A lost piece of the puzzle of the alkane cracking mechanism: a carbanion pathway on a solid base catalyst.

The alkane cracking mechanism has been a subject of intense scrutiny, with carbonium and free radical mechanisms being two well-established pathways which correlate to solid acid catalysis and thermal cracking, respectively. However, despite an understanding of these two mechanisms, certain intricacies remain unexplored, especially when it comes to alternative reaction routes over solid base materials. This gap in the knowledge hinders optimization of the desired product selectivity of alkane cracking processes. In this work, solid superbases were first prepared by impregnation of NaNO3 on MgO. The Na/MgO catalysts were characterized by XRD, BET, XPS and CO2-TPD techniques. To investigate the role of solid base materials, propane cracking was conducted over MgO and Na/MgO. SiO2 was chosen as a representative of thermal cracking. Na/MgO showed better selectivity for light olefins than MgO or SiO2. Ethylene and light olefin selectivity could reach about 65.8% and 91.7%, respectively. Meanwhile, in terms of Na/MgO, the ratio of ethylene selectivity and propylene selectivity is greater than 2, exhibiting the advantage of selectivity for ethylene, which is obviously different from MgO and SiO2. Propane cracking over Na/MgO with different loading amounts of NaNO3 was investigated further. The conversion rates of the samples presented a "volcano curve" with increasing Na content. Furthermore, DFT calculation showed that the base-catalyzed process of the propane cracking reaction follows a carbanion mechanism. The better product distribution and stronger surface base sites can be ascribed to charge transfer arising from the loading of NaNO3.

Pseudomonas-associated bacteria play a key role in obtaining nutrition from bamboo for the giant panda (Ailuropoda melanoleuca).

Gut microbiota plays a vital role in obtaining nutrition from bamboo for giant pandas. However, low cellulase activity has been observed in the panda's gut. Besides, no specific pathway has been implicated in lignin digestion by gut microbiota of pandas. Therefore, the mechanism by which they obtain nutrients is still controversial. It is necessary to elucidate the precise pathways employed by gut microbiota of pandas to degrade lignin. Here, the metabolic pathways for lignin degradation in pandas were explored by comparing 209 metagenomic sequencing data from wild species with different feeding habits. Lignin degradation central pathways, including beta-ketoadipate and homogentisate pathway, were enriched in the gut of wild bamboo-eating pandas. The gut microbiome of wild bamboo-eating specialists was enriched with genes from pathways implicated in degrading ferulate and p-coumarate into acetyl-CoA and succinyl-CoA, which can potentially provide the raw materials for metabolism in pandas. Specifically, Pseudomonas, as the most dominant gut bacteria genus, was found to be the main bacteria to provide genes involved in lignin or lignin derivative degradation. Herein, three Pseudomonas-associated strains isolated from the feces of wild pandas showed the laccase, lignin peroxidase, and manganese peroxidase activity and extracellular lignin degradation ability in vitro. A potential mechanism for pandas to obtain nutrition from bamboo was proposed based on the results. This study provides novel insights into the adaptive evolution of pandas from the perspective of lignin metabolism. IMPORTANCE: Although giant pandas only feed on bamboo, the mechanism of lignin digestion in pandas is unclear. Here, the metabolic pathways for lignin degradation in wild pandas were explored by comparing gut metagenomic from species with different feeding habits. Results showed that lignin degradation central pathways, including beta-ketoadipate and homogentisate pathway, were enriched in the gut of wild bamboo-eating pandas. Genes from pathways involved in degrading ferulate and p-coumarate via beta-ketoadipate pathway were also enriched in bamboo-eating pandas. The final products of the above process, such as acetyl-CoA, can potentially provide the raw materials for metabolism in pandas. Specifically, Pseudomonas, as the most dominant gut bacteria genus, mainly provides genes involved in lignin degradation. Herein, Pseudomonas-associated strains isolated from the feces of pandas could degrade extracellular lignin. These findings suggest that gut microbiome of pandas is crucial in obtaining nutrition from lignin via Pseudomonas, as the main lignin-degrading bacteria.

