Christensenella strain resources, genomic/metabolomic profiling, and association with host at species level.

The gut commensal bacteria Christensenellaceae species are negatively associated with many metabolic diseases, and have been seen as promising next-generation probiotics. However, the cultured Christensenellaceae strain resources were limited, and their beneficial mechanisms for improving metabolic diseases have yet to be explored. In this study, we developed a method that enabled the enrichment and cultivation of Christensenellaceae strains from fecal samples. Using this method, a collection of Christensenellaceae Gut Microbial Biobank (ChrisGMB) was established, composed of 87 strains and genomes that represent 14 species of 8 genera. Seven species were first described and the cultured Christensenellaceae resources have been significantly expanded at species and strain levels. Christensenella strains exerted different abilities in utilization of various complex polysaccharides and other carbon sources, exhibited host-adaptation capabilities such as acid tolerance and bile tolerance, produced a wide range of volatile probiotic metabolites and secondary bile acids. Cohort analyses demonstrated that Christensenellaceae and Christensenella were prevalent in various cohorts and the abundances were significantly reduced in T2D and OB cohorts. At species level, Christensenellaceae showed different changes among healthy and disease cohorts. C. faecalis, F. tenuis, L. tenuis, and Guo. tenuis significantly reduced in all the metabolic disease cohorts. The relative abundances of C. minuta, C. hongkongensis and C. massiliensis showed no significant change in NAFLD and ACVD. and C. tenuis and C. acetigenes showed no significant change in ACVD, and Q. tenuis and Geh. tenuis showed no significant change in NAFLD, when compared with the HC cohort. So far as we know, this is the largest collection of cultured resource and first exploration of Christensenellaceae prevalences and abundances at species level.

Xiashengella succiniciproducens gen. nov., sp. nov., a succinate-producing bacterium isolated from an anaerobic digestion tank in the family Marinilabiliaceae of the order Bacteroidales.

A strictly anaerobic, motile bacterium, designated as strain Ai-910T, was isolated from the sludge of an anaerobic digestion tank in China. Cells were Gram-stain-negative rods. Optimal growth was observed at 38 °C (growth range 25-42 °C), pH 8.5 (growth range 5.5-10.5), and under a NaCl concentration of 0.06% (w/v) (range 0-2.0%). Major cellular fatty acids were iso-C15 : 0 and anteiso-C15 : 0. The respiratory quinone was MK-7. Using xylose as the growth substrate, succinate was produced as the fermentation product. Phylogenetic analysis based on the 16 S rRNA gene sequences indicated that strain Ai-910T formed a distinct phylogenetic lineage that reflects a new genus in the family Marinilabiliaceae, sharing high similarities to Alkaliflexus imshenetskii Z-7010T (92.78%), Alkalitalea saponilacus SC/BZ-SP2T (92.51%), and Geofilum rubicundum JAM-BA0501T (92.36%). Genomic similarity (average nucleotide identity and digital DNA-DNA hybridization) values between strain Ai-910T and its phylogenetic neighbors were below 65.27 and 16.90%, respectively, indicating that strain Ai-910T represented a novel species. The average amino acid identity between strain Ai-910T and other related members of the family Marinilabiliaceae were below 69.41%, supporting that strain Ai-910T was a member of a new genus within the family Marinilabiliaceae. Phylogenetic, genomic, and phenotypic analysis revealed that strain Ai-910T was distinguished from other phylogenetic relatives within the family Marinilabiliaceae. The genome size was 3.10 Mbp, and the DNA G + C content of the isolate was 42.8 mol%. Collectively, differences of the phenotypic and phylogenetic features of strain Ai-910T from its close relatives suggest that strain Ai-910T represented a novel species in a new genus of the family Marinilabiliaceae, for which the name Xiashengella succiniciproducens gen. nov., sp. nov. was proposed. The type strain of Xiashengella succiniciproducens is Ai-910T (= CGMCC 1.17893T = KCTC 25,304T).

Gut commensal Christensenella minuta modulates host metabolism via acylated secondary bile acids.

