EGFR signaling controls directionality of epithelial multilayer formation upon loss of cell polarity.

Apical-basal polarity is maintained by distinct protein complexes that reside in membrane junctions, and polarity loss in monolayered epithelial cells can lead to formation of multilayers, cell extrusion, and/or malignant overgrowth. Yet, how polarity loss cooperates with intrinsic signals to control directional invasion toward neighboring epithelial cells remains elusive. Using the Drosophila ovarian follicular epithelium as a model, we found that posterior follicle cells with loss of lethal giant larvae (lgl) or Discs large (Dlg) accumulate apically toward germline cells, whereas cells with loss of Bazooka (Baz) or atypical protein kinase C (aPKC) expand toward the basal side of wildtype neighbors. Further studies revealed that these distinct multilayering patterns in the follicular epithelium were determined by epidermal growth factor receptor (EGFR) signaling and its downstream target Pointed, a zinc-finger transcription factor. Additionally, we identified Rho kinase as a Pointed target that regulates formation of distinct multilayering patterns. These findings provide insight into how cell polarity genes and receptor tyrosine kinase signaling interact to govern epithelial cell organization and directional growth that contribute to epithelial tumor formation.

Damage-induced regeneration of the intestinal stem cell pool through enteroblast mitosis in the Drosophila midgut.

Many adult tissues and organs including the intestine rely on resident stem cells to maintain homeostasis and regeneration. In mammals, the progenies of intestinal stem cells (ISCs) can dedifferentiate to generate ISCs upon ablation of resident stem cells. However, whether and how mature tissue cells generate ISCs under physiological conditions remains unknown. Here, we show that infection of the Drosophila melanogaster intestine with pathogenic bacteria induces entry of enteroblasts (EBs), which are ISC progenies, into the mitotic cycle through upregulation of epidermal growth factor receptor (EGFR)-Ras signaling. We also show that ectopic activation of EGFR-Ras signaling in EBs is sufficient to drive enteroblast mitosis cell autonomously. Furthermore, we find that the dividing enteroblasts do not gain ISC identity as a prerequisite to divide, and the regenerative ISCs are produced through EB mitosis. Taken together, our work uncovers a new role for EGFR-Ras signaling in driving EB mitosis and replenishing the ISC pool during fly intestinal regeneration, which may have important implications for tissue homeostasis and tumorigenesis in vertebrates.

Bone marrow mesenchymal stem cells-derived exosomes ameliorate LPS-induced acute lung injury by miR-223-regulated alveolar macrophage M2 polarization.

Numerous studies have shown that the M2 polarization of alveolar macrophages (AM) plays a protective role in acute lung injury (ALI). Mesenchymal stem cells (MSCs) secreted exosomes have been reported to be involved in inflammatory diseases by the effects of polarized M1/M2 macrophage populations. However, whether bone marrow mesenchymal stem cells (BMMSCs) derived exosomes could protect from ALI and its mechanisms are still unclear. Here, we explored the role of exosomes from BMMSC in rat AM polarization and the lipopolysaccharide- (LPS-) induced ALI rat model. Furthermore, the levels of exosomal miR-223 in BMMSCs were measured by RT-qPCR. Additionally, miR-223 mimics and its inhibitors were used to verify the vital role of miR-223 of BMMSCs-derived exosomes in the polarization of M2 macrophages. The results showed that BMMSCs-derived exosomes were taken up by the AM. Exosomes derived from BMMSCs promoted M2 polarization of AM in vitro. BMMSCs exosomes effectively mitigated pathological injuries, lung edema, and the inflammation of rats from LPS-induced ALI, accompanied by an increase of M2 polarization of AM in lung tissue. Interestingly, we also found that miR-223 was enriched in BMMSCs-derived exosomes, and overexpression of miR-223 in BMMSCs-derived exosomes promoted M2 polarization of AM while depressing miR-223 showed opposite effects in AM. The present study demonstrated that BMMSCs-derived exosomes triggered alveolar M2 polarization to improve inflammation by transferring miR-223, which may provide new therapeutic strategies in ALI.

Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder.

The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS.

Polyploidy in development and tumor models in Drosophila.

Polyploidy, a cell status defined as more than two sets of genomic DNA, is a conserved strategy across species that can increase cell size and biosynthetic production, but the functional aspects of polyploidy are nuanced and vary across cell types. Throughout Drosophila developmental stages (embryo, larva, pupa and adult), polyploid cells are present in numerous organs and help orchestrate development while contributing to normal growth, well-being and homeostasis of the organism. Conversely, increasing evidence has shown that polyploid cells are prevalent in Drosophila tumors and play important roles in tumor growth and invasiveness. Here, we summarize the genes and pathways involved in polyploidy during normal and tumorigenic development, the mechanisms underlying polyploidization, and the functional aspects of polyploidy in development, homeostasis and tumorigenesis in the Drosophila model.