Aggregation-induced emission-active iridium (III)-based mitochondria-targeting nanoparticle for two-photon imaging-guided photodynamic therapy.

The efficacy of imaging-guided photodynamic therapy (PDT) is compromised by the attenuation of fluorescence and decline in reactive oxygen species (ROS) generation efficiency in the physiological environment of conventional photosensitizers, limited near-infrared (NIR) absorption, and high systemic cytotoxicity. This paper presents the synthesis of two cyclometalated Ir (III) complexes (Ir-thpy and Ir-ppy) by using a triphenylamine derivative (DPTPA) as the primary ligand and their encapsulation into an amphiphilic phospholipid to form nanoparticles (NPs). These complexes exhibit aggregation-induced emission features and remarkably enhanced ROS generation compared to Chlorin e6 (Ce6). Moreover, Ir-thpy NPs possess the unique ability to selectively target mitochondria, leading to depolarization of the mitochondrial membrane potential and ultimately triggering apoptosis. Notably, Ir-thpy NPs exhibit exceptional photocytotoxicity even towards cisplatin-resistant A549/DDP tumor cells. In vivo two-photon imaging verified the robust tumor-targeting efficacy of Ir-thpy NPs. The in vivo results unequivocally demonstrate that Ir-thpy NPs exhibit excellent tumor ablation along with remarkable biocompatibility. This study presents a promising approach for the development of multifunctional Ir-NPs for two-photon imaging-guided PDT and provides novel insights for potential clinical applications in oncology.

[Retracted] TRIM16 suppresses the progression of prostate tumors by inhibiting the Snail signaling pathway.

Following the publication of this paper, it was drawn to the Editor's attention by concerned readers that the western blotting data shown in Figs. 4C and 7B and D, the scratch­wound assay images shown in Figs. 5A and 6A, and certain of the cell migration and invasion assay data shown in Figs. 5B and 6B were strikingly similar to data that had previously appeared in different form in other articles by different authors. Owing to the fact that the contentious data in the above article had already been published elsewhere, or were already under consideration for publication, prior to its submission to International Journal of Molecular Medicine, the Editor has decided that this paper should be retracted from the Journal. After having been in contact with the authors, they accepted the decision to retract the paper. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Molecular Medicine 38: 1734­1742, 2016; DOI: 10.3892/ijmm.2016.2774].

Engineering Z-Scheme FeOOH/PCN with Fast Photoelectron Transfer and Surface Redox Kinetics for Efficient Solar-Driven CO2 Reduction.

Solar-driven conversion of carbon dioxide (CO2) without sacrificial agents offers an attractive alternative in sustainable energy research; nevertheless, it is often retarded by the sluggish water oxidation kinetics and severe charge recombination. To this end, a Z-scheme iron oxyhydroxide/polymeric carbon nitride (FeOOH/PCN) heterojunction, as identified by quasi in situ X-ray photoelectron spectroscopy, is constructed. In this heterostructure, the two-dimensional FeOOH nanorod provides rich coordinatively unsaturated sites and highly oxidative photoinduced holes to boost the sluggish water decomposition kinetics. Meanwhile, PCN acts as a robust agent for CO2 reduction. Consequently, FeOOH/PCN achieves efficient CO2 photoreduction with a superior selectivity of CH4 (>85%), together with an apparent quantum efficiency of 2.4% at 420 nm that outperforms most two-step photosystems to date. This work offers an innovative strategy for the construction of photocatalytic systems toward solar fuel production.

Clinical Characteristics of persistent geotropic horizontal direction-changing positional nystagmus: Experience in 189 participants.