A strong correlation between gut microbes and host health has been observed in numerous gut metagenomic cohort studies. However, the underlying mechanisms governing host-microbe interactions in the gut remain largely unknown. Here we report that the gut commensal Christensenella minuta modulates host metabolism by generating a previously undescribed class of secondary bile acids with 3-O-acylation substitution that inhibit the intestinal farnesoid X receptor. Administration of C. minuta alleviated features of metabolic disease in high fat diet-induced obese mice associated with a significant increase in these acylated bile acids, which we refer to as 3-O-acyl-cholic acids. Specific knockout of intestinal farnesoid X receptor in mice counteracted the beneficial effects observed in their wild-type counterparts. Finally, we showed that 3-O-acyl-CAs were prevalent in healthy humans but significantly depleted in patients with type 2 diabetes. Our findings indicate a role for C. minuta and acylated bile acids in metabolic diseases.

Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels.

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR.

RST: Rough Set Transformer for Point Cloud Learning.

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method.

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.

Objective.Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.Approach.We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).Main results.Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples.Significance.These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.

Degradation mechanism of microplastics and potential risks during sewage sludge co-composting: A comprehensive review.

Microplastics (MPs) as a kind of emerging contaminants, widely exists in various kinds of medium, sewage sludge (SS) is no exception. In the sewage treatment process, a large number of microplastics will be deposited in SS. More seriously, microplastics in sewage sludge can migrate to other environmental media and threaten human health. Therefore, it is necessary to remove MPs from SS. Among the various restorations, aerobic composting is emerging as a green microplastic removal method. There are more and more reports of using aerobic compost to degrade microplastics. However, there are few reports on the degradation mechanism of MPs in aerobic composting, hindering the innovation of aerobic composting methods. Therefore, in this paper, the degradation mechanism of MPs in SS is discussed based on the environmental factors such as physical, chemical and biological factors in the composting process. In addition, this paper expounds the MPs in potential hazards, and combined with the problems in the present study were studied the outlook.

KIF15 knockdown inhibits the development of endometrial cancer by suppressing epithelial-mesenchymal transition and stemness through Wnt/ß-catenin signaling.

Endometrial cancer (EC) is one of the most common cancers among women, while the incidence of EC is rising. Many studies have found that Kinesin family member 15 (KIF15) is highly expressed in a series of cancers, but the role of KIF15 in EC is unclear. We detected the expression level of KIF15 in a microarray of EC tissues by immunohistochemical staining (IHC), and analyzed the correlation between the expression level of KIF15 and the pathological characteristics of patients. After inhibit the expression of KIF15 in EC cells with lentivirus, cell proliferation and apoptosis were detected respectively by CCK8 assay, flow cytometry and tunnel assay. Transwell assay and wound healing assay were used to examine the migration ability and invasion ability of EC cells. Spheroid formation assay was used to evaluate cell self-renewal ability. In vivo tumor xenograft model was used for validation. The expressions of epithelial-mesenchymal transition, cancer stem cells, and Wnt/ß-catenin signaling molecules were detected by Western blotting. The results showed that the expression of KIF15 in EC tissues was higher than that in normal endometrial tissues, while the expression level of KIF15 in EC was positively correlated with the pathological grade of the tumor. The down-regulation of KIF15 reduced the proliferation, colony formation, invasion, migration and self-renewal ability of EC cells, while promoted cell apoptosis. Knockdown of KIF15 inactivates the Wnt/ß-catenin signaling of EC cells, inhibitors of Wnt signaling can counteract the enhanced self-renewal ability caused by KIF15 overexpression. Therefore, KIF15 may be a new potential target for diagnosis and treatment of EC.

Hormesis-tempting stressors driven by evolutionary factors for mitigating negative impacts instigated over extended exposure to chemical elements.