Developmental regulation of epithelial cell cuboidal-to-squamous transition in Drosophila follicle cells.

Epithelial cells form continuous membranous structures for organ formation, and these cells are classified into three major morphological categories: cuboidal, columnar, and squamous. It is crucial that cells transition between these shapes during the morphogenetic events of organogenesis, yet this process remains poorly understood. All three epithelial cell shapes can be found in the follicular epithelium of Drosophila egg chamber during oogenesis. Squamous cells (SCs) are initially restricted to the anterior terminus in cuboidal shape. They then rapidly become flattened to assume squamous shape by stretching and expansion in 12 â€‹h during midoogenesis. Previously, we reported that Notch signaling activated a zinc-finger transcription factor Broad (Br) at the end of early oogenesis. Here we report that ecdysone and JAK/STAT pathways subsequently converge on Br to serve as an important spatiotemporal regulator of this dramatic morphological change of SCs. The early uniform pattern of Br in the follicular epithelium is directly established by Notch signaling at stage 5 of oogenesis. Later, ecdysone and JAK/STAT signaling activities synergize to suppress Br in SCs from stage 8 to 10a, contributing to proper SC squamous shape. During this process, ecdysone signaling is essential for SC stretching, while JAK/STAT regulates SC clustering and cell fate determination. This study reveals an inhibitory role of ecdysone signaling in suppressing Br in epithelial cell remodeling. In this study we also used single-cell RNA sequencing data to highlight the shift in gene expression which occurs as Br is suppressed and cells become flattened.

A single-cell atlas of adult Drosophila ovary identifies transcriptional programs and somatic cell lineage regulating oogenesis.

Oogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modeled extensively using the Drosophila ovary. Although different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic data set for the adult Drosophila ovary and connected tissues. Using this data set, we identified the transcriptional trajectory of the entire follicle-cell population over the course of their development from stem cells to the oogenesis-to-ovulation transition. We further identify expression patterns during essential developmental events that take place in somatic and germline cell types such as differentiation, cell-cycle switching, migration, symmetry breaking, nurse-cell engulfment, egg-shell formation, and corpus luteum signaling. Extensive experimental validation of unique expression patterns in both ovarian and nearby, nonovarian cells also led to the identification of many new cell type-and stage-specific markers. The inclusion of several nearby tissue types in this data set also led to our identification of functional convergence in expression between distantly related cell types such as the immune-related genes that were similarly expressed in immune cells (hemocytes) and ovarian somatic cells (stretched cells) during their brief phagocytic role in nurse-cell engulfment. Taken together, these findings provide new insight into the temporal regulation of genes in a cell-type specific manner during oogenesis and begin to reveal the relatedness in expression between cell and tissues types.

A comprehensive in vivo screen for anti-apoptotic miRNAs indicates broad capacities for oncogenic synergy.

microRNAs (miRNAs) are ~21-22 nucleotide (nt) RNAs that mediate broad post-transcriptional regulatory networks. However, genetic analyses have shown that the phenotypic consequences of deleting individual miRNAs are generally far less overt compared to their misexpression. This suggests that miRNA deregulation may have broader phenotypic impacts during disease situations. We explored this concept in the Drosophila eye, by screening for miRNAs whose misexpression could modify the activity of pro-apoptotic factors. Via unbiased and comprehensive in vivo phenotypic assays, we identify an unexpectedly large set of miRNA hits that can suppress the action of pro-apoptotic genes hid and grim. We utilize secondary assays to validate that a subset of these miRNAs can inhibit irradiation-induced cell death. Since cancer cells might seek to evade apoptosis pathways, we modeled this situation by asking whether activation of anti-apoptotic miRNAs could serve as "second hits". Indeed, while clones of the lethal giant larvae (lgl) tumor suppressor are normally eliminated during larval development, we find that diverse anti-apoptotic miRNAs mediate the survival of lgl mutant clones in third instar larvae. Notably, while certain anti-apoptotic miRNAs can target apoptotic factors, most of our screen hits lack obvious targets in the core apoptosis machinery. These data highlight how a genetic approach can reveal distinct and powerful activities of miRNAs in vivo, including unexpected functional synergies during disease or cancer-relevant settings.

Time in range in relation to amputation and all-cause mortality in hospitalised patients with diabetic foot ulcers.