BACKGROUND: The etiology and mechanism of persistent geotropic horizontal direction-changing positional nystagmus (DCPN) are still unclear. Whether this pattern of nystagmus is a subtype of benign paroxysmal positional vertigo (BPPV) remains controversial. OBJECTIVE: The goal of this study was to observe the clinical characteristics of persistent geotropic horizontal DCPN involving the lateral semicircular canal. METHODS: The analysis was performed to participants with episodic positional vertigo in our clinic from 2014 to 2021. Participants were included via positional test. We observed and summarized the distribution data, vertigo history, follow-up tests, and recurrence situations of 189 persistent geotropic horizontal DCPN participants. RESULTS: The mean age at the first time showing of persistent geotropic horizontal DCPN was 56±14.7 and more women than men were affected by persistent geotropic horizontal DCPN (female-to-male ratio 2.4 : 1). Overall, 58.7% (57/94) of participants who came for the first-week follow-up test were asymptomatic. Thirty-three participants experienced recurrence (female-to-male ratio: 4.5 : 1). Fifty-three (28.0%) participants experienced the conversion of the patterns of DCPN in the history, the follow-up tests and the recurrence compared to the first showing of persistent geotropic horizontal DCPN. 24(12.1%) participants still experienced persistent geotropic horizontal DCPN attack in the follow-up tests and the recurrences without benign paroxysmal positional vertigo history. CONCLUSION: Persistent geotropic horizontal DCPN affected more women than men. The persistent geotropic horizontal DCPN that with conversion to transient geotropic horizontal DCPN or to persistent apogeotropic horizontal DCPN might be a subtype of BPPV or in a stage of BPPV process. However, the persistent geotropic horizontal DCPN without conversion might be an independent disease that is not related to BPPV.

Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer.

Breast cancer is common in women, and its number of patients ranks first among female malignant tumors. Breast cancer is highly heterogeneous, and different types of breast cancer have different biological behaviors and prognoses. Therefore, identifying the different types of breast cancer is of great help in formulating individualized treatment plans. Based on serum Raman spectroscopy and deep learning algorithms, we propose a fast and low-cost diagnosis method for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and healthy controls. We collected 75 serum samples in this study, including 23 triple-negative breast cancers, 22 HER2-positive breast cancers, and 30 healthy controls. Using the preprocessed Raman spectra as the input of deep learning, three deep learning models, neural network language model (NNLM), bidirectional long-short-term memory network (BiLSTM), and convolutional neural network (CNN), were established, and the accuracy rates of the three models were 87.78%, 90.37%, and 91.11%, respectively. The experimental results demonstrate the feasibility of serum Raman spectroscopy combined with deep learning algorithms to diagnose breast cancer, which can be used as an effective auxiliary diagnosis method for breast cancer.

Mathematical modeling the dynamics of SARS-CoV-2 infection with antibody-dependent enhancement.

The advent and swift global spread of the novel coronavirus (COVID-19) transmitted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have caused massive deaths and economic devastation worldwide. Antibody-dependent enhancement (ADE) is a common phenomenon in virology that directly affects the effectiveness of the vaccine, and there is no fully effective vaccine for diseases. In order to study the potential role of ADE on SARS-CoV-2 infection, we establish the SARS-CoV-2 infection dynamics model with ADE. The basic reproduction number is computed. We prove that when R 0 < 1 , the infection-free equilibrium is globally asymptotically stable, and the system is uniformly persistent when R 0 > 1 . We carry out the sensitivity analysis by the partial rank correlation coefficients and the extended version of the Fourier amplitude sensitivity test. Numerical simulations are implemented to illustrate the theoretical results. The potential impact of ADE on SARS-CoV-2 infection is also assessed. Our results show that ADE may accelerate SARS-CoV-2 infection. Furthermore, our findings suggest that increasing antibody titers can have the ability to control SARS-CoV-2 infection with ADE, but enhancing the neutralizing power of antibodies may be ineffective to control SARS-CoV-2 infection with ADE. Our study presumably contributes to a better understanding of the dynamics of SARS-CoV-2 infection with ADE.

FSAD-Net: Feedback Spatial Attention Dehazing Network.