The adaptive responses to moderate environmental challenges by the biological systems have usually been credited to hormesis. Since the hormetic biphasic dose-response illustrates a prominent pattern towards biological responsiveness, the studies concerning such aspects will get much more significance in risk assessment practices and toxicological evaluation research. From this point of view, the past few epochs have witnessed the extending recognition of the notion concerning hormesis. The extraction of its basic foundations of evolutionary perspectives-along with the probable underlying molecular and cellular mechanisms followed by the practical implications to enhance the quality of life. To get better and more effective output in this regard, the present article has evaluated the various observations of previous investigations. The intent of integrating the novel inferences concerning the hormesis-tempting stressors driven by predominant evolutionary factors for mitigating the adverse impacts that were prompted over frequent and continuous exposure to the various chemical elements. Such inferences can offer extensive insight into the implications concerning the risk assessment of hormesis.

PatchMix Augmentation to Identify Causal Features in Few-Shot Learning.

The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It is currently an important research question and has great practical values in the real-world applications. Despite extensive previous efforts are made on few-shot learning tasks, we emphasize that most existing methods did not take into account the distributional shift caused by sample selection bias in the FSL scenario. Such a selection bias can induce spurious correlation between the semantic causal features, that are causally and semantically related to the class label, and the other non-causal features. Critically, the former ones should be invariant across changes in distributions, highly related to the classes of interest, and thus well generalizable to novel classes, while the latter ones are not stable to changes in the distribution. To resolve this problem, we propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency by replacing the patch-level information and supervision of the query images with random gallery images from different classes from the query ones. We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features. To further make these features to be discriminative enough for classification, we propose Correlation-guided Reconstruction (CGR) and Hardness-Aware module for instance discrimination and easier discrimination between similar classes. Moreover, such a framework can be adapted to the unsupervised FSL scenario. The utility of our method is demonstrated on the state-of-the-art results consistently achieved on several benchmarks including miniImageNet, tieredImageNet, CIFAR-FS, CUB, Cars, Places and Plantae, in all settings of single-domain, cross-domain and unsupervised FSL. By studying the intra-variance property of learned features and visualizing the learned features, we further quantitatively and qualitatively show that such a promising result is due to the effectiveness in learning causal features.

Recent advances in lignocellulosic and algal biomass pretreatment and its biorefinery approaches for biochemicals and bioenergy conversion.

As the global demand for sustainable energy increases, lignocellulosic (such as agricultural residues, forest biomass, municipal waste, and dedicated energy crops) and algal (including macroalgae and microalgae) biomass have attracted considerable attention, because of their high availability of carbohydrates. This is a potential feedstock to produce biochemical and bioenergy. Pretreatment of biomass can disrupt their complex structure, increasing conversion efficiency and product yield. Therefore, this review comprehensively discusses recent advances in different pretreatments (physical, chemical, physicochemical, and biological pretreatments) for lignocellulosic and algal biomass and their biorefining methods. Life cycle assessment (LCA) which enables the quantification of the environmental impact assessment of a biorefinery also be introduced. Biorefinery processes such as raw material acquisition, extraction, production, waste accumulation, and waste conversion are all monitored under this concept. Nevertheless, there still exist some techno-economic barriers during biorefinery and extensive research is still needed to develop cost-effective processes.

Exploring Structural Sparsity of Deep Networks Via Inverse Scale Spaces.

The great success of deep neural networks is built upon their over-parameterization, which smooths the optimization landscape without degrading the generalization ability. Despite the benefits of over-parameterization, a huge amount of parameters makes deep networks cumbersome in daily life applications. On the other hand, training neural networks without over-parameterization faces many practical problems, e.g., being trapped in the local optimal. Though techniques such as pruning and distillation are developed, they are expensive in fully training a dense network as backward selection methods; and there is still a void on systematically exploring forward selection methods for learning structural sparsity in deep networks. To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces. Specifically, our method can generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously. This kind of differential inclusion scheme has a simple discretization, dubbed Deep structure splitting Linearized Bregman Iteration (DessiLBI), whose global convergence in learning deep networks could be established under the Kurdyka-Lojasiewicz framework. Particularly, we explore several applications of DessiLBI, including finding sparse structures of networks directly via the coupled structure parameter and growing networks from simple to complex ones progressively. Experimental evidence shows that our method achieves comparable and even better performance than the competitive optimizers in exploring the sparse structure of several widely used backbones on the benchmark datasets. Remarkably, with early stopping, our method unveils "winning tickets" in early epochs: the effective sparse network structures with comparable test accuracy to fully trained over-parameterized models, that are further transferable to similar alternative tasks. Furthermore, our method is able to grow networks efficiently with adaptive filter configurations, demonstrating the good performance with much less computational cost. Codes and models can be downloaded at https://github.com/DessiLBI2020/DessiLBI.

