogen that can induce higher neutralization activities against the SARS-CoV-2 Omicron sublineages.

Preformed Pt Nanoparticles Supported on Nanoshaped CeO2 for Total Propane Oxidation.

Pt-based catalysts have been widely used for the removal of short-chain volatile organic compounds (VOCs), such as propane. In this study, we synthesized Pt nanoparticles with a size of ca. 2.4 nm and loaded them on various fine-shaped CeO2 with different facets to investigate the effect of CeO2 morphology on the complete oxidation of propane. The Pt/CeO2-o catalyst with {111} facets exhibited superior catalytic activity compared to the Pt/CeO2-r catalyst with {110} and {100} facets. Specifically, the turnover frequency (TOF) value of Pt/CeO2-o was 1.8 times higher than that of Pt/CeO2-r. Moreover, Pt/CeO2-o showed outstanding long-term stability during 50 h. X-ray photoelectron spectroscopy (XPS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) revealed that the excellent performance of Pt/CeO2-o is due to the prevalence of metallic Pt species, which promotes C-C bond cleavage and facilitates the rapid removal of surface formate species. In contrast, a stronger metal-support interaction in Pt/CeO2-r leads to easier oxidation of Pt species and the accumulation of intermediates, which is detrimental to the catalytic activity. Our work provides insight into the oxidation of propane on different nanoshaped Pt/CeO2 catalysts.

Synergistic Effect and Electrical Properties of Microporous Liquid Metal/Carbon Nanotube Polymer Sponge.

Sensing sponge materials with light weight, high elasticity, and electrical sensing properties are in enormous demand in electronic fields, but there is an imminent need to develop a scalable and facile method for the manufacture of the sensing material. Herein, an efficient in situ polymerization and convenient preparation process is reported to manufacture the microporous liquid metal/carbon nanotube-polysulfide rubber (LM/CNT-PSR) sponges with excellent mechanical and electrical properties, based on fluidic LMs and rigid CNTs with unique synergistic effect for sponge composites. Excellent mechanical properties of LM/CNT-PSR sponges, such as low density, excellent elasticity, remarkable mechanical recoverability, and self-healing property, are endowed by the interconnected microporous structure of sponge and flexible polysulfide rubber matrix with disulfide bonds. In addition, the synergistic effect of LMs and CNTs leads to excellent conductivity and unique electrical sensing property under mechanical pressure. Microporous LM/CNT-PSR sponges with high performance and simple fabrication process are promising sensing materials for various electronic devices, such as human motion monitoring, and weighing sensing.

Silver and Copper Dual Single Atoms Boosting Direct Oxidation of Methane to Methanol via Synergistic Catalysis.

Rationally constructing atom-precise active sites is highly important to promote their catalytic performance but still challenging. Herein, this work designs and constructs ZSM-5 supported Cu and Ag dual single atoms as a proof-of-concept catalyst (Ag1 -Cu1 /ZSM-5 hetero-SAC (single-atom catalyst)) to boost direct oxidation of methane (DOM) by H2 O2 . The Ag1 -Cu1 /ZSM-5 hetero-SAC synthesized via a modified co-adsorption strategy yields a methanol productivity of 20,115 µmol gcat -1 with 81% selectivity at 70 °C within 30 min, which surpasses most of the state-of-the-art noble metal catalysts. The characterization results prove that the synergistic interaction between silver and copper facilitates the formation of highly reactive surface hydroxyl species to activate the C-H bond as well as the activity, selectivity, and stability of DOM compared with SACs, which is the key to the enhanced catalytic performance. This work believes the atomic-level design strategy on dual-single-atom active sites should pave the way to designing advanced catalysts for methane conversion.

Network Robustness Prediction: Influence of Training Data Distributions.