Recent dehazing networks learn more discriminative high-level features by designing deeper networks or introducing complicated structures, while ignoring inherent feature correlations in intermediate layers. In this article, we establish a novel and effective end-to-end dehazing method, named feedback spatial attention dehazing network (FSAD-Net). FSAD-Net is based on the recurrent structure and consists of four modules: a shallow feature extraction block (SFEB), a feedback block (FB), multiple advanced residual blocks (ARBs), and a reconstruction block (RB). FB is designed to handle feedback connections, and it can improve the dehazing performance by exploiting the dependencies of deep features across stages. ARB implements a novel attention-based estimation on a residual block to adapt to pixels with different distributions. Finally, RB helps restore haze-free images. It can be seen from the experimental results that FSAD-Net almost outperforms the state-of-the-arts in terms of five quantitative metrics. Moreover, the qualitatively comparisons on real-world images also demonstrate the superiority of the proposed FSAD-Net. Considering the efficiency and effectiveness of FSAD-Net, it can be expected to serve as a suitable image dehazing baseline in the future.

Evaluations of COVID-19 epidemic models with multiple susceptible compartments using exponential and non-exponential distribution for disease stages.

Mathematical models have wide applications in studying COVID-19 epidemic transmission dynamics, however, most mathematical models do not take into account the heterogeneity of susceptible populations and the non-exponential distribution infectious period. This paper attempts to investigate whether non-exponentially distributed infectious period can better characterize the transmission process in heterogeneous susceptible populations and how it impacts the control strategies. For this purpose, we establish two COVID-19 epidemic models with heterogeneous susceptible populations based on different assumptions for infectious period: the first one is an exponential distribution model (EDM), and the other one is a gamma distribution model (GDM); explicit formula of peak time of the EDM is presented via our analytical approach. By data fitting with the COVID-19 (Omicron) epidemic in Spain and Norway, it seems that Spain is more suitable for EDM while Norway is more suitable for GDM. Finally, we use EDM and GDM to evaluate the impaction of control strategies such as reduction of transmission rates, and increase of primary course rate (PCR) and booster dose rate (BDR).

Global stability and optimal control for a COVID-19 model with vaccination and isolation delays.

COVID-19 pandemic remains serious around the world and causes huge deaths and economic losses. To investigate the effect of vaccination and isolation delays on the transmission of COVID-19, we propose a mathematical model of COVID-19 transmission with vaccination and isolation delays. The basic reproduction number is computed, and the global dynamics of the model are proved. When R 0 < 1 , the disease-free equilibrium is globally asymptotically stable. The unique endemic equilibrium is globally asymptotically stable if R 0 > 1 . Based on the public information, parameter values are estimated, and sensitivity analysis is carried out by the partial rank correlation coefficients (PRCCs) and the extended version of the Fourier amplitude sensitivity test (eFAST). Our results suggest that the isolation rates of asymptomatic and symptomatic infectious individuals have a significant impact on the transmission of COVID-19. When the COVID-19 is epidemic, the optimal control strategies of our model with vaccination and isolation delays are analyzed. Under the limited resource with constant and time-varying isolation rates, we find that the optimal isolation rates may minimize the cumulative number of infected individuals and the cost of disease control, and effectively contain the transmission of COVID-19. Our study may help public health to prevent and control the COVID-19 spread.

Grouped-sampling technique to deal with unbalance in Raman spectral data modeling.