Thermally Activated Delayed Fluorescence of a Pyromellitic Diimide Derivative in the Film Environment Investigated by Combined QM/MM and MS-CASPT2 Methods.

Arylene diimide compounds exhibit thermally activated delayed fluorescence (TADF), but its mechanism remains elusive. Herein we studied the TADF mechanism of a carbazole-substituted pyromellitic diimide derivative (CzPhPmDI) in poly(methyl methacrylate) (PMMA) film by using DFT, TD-DFT, and MS-CASPT2 methods within the QM/MM framework. We found that the TADF mechanism involves three electronic states (i.e., S0, S1, and T1), but the T2 state is not involved because its energy is higher than the S1 state by 6.9 kcal/mol. By contrast, the T1 state is only 3.2 kcal/mol lower than the S1 state and such small energy difference benefits the reverse intersystem crossing (rISC) process from T1 to S1 thereto TADF. This point is seconded by relevant radiative and nonradiative rates calculated. At room temperature, the ISC rate from S1 to T1 is calculated to be 6.1 × 106 s-1, which is larger than the fluorescence emission rate, 2.2 × 105 s-1; thus, the dominant S1 population converts to the T1 state. However, in the T1 state, the rISC process (1.8 × 104 s-1) becomes the most important channel because of the negligible phosphorescence emission rate (3.5 × 10-2 s-1). So, the T1 population is still converted back to the S1 state to fluoresce enabling TADF. Unfortunately, the rISC process is blocked in low temperature. Besides, we found that relevant Huang-Rhys factors have dominant contribution from low-frequency vibrational motion related to the torsional motion of functional groups. These gained insights could provide useful information for the design of organic TADF materials with excellent luminescence efficiency.

Bacteroides propionicigenes sp. nov., isolated from human faeces.

An anaerobic bacterial strain, designated as NSJ-90T, was isolated from the faeces of a healthy adult in China. Cells of strain NSJ-90T were Gram-stain-negative, non-motile, non-spore-forming and rod-shaped. Based on 16S rRNA gene sequence analysis, strain NSJ-90T belonged to the genus Bacteroides and was phylogenetically closely related to Bacteroides clarus YIT 12056T (16S rRNA gene identity was 97.04 %). The DNA G+C content of strain NSJ-90T was 44.85 mol% (calculated from the genome). The average nucleotide identity between strain NSJ-90T and B. clarus YIT 12056T was 87.60 %. The major cellular fatty acids (>10 %) of strain NSJ-90T were iso-C15 : 0, anteiso-C15 : 0 and iso-C17 : 0 3-OH. Menaquinone-10 was detected as the respiratory quinone. The major products of glucose fermentation were acetic, propionic and isovaleric acids. Based on its phylogenetic, phenotypic and chemotaxonomic characteristics, we propose that strain NSJ-90T represents a novel species of the genus Bacteroides, for which the name Bacteroides propionicigenes sp. nov. is proposed. The type strain is NSJ-90T (=CGMCC 1.17886T=KCTC 25305T).

Quantifiable models for surface protonic conductivity in porous oxides - case of monoclinic ZrO2.