Network robustness refers to the ability of a network to continue its functioning against malicious attacks, which is critical for various natural and industrial networks. Network robustness can be quantitatively measured by a sequence of values that record the remaining functionality after a sequential node-or edge-removal attacks. Robustness evaluations are traditionally determined by attack simulations, which are computationally very time-consuming and sometimes practically infeasible. The convolutional neural network (CNN)-based prediction provides a cost-efficient approach to fast evaluating the network robustness. In this article, the prediction performances of the learning feature representation-based CNN (LFR-CNN) and PATCHY-SAN methods are compared through extensively empirical experiments. Specifically, three distributions of network size in the training data are investigated, including the uniform, Gaussian, and extra distributions. The relationship between the CNN input size and the dimension of the evaluated network is studied. Extensive experimental results reveal that compared to the training data of uniform distribution, the Gaussian and extra distributions can significantly improve both the prediction performance and the generalizability, for both LFR-CNN and PATCHY-SAN, and for various functionality robustness. The extension ability of LFR-CNN is significantly better than PATCHY-SAN, verified by extensive comparisons on predicting the robustness of unseen networks. In general, LFR-CNN outperforms PATCHY-SAN, and thus LFR-CNN is recommended over PATCHY-SAN. However, since both LFR-CNN and PATCHY-SAN have advantages for different scenarios, the optimal settings of the input size of CNN are recommended under different configurations.

Hollow SnO2/CdS QDs/CdCO3 heterostructured nanocubes coupled with hollow PtPd/MnCo-CeO2 nanozyme-mediated synergistic amplification for ultrasensitive PEC immunoanalysis of lung cancer biomarker.

Nowadays, lung cancer is one of the most dangerous cancers threatening human life all over the world. As a crucial biomarker, cytokeratin 19 fragment 21-1 (CYFRA 21-1) is extraordinary important for diagnosis of non-small cell lung cancer (NSCLC). In this work, we synthesized hollow SnO2/CdS QDs/CdCO3 heterostructured nanocubes with high and stable photocurrents, which applied to construction of a sandwich-typed photoelectrochemical (PEC) immunosensor for detection of CYFRA 21-1, integrated by in-situ catalytic precipitation strategy with home-built PtPd alloy anchored MnCo-CeO2 (PtPd/MnCo-CeO2) nanozyme for synergistic amplification. The interfacial electron transfer mechanism upon visible-light irradiation was investigated in details. Further, the PEC responses were seriously quenched by the specific immunoreaction and precipitation catalyzed by the PtPd/MnCo-CeO2 nanozyme. The established biosensor showed a wider linear range of 0.001-200 ng mL-1 and a lower limit of detection (LOD = 0.2 pg mL-1, S/N = 3), coupled by exploring such analysis even in diluted human serum sample. This work opens a constructive avenue to develop ultrasensitive PEC sensing platforms for detecting diverse cancer biomarkers in clinic.

Mechanism of an RBM-targeted rabbit monoclonal antibody 9H1 neutralizing SARS-CoV-2.

The COVID-19 pandemic, caused by SARS-CoV-2, has led to over 750 million infections and 6.8 million deaths worldwide since late 2019. Due to the continuous evolution of SARS-CoV-2, many significant variants have emerged, creating ongoing challenges to the prevention and treatment of the pandemic. Therefore, the study of antibody responses against SARS-CoV-2 is essential for the development of vaccines and therapeutics. Here we perform single particle cryo-electron microscopy (cryo-EM) structure determination of a rabbit monoclonal antibody (RmAb) 9H1 in complex with the SARS-CoV-2 wild-type (WT) spike trimer. Our structural analysis shows that 9H1 interacts with the receptor-binding motif (RBM) region of the receptor-binding domain (RBD) on the spike protein and by directly competing with angiotensin-converting enzyme 2 (ACE2), it blocks the binding of the virus to the receptor and achieves neutralization. Our findings suggest that utilizing rabbit-derived mAbs provides valuable insights into the molecular interactions between neutralizing antibodies and spike proteins and may also facilitate the development of therapeutic antibodies and expand the antibody library.

Mechanism of a rabbit monoclonal antibody broadly neutralizing SARS-CoV-2 variants.