Due to limitations in disease prevalence and hospital specificity, spectral data are often collected with unbalanced sample size. To solve this problem, a new sampling method - grouped-sampling was proposed in this research, which is shown to be effective for unbalanced data. It avoids over-fitting of over-sampling and overcomes under-sampling utilization of under-sampling. In this study, we applied grouped-sampling to two unbalanced datasets where the sample proportions are 199:40 and 75:225. And then verified from two classic models: PCA-SVM (Principal Component Analysis-Support Vector Machine) and the deep learning algorithm GoogLeNet. The accuracy of these two datasets were 85.11% and 96.15% in PCA-SVM and 85.10% and 84.61% on GoogLeNet. Also, the F1-score were evaluated to measure the classification balance of sampling method, and result shows that F1-score of grouped-sampling is always the highest compared to over-sampling and under-sampling. In summary, compared to traditional sampling methods, grouped-sampling performs better on prediction for classes with smaller sample size, which means grouped-sampling can improve the balance of classification results and the potential of practical application. Therefore, we develop a group sampling method that distinguishes between under- and over-sampling, which greatly improves the accuracy and balance of predictions for unbalanced samples.

Evaluation of Effectiveness of Global COVID-19 Vaccination Campaign.

To model estimated deaths averted by COVID-19 vaccines, we used state-of-the-art mathematical modeling, likelihood-based inference, and reported COVID-19 death and vaccination data. We estimated that >1.5 million deaths were averted in 12 countries. Our model can help assess effectiveness of the vaccination program, which is crucial for curbing the COVID-19 pandemic.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

Influence of coke heterogeneity and the interaction between different coke species on the emission of toxic HCN and NOx from FCC spent catalyst regeneration.

Concerning the emissions of hydrogen cyanide (HCN) and other N-bearing air pollutants from the fluid catalytic cracking (FCC) regeneration units, this paper has conducted a comprehensive testing and surface characterisation of four industrial spent catalysts, aged catalysts and hard coke sample in three different schemes, Ar-TPD, O2 -TPO and rapid heating to elaborate the transformation of N upon the influence of the heterogeneity of coke and N speciation. In the Ar-TPD scheme, the surface N is responsive for the emission of gaseous NH3 from pyrrolic N-5 and HCN from both pyridinic N-6 and quaternary N-Q. The removal of soft coke is beneficial in promoting the surface exposure of hard coke, thereby increasing the HCN emission dramatically. In the O2-TPO scheme, the oxygen accessibility is the principal factor governing the emission of HCN. The external soft coke is able to access the bulk O2 firstly, the combustion of which in turn provides heat back to promote the cracking of internal hard coke from the same and neighbouring particles to release more HCN. The induction effect of bulk O2 is also superior over the spent catalyst properties in formulating a nearly identical trend of HCN emission for all the four spent catalysts tested. Finally, for the use of rapid heating scheme that is typical in a commercial FCC regenerator, it is effective in accelerating the volatilisation of soft coke quickly, thereby promoting the oxygen accessibility to hard coke and the internal N-bearing precursors so as to mitigate the emission of HCN effectively. The use of a large superficial velocity of gas is further effective in sweeping the volatiles including HCN away from the catalyst, promoting their oxidation extent accordingly.

Impact of low vaccine coverage on the resurgence of COVID-19 in Central and Eastern Europe.

The coronavirus disease 2019 (COVID-19) pandemic has caused a tremendous global impact both socially and economically. The mechanisms behind the disparity in the severity, vaccine coverage, and variant replacement patterns across European countries are unclear. In this work, we aim to reveal the possible reasons via data visualization and model fitting. We developed a model with a vaccination component to simulate the mortality waves in these countries. Deaths averted by the vaccination campaign were estimated. Finally, we discuss the potential reasons behind the differences in vaccine coverage across European countries. Contemporary transportation and global trade bring significant convenience to our daily life but also facilitate the spread of the novel virus COVID-19 to anywhere globally within a short time. The observations and results in this work highlight the importance of the global campaign to mitigate the COVID-19 pandemic and future pandemics under the One Health approach.

Bioinformatics Analysis Reveals Hub Genes That May Reduce Inflammation and Complications After Cardiopulmonary Bypass.