The surface protonic conductivity of porous monoclinic ZrO2 sintered at temperatures in the range 700-1100 °C yielding relative densities of around 60% and grain sizes of approximately 160 nm has been studied using impedance spectroscopy as a function of temperature well below the sintering temperature in wet atmospheres (pH2O = 0.03 bar). The sum of two high-frequency impedance responses is argued to represent surface conductance according to a new model of impedance over curved surfaces. A simple brick layer model is applied to compare the measured macroscopic conductivities with predicted surface conductances. The well-faceted samples sintered at the highest temperatures exhibited activation enthalpies up to 58 kJ mol-1 of surface protonic conduction in wet atmospheres at temperatures above 300 °C. We attribute this to the mobility of dissociated protons over surface oxide ions, and the high preexponential is in good agreement with a model comprising relatively strong dissociative chemisorption. With decreasing sintering temperature, the particles appear more rounded, with less developed facets, and we obtain activation enthalpies of surface protonic conduction in the chemisorbed layer down to around 30 kJ mol-1, with correspondingly smaller preexponentials and an observed dependency. Supported by the thermogravimetry of adsorption, we attribute this to weaker and more molecular chemisorption on the more randomly terminated less faceted surfaces, providing water layers with fewer dissociated charge carrying protons, but also smaller activation enthalpies of mobility. Below 200 °C, all samples exhibit a strongly inverse temperature dependency characteristic of conduction in the 1st physisorbed layer with increasing coverage. The preexponentials correspond well to the models of physisorption, with dissociation to and proton migration between physisorbed water molecules. The enthalpies fit well to physisorption and with enthalpies of dissociation and proton mobility close to those of liquid water. We have by this introduced models for proton conduction in chemisorbed and physisorbed water on ZrO2, applicable to other oxides as well, and shown that preexponentials are quantitatively assessable in the order-of-magnitude level to discriminate models via a simple brick layer model based topographical analysis of the ceramic microstructure.

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification.

Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions on one side of breasts rarely appear in the corresponding areas on the other side, we explore to answer a counterfactual question to identify the lesion areas. This counterfactual question means: given an image with lesions, how would the features have behaved if there were no lesions in the image? To answer this question, we derive a new theoretical result based on the symmetric prior. Specifically, by building a causal model that entails such a prior for bilateral images, we identify to optimize the distances in distribution between i) the counterfactual features and the target side's features in lesion-free areas; and ii) the counterfactual features and the reference side's features in lesion areas. To realize these optimizations for better benign/malignant classification, we propose a counterfactual generative network, which is mainly composed of Generator Adversarial Network and a prediction feedback mechanism, they are optimized jointly and prompt each other. Specifically, the former can further improve the classi?cation performance by generating counterfactual features to calculate lesion areas. On the other hand, the latter helps counterfactual generation by the supervision of classification loss. The utility of our method and the effectiveness of each module in our model can be verified by state-of-the-art performance on INBreast and an in-house dataset and ablation studies.

Transcriptome-based stemness indices analysis reveals platinum-based chemo-theraputic response indicators in advanced-stage serous ovarian cancer.

Serous ovarian cancer (SOC) is a main histological subtype of ovarian cancer, in which cancer stem cells (CSC) are responsible for its chemoresistance. However, the underlying modulation mechanisms of chemoresistance led by cancer stemness are still undefined. We aimed to investigate potential drug-response indicators among stemness-associated biomarkers in advanced SOC samples. The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Weighted gene co-expression network analysis (WGCNA) was utilized to explore the gene modules and key genes involved in stemness characteristics. We found that mRNAsi and corrected mRNAsi scores were both greater in tumors of Grade 3 and 4 than that of Grade 1 and 2. Forty-two key genes were obtained from the most significant mRNAsi-related gene module. Functional annotation revealed that these key genes were mainly involved in the mitotic division. Thirteen potential platinum-response indicators were selected from the genes enriched to platinum-response associated pathways. Among them, we identified 11 genes with prognostic value of progression-free survival (PFS) in advanced SOC patients treated with platinum and 7 prognostic genes in patients treated with a combination of platinum and taxol. The expressions of the 13 key genes were also validated between platinum-resistant and -sensitive SOC samples of advanced stages in two Gene Expression Omnibus (GEO) datasets. The results revealed that CDC20 was a potential platinum-sensitivity indicator in advanced SOC. These findings may provide a new insight for chemotherapies in advanced SOC patients clinically.