Due to the continuous evolution of SARS-CoV-2, the Omicron variant has emerged and exhibits severe immune evasion. The high number of mutations at key antigenic sites on the spike protein has made a large number of existing antibodies and vaccines ineffective against this variant. Therefore, it is urgent to develop efficient broad-spectrum neutralizing therapeutic drugs. Here we characterize a rabbit monoclonal antibody (RmAb) 1H1 with broad-spectrum neutralizing potency against Omicron sublineages including BA.1, BA.1.1, BA.2, BA.2.12.1, BA.2.75, BA.3 and BA.4/5. Cryo-electron microscopy (cryo-EM) structure determination of the BA.1 spike-1H1 Fab complexes shows that 1H1 targets a highly conserved region of RBD and avoids most of the circulating Omicron mutations, explaining its broad-spectrum neutralization potency. Our findings indicate 1H1 as a promising RmAb model for designing broad-spectrum neutralizing antibodies and shed light on the development of therapeutic agents as well as effective vaccines against newly emerging variants in the future.

Carbonic Anhydrase 4 Serves as A Novel Prognostic Biomarker and Therapeutic Target for Non-Small Cell Lung Cancer: A Study Based on TCGA Samples.

BACKGROUND: Carbonic anhydrase 4 (CA4) is a member of a large family of zinc metalloenzymes that catalyze the reversible hydration of carbon dioxide and was found to have low expression in non-small cell lung cancer (NSCLC). However, the specific role of CA4 in NSCLC and the underlying mechanisms remain unknown. METHODS: The bioinformatic analysis on lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) datasets downloaded from The Cancer Genome Atlas (TCGA) database was performed. We found that CA4 expression was lower in tumors than that in normal tissues, which were verified by Real-time PCR. Lower CA4 levels were significantly associated with higher T stages in LUAD and LUSC cohorts. Multivariate analysis showed that CA4 is an independent prognostic factor for NSCLC. Furthermore, the expression of CA4 also correlated with immune infiltration and drug sensitivity. RESULTS: Ectopic expression of CA4 decreased NSCLC cell proliferation in vitro by CCK-8 assay. CA4 caused G0/G1 cell cycle arrest by cell experiments. Mechanistic studies found that CA affects the cell cycle and inhibits cell proliferation by downregulating the expression of CDK2. CONCLUSION: The present findings highlight the role of CA4 in NSCLC and identify CA4 as a potential novel diagnostic and prognostic biomarker for the treatment of NSCLC.

Adequate Number of Lymph Nodes Sampled May Determine Appropriate Surgical Modality for Early-Stage NSCLC: A Population-Based Real-World Study.

BACKGROUND: The standard surgical procedure for ≤ 2 cm non-small cell lung cancer (NSCLC), including the number of lymph nodes sampled (nLN) and surgical modality, remains controversial. This study was designed to determine the optimal cohort in which sublobectomy could be an alternative to lobectomy. MATERIALS (OR PATIENTS) AND METHODS: Patients from 1998 to 2017 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The optimal cutoff value of nLN was identified using a restrictive cubic spline graph (RCS). Kaplan-Meier analysis was used to determine cancer-specific survival (CSS). The COX proportional hazard regression model was used to identify the influence of clinical and demographic variables on survival, and propensity score matching (PSM) was used to balance differences in baseline characteristics. Finally, we used an external cohort from a single-center medical institution to verify the conclusions drawn from the SEER database. RESULTS: A total of 6150 patients were included. The sublobectomy subgroup included segmentectomy (308, 5.0%) and wedge resection (1611, 26.2%). The cutoff value for nLN was 7. In the nLN ≥7 subgroup of the PSM cohort, the CSS of segmentectomy and wedge resection was close to that of the lobectomy subgroup (P = .12), whereas in the nLN <7 subgroup, the CSS of the lobectomy subgroup was significantly higher than that of the sublobectomy with P < .001). Surgical methods, nLN, age, sex, and differentiated grade were independent predictors of CSS. External cohort validation: A total of 1106 patients from the Affiliated Jinhua Hospital of Zhejiang University School of Medicine between 2013 and 2020 were included. The grouping criteria were consistent with the SEER database. In the nLN≥7 subgroup, sublobectomy had a survival outcome similar to that of lobectomy (P = .81). CONCLUSION: Sublobectomy and nLN < 7 were strongly associated with poorer CSS for early-stage NSCLC. On the premise of nLN ≥ 7, sublobectomy could provide similar survival outcomes to lobectomy for these patients.