Cardiopulmonary bypass (CPB), though indispensable in many cardiac surgery procedures, has several undesirable consequences. The aim of this study was to identify potential genes that may reduce the inflammatory response and complications after CPB. The GSE132176 dataset was selected from the Gene Expression Omnibus (GEO) database and included 10 patients with tetralogy of Fallot and 10 patients with an atrial septal defect who underwent CPB surgery. TSV files were downloaded after GEO2R processing. Protein-protein interaction analysis of common differentially expressed genes (DEGs) was performed using the Search Tool for the Retrieval of Interacting Genes. Gene modules and hub genes were visualized in the protein-protein interaction network using Cytoscape. Enrichment analysis was performed for all important DEGs, modular genes, and hub genes. A total of 72 DEGs were screened, including two functional and one hub gene module. FOS modular genes were primarily enriched in NGF-stimulated transcription, spinal cord injury, and PID AP1 pathway. The ATF3 modular gene was mainly enriched in cytomegalovirus infection and transcriptional misregulation in cancer. Hub gene modules were primarily enriched in the PID AP1 pathway, positive regulation of pri-miRNA transcription by RNA polymerase II, and the PID ATF2 pathway. FOS, JUN, ATF3, and EGR1 were the four most important hub genes; the top three hub genes were involved in the formation of AP-1 and enriched in the AP-1 pathway. Finally, we measured the expression levels of these four genes in patients undergoing CPB via qRT-PCR, and the results were consistent with those obtained in bioinformatic analysis. FOS, JUN, ATF3, and EGR1 and the AP-1 pathway may play key roles in inflammation and complications caused by CPB.

Rapid identification of papillary thyroid carcinoma and papillary microcarcinoma based on serum Raman spectroscopy combined with machine learning models.

Thyroid carcinoma is with the highest diagnosis rate in the endocrine system, and its main histological subtype is papillary thyroid carcinoma (PTC) accounting for 80% of thyroid malignancies. In recent years, the incidence of thyroid cancer has increased exponentially, and its substantial increase was closely related to the overdiagnosis of papillary microcarcinoma (PMC). Therefore, early and accurate identification of PTC and PMC can prevent patients from over treatment. This study aimed to identify PTC and PMC using Raman spectroscopy. We collected serum Raman spectra from 16 patients with PTC and 31 patients with PMC. Firstly, the collected imbalance data were preprocessed using the synthetic minority over-sampling technique (SMOTE). Then, the equalized data were dimensionality reduced by principal component analysis (PCA). Finally, the processed data were fed into the single decision tree (DT) classifier, as well as the random forest (RF) built on the idea of Boosting ensemble and the Adaptive Boosting (Adaboost) model built on the idea of Bagging ensemble for classification. The classification accuracy of the three models in the testing set were 75.38%, 81.54%, and 84.61%, respectively. Compared with the DT classifier, the accuracy of the models introducing the idea of ensemble learning was enhanced by 6.16% and 9.23%, respectively. The best model was the Adaboost. This result demonstrates that serum Raman spectroscopy combined with an ensemble learning algorithm was feasible in rapidly identifying PTC and PMC. At the same time, the method has great potential for application in the field of clinical diagnosis.

Modelling and stability analysis of ASFV with swill and the virus in the environment.

African swine fever (ASF) is an acute, hemorrhagic and severe infectious disease caused by the African swine fever virus (ASFV), and leads to a serious threat to the pig industry in China. Yet the impact of the virus in the environment and contaminated swill on the ASFV transmission is unclear in China. Then we build the ASFV transmission model with the virus in the environment and swill. We compute the basic reproduction number, and prove that the disease-free equilibrium is globally asymptotically stable when $ R_0 < 1 $ and the unique endemic equilibrium is globally asymptotically stable when $ R_0 > 1 $. Using the public information, parameter values are evaluated. PRCCs and eFAST sensitivity analysis reveal that the release rate of ASFV from asymptomatic and symptomatic infectious pigs and the proportion of pig products from infectious pigs to swill have a significant impact on the ASFV transmission. Our findings suggest that the virus in the environment and contaminated swill contribute to the ASFV transmission. Our results may help animal health to prevent and control the ASFV transmission.