AIMS: The aim of this study was to evaluate the association of time in range (TIR) with amputation and all-cause mortality in hospitalised patients with diabetic foot ulcers (DFUs). MATERIALS AND METHODS: A retrospective analysis was performed on 303 hospitalised patients with DFUs. During hospitalisation, TIR, mean blood glucose (MBG), coefficient of variation (CV), time above range (TAR) and time below range (TBR) of patients were determined from seven-point blood glucose profiles. Participants were grouped based on their clinical outcomes (i.e., amputation and death). Logistic regression was employed to analyse the association of TIR with amputation and all-cause mortality of inpatients with DFUs. RESULTS: Among the 303 enrolled patients, 50 (16.5%) had undergone amputation whereas seven (2.3%) were deceased. Blood glucose was determined in 41,012 samples obtained from all participants. Patients who underwent amputation had significantly lower TIR and higher MBG, CV, level 2 TAR and level 1 TBR whereas deceased patients had significantly lower TIR and higher MBG and level 2 TAR. Both amputation and all-cause mortality rate declined with an increase in TIR quartiles. Logistic regression showed association of TIR with amputation (p = 0.034) and all-cause mortality (p = 0.013) after controlling for 15 confounders. This association was similarly significant in all-cause mortality after further adjustment for CV (p = 0.022) and level 1 TBR (p = 0.021), respectively. CONCLUSIONS: TIR is inversely associated with amputation and all-cause mortality of hospitalised patients with DFUs. Further prospective studies are warranted to establish a causal relationship between TIR and clinical outcomes in patients with DFUs.

CRL4Mahj E3 ubiquitin ligase promotes neural stem cell reactivation.

The ability of neural stem cells (NSCs) to transit between quiescence and proliferation is crucial for brain development and homeostasis. Drosophila Hippo pathway maintains NSC quiescence, but its regulation during brain development remains unknown. Here, we show that CRL4Mahj, an evolutionarily conserved E3 ubiquitin ligase, is essential for NSC reactivation (exit from quiescence). We demonstrate that damaged DNA-binding protein 1 (DDB1) and Cullin4, two core components of Cullin4-RING ligase (CRL4), are intrinsically required for NSC reactivation. We have identified a substrate receptor of CRL4, Mahjong (Mahj), which is necessary and sufficient for NSC reactivation. Moreover, we show that CRL4Mahj forms a protein complex with Warts (Wts/large tumor suppressor [Lats]), a kinase of the Hippo signaling pathway, and Mahj promotes the ubiquitination of Wts. Our genetic analyses further support the conclusion that CRL4Mahj triggers NSC reactivation by inhibition of Wts. Given that Cullin4B mutations cause mental retardation and cerebral malformation, similar regulatory mechanisms may be applied to the human brain.

Clinical significance of cyclin-dependent kinase inhibitor 2C expression in cancers: from small cell lung carcinoma to pan-cancers.

BACKGROUND: Cyclin-dependent kinase inhibitor 2C (CDKN2C) was identified to participate in the occurrence and development of multiple cancers; however, its roles in small cell lung carcinoma (SCLC) remain unclear. METHODS: Differential expression analysis of CDKN2C between SCLC and non-SCLC were performed based on 937 samples from multiple centers. The prognosis effects of CDKN2C in patients with SCLC were detected using both Kaplan-Meier curves and log-rank tests. Using receiver-operating characteristic curves, whether CDKN2C expression made it feasible to distinguish SCLC was determined. The potential mechanisms of CDKN2C in SCLC were investigated by gene ontology terms and signaling pathways (Kyoto Encyclopedia of Genes and Genomes). Based on 10,080 samples, a pan-cancer analysis was also performed to determine the roles of CDKN2C in multiple cancers. RESULTS: For the first time, upregulated CDKN2C expression was detected in SCLC samples at both the mRNA and protein levels (p of Wilcoxon rank-sum test < 0.05; standardized mean difference = 2.86 [95% CI 2.20-3.52]). Transcription factor FOXA1 expression may positively regulate CDKN2C expression levels in SCLC. High CDKN2C expression levels were related to the poor prognosis of patients with SCLC (hazard ratio > 1, p < 0.05) and showed pronounced effects for distinguishing SCLC from non-SCLC (sensitivity, specificity, and area under the curve ≥ 0.95). CDKN2C expression may play a role in the development of SCLC by affecting the cell cycle. Furthermore, the first pan-cancer analysis revealed the differential expression of CDKN2C in 16 cancers (breast invasive carcinoma, etc.) and its independent prognostic significance in nine cancers (e.g., adrenocortical carcinoma). CDKN2C expression was related to the immune microenvironment, suggesting its potential usefulness as a prognostic marker in immunotherapy. CONCLUSIONS: This study identified upregulated CDKN2C expression and its clinical significance in SCLC and other multiple cancers, suggesting its potential usefulness as a biomarker in treating and differentiating cancers.

An Improved Density Peak Clustering Algorithm for Multi-Density Data.

Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter dc, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples.

An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer.

Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision-making. We aimed to develop an accurate and explainable prediction model to estimate the risk of in-hospital amputation in patients with DFU. A total of 618 hospitalised patients with DFU were included in this study. The patients were divided into non-amputation, minor amputation or major amputation group. Light Gradient Boosting Machine (LightGBM) and 5-fold cross-validation tools were used to construct a multi-class classification model to predict the three outcomes of interest. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the predictions of the model. Our area under the receiver-operating-characteristic curve (AUC) demonstrated a 0.90, 0.85 and 0.86 predictive ability for non-amputation, minor amputation and major amputation outcomes, respectively. Taken together, our data demonstrated that the developed explainable machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors.

The amputation and mortality of inpatients with diabetic foot ulceration in the COVID-19 pandemic and postpandemic era: A machine learning study.

This study aimed to explore the clinical characteristic and outcomes of inpatients with diabetic foot ulceration (DFU) in 2019 (prelockdown) and 2020 (postlockdown) due to the COVID-19 pandemic, at an emergency medical service unit. Prediction models for mortality and amputation were developed to describe the risk factors using a machine learning-based approach. Hospitalized DFU patients (N = 23) were recruited after the lockdown in 2020 and matched with corresponding inpatients (N = 23) before lockdown in 2019. Six widely used machine learning models were built and internally validated using 3-fold cross-validation to predict the risk of amputation and death in DFU inpatients under the COVID-19 pandemic. Previous DF ulcers, prehospital delay, and mortality were significantly higher in 2020 compared to 2019. Diabetic foot patients in 2020 had higher hs-CRP levels (P = .037) but lower hemoglobin levels (P = .017). The extreme gradient boosting (XGBoost) performed best in all models for predicting amputation and mortality with the highest area under the curve (0.86 and 0.94), accuracy (0.80 and 0.90), sensitivity (0.67 and 1.00), and negative predictive value (0.86 and 1.00). A long delay in admission and a higher risk of mortality was observed in patients with DFU who attended the emergency center during the COVID-19 post lockdown. The XGBoost model can provide evidence-based risk information for patients with DFU regarding their amputation and mortality. The prediction models would benefit DFU patients during the COVID-19 pandemic.

[Bioinformatics Analysis of Hub Genes of Diabetic Foot Ulcer and Their Biofunctions].

Objective: To explore the hub genes associated with the pathogenesis and healing of diabetic foot ulcer (DFU) and their biological functions through bioinformatics analysis of transcriptome sequencing data. Methods: The transcriptome sequencing datasets of DFU were selected from Gene Expression Omnibus (GEO) database, and the data were regrouped and normalized for bioinformatics analysis. The skin transcriptome sequencing datasets of DFU patients were compared with those of normal controls and the transcriptome sequencing datasets of skin from ulcerous wound edge of DFU patients were compared with those of non-ulcerous skin of DFU patients so that differentially expressed genes were identified, pathway enrichment and protein-to-protein interaction (PPI) analyses were performed, hub genes were found through nodal analysis, and receiver operating characteristic (ROC) curve was applied to a testing dataset to validate the diagnostic efficiency of the hub genes related to DFU. The intersecting genes from the two sets of analyses were again subjected to pathway enrichment and PPI analyses to screen for hub genes associated with DFU wound healing. What's more, gene set enrichment analysis (GSEA) was carried out on relevant samples to probe for the possible functions and pathway of non-significant genes in DFU. Results: A total of 620 up-regulated differentially expressed genes and 196 down-regulated differentially expressed genes were identified in the training dataset which compared DFU patients with non-diabetic patients. The functions of these genes were enriched in the metabolism of terpenoids and polyketides, signaling molecules and interaction, phospholipase D signaling pathway, propanoate metabolism, PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, pyrimidine metabolism, IL-17 signaling pathway, Rap1 signaling pathway, etc. A total of 10 hub genes were identified with the PPI network. Among them, BGN's value of the area under the curve of ROC analysis was 0.714 and CCND1's was 0.712. In the sequencing analysis of ulcerous wound edge of DFU patients and non-ulcerous skin of DFU patients, 4072 up-regulated genes and 911 down-regulated genes were identified, of which, 372 genes were also detected in the differentially expressed genes of DFU. The functions of these differentially expressed genes were enriched in phospholipase D signaling pathway, xenobiotics biodegradation and energy metabolism, glutathione metabolism, pyrimidine metabolism, ErbB signaling pathway, melanin production, etc. A total of 7 hub genes were identified from PPI network. In GSEA analysis, pathways including pentose and glucuronate interconversions and homologous recombination, nicotinate and nicotinamide metabolism, neuroactive ligand receptor interaction, maturity-onset diabetes of the young, butanoate metabolism, lysine degradation, pantothenate and coenzyme A biosynthesis, riboflavin