Biocidal Potency of Polymers with Bulky Cations.

The performance of antimicrobial polymers depends sensitively on the type of cationic species, charge density, and spatial arrangement of cations. Here we report antimicrobial polymers bearing unusually bulky tetraaminophosphonium groups as the source of highly delocalized cationic charge. The bulky cations drastically enhanced the biocidal activity of amphiphilic polymers, leading to remarkably potent activity in the submicromolar range. The cationic polynorbornenes with pendent tetraaminophosphonium groups killed over 98% E. coli at a concentration of 0.1 µg/mL and caused a 4-log reduction of E. coli within 2 h at a concentration of 2 µg/mL, showing very rapid and potent bactericidal activity. The polymers are also highly hemolytic at similar concentrations, indicating a biocidal activity profile. Polymers of a similar chemical structure but with more flexible backbones were made to examine the effects of the flexibility of polymer chains on their activity, which turned out to be marginal. We also explore variants with different spacer arm groups separating the cations from the backbone main chain. The antibacterial activity was comparably potent in all cases, but the polymers with shorter spacer arm groups showed more rapid bactericidal kinetics. Interestingly, pronounced counterion effects were observed. Tightly bound PF6- counteranions showed poor activity at high concentrations due to gross aggregate formation and precipitation from the assay media, whereas loosely bound Cl- counterions resulted in very potent activity that monotonically increased with increasing concentration. In this paper, we reveal that bulky phosphonium cations are associated with markedly enhanced biocidal activity, which provides an innovative strategy to develop more effective self-disinfecting materials.

A Learning Convolutional Neural Network Approach for Network Robustness Prediction.

Network robustness is critical for various societal and industrial networks against malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible for large-scale networks. In this article, an improved method for network robustness prediction is developed based on learning feature representation using the convolutional neural network (LFR-CNN). In this scheme, the higher-dimensional network data are compressed into lower-dimensional representations, which are then passed to a convolutional neural network to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that: 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly smaller prediction errors; 2) LFR-CNN is insensitive to the variation of the input network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; and 4) LFR-CNN not only accurately predicts the network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.

Classification-based prediction of network connectivity robustness.

Today, there is an increasing concern about malicious attacks on various networks in society and industry, against which the network robustness is critical. Network connectivity robustness, in particular, is of fundamental importance, which is generally measured by a sequence of calculated values that indicate the connectedness of the remaining network after a sequence of attacks by means of node- or edge-removal. It is computationally time-consuming, however, to measure and evaluate the network connectivity robustness using the conventional attack simulations, especially for large-scale networked systems. In the present paper, an efficient robustness predictor based on multiple convolutional neural networks (mCNN-RP) is proposed for predicting the network connectivity robustness, which is an natural extension of the single CNN-based predictor. In mCNN-RP, one CNN works as the classifier, while each of the rest CNNs works as an estimator for predicting the connectivity robustness of every classified network category. The network categories are classified according to the available prior knowledge. A data-based filter is installed for predictive data refinement. Extensive experimental studies on both synthetic and real-world networks, including directed and undirected as well as weighted and unweighted topologies, verify the effectiveness of mCNN-RP. The results demonstrate that the average prediction error is lower than the standard deviation of the tested data, which outperforms the single CNN-based framework. The runtime in assessing network connectivity robustness is significantly reduced by using the CNN-based technique. The proposed mCNN-RP not only can accurately predict the connectivity robustness of various complex networks, but also provides an excellent indicator for the connectivity robustness, better than other existing prediction measures.