Knockdown of KIF15 promotes cell apoptosis by activating crosstalk of multiple pathways in ovarian cancer: bioinformatic and experimental analysis.

BACKGROUND: Ovarian cancer (OC) is the most lethal malignancy of women. Unlimited proliferation is a fundamental feature of OC cells. The genes associated with cell proliferation may be histopathologic biomarkers and targets of anti-tumor therapeutic strategies. The present study aimed to identify proliferation-associated biomarkers with prognostic, diagnostic, and therapeutic value and reveal the underlying molecular mechanism of candidate genes involved in OC by a combination of bioinformatic and experimental methods. RESULTS: KIF15 was upregulated in early-stage OC tissues and could predict poor prognosis of patients of Stage I and II. The knockdown of KIF15 significantly inhibited cell proliferation, tumor formation, and growth as well as promoting apoptosis of OC cells. A combination of experimental and bioinformatic analyses revealed KIF15 knockdown promoted cell apoptosis by activating crosstalk of multiple pathways in OC. CONCLUSION: KIF15, an early-stage prognostic gene, was identified as a candidate histopathologic biomarker and therapeutic target of OC.

Prostaglandin E2 serves a dual role in regulating the migration of dendritic cells.

Dendritic cells (DCs) are the most potent antigen­presenting cells, and are indispensable in the immune system. Prostaglandin E2 (PGE2) has been demonstrated to modulate the migration of DCs, but with inconsistent results. The present study, based on our previous research, used murine bone marrow­derived DCs to elucidate the potential regulatory mechanism of PGE2 on the migration of DCs. The results indicated that PGE2 served a dual role in regulating the migration of DCs in a dose­dependent manner. High concentrations of PGE2 inhibited cell migration, whereas low concentrations exhibited the opposite effect. Flow cytometry revealed that the expression of CC chemokine receptor type 7 on the DC surface was increased following treatment with low concentrations of PGE2 and slightly decreased by high concentrations of PGE2. The effect of PGE2 was indicated to be exerted via reorganizing the F­actin cytoskeleton using confocal microscopy. Moreover, the regulatory effect of PGE2 on the migration of DCs was validated in vivo. Subsequent gene expression profile analyses using RNA­sequencing technology indicated that PGE2 induced alterations in the expression of multiple downstream genes and signaling pathway molecules associated with cell migration and the cytoskeleton. These findings may provide an improved understanding on the mechanism of DC migration under both pathological and physiological conditions. Moreover, the biological implications of these findings may provide a novel perspective of the immunological surveillance in the progression of different types of diseases.

Nonadiabatic Dynamics Simulations on Early-Time Photochemistry of Spirobenzopyran.

Photoinduced ring-opening, decay, and isomerization of spirobenzopyran have been explored by the OM2/MRCI nonadiabatic dynamics simulations based on Tully's fewest-switches surface hopping scheme. The efficient S1 to S0 internal conversion as observed in experiments is attributed to the existence of two efficient excited-state decay pathways. The first one is related to the C-N dissociation, and the second one is done to the C-O dissociation. The C-O dissociation pathway is dominant, and more than 90% trajectories decay to the S0 state via the C-O bond-fission related S1/S0 conical intersections. Near these regions in the S0 state, trajectories can either return to spirobenzopyran or proceed to various intermediates including merocyanine via a series of bond rotations. Our nonadiabatic dynamics simulations also demonstrate that the hydrogen-out-of-plane (HOOP) motion is important for efficient and ultrafast excited-state deactivation. On the other hand, we have also found that the replacement of methyl groups by hydrogen atoms in spirobenzopyran can artificially introduce different intramolecular hydrogen transfers leading to hydrogen-transferred intermediates. This finding is important for the community and demonstrates that such a kind of structural truncation, sometimes, could be problematic, leading to incorrect photodynamics. Our present work provides valuable insights into the photodynamics of spirobenzopyran, which could be helpful for the design of spiropyran-based photochromic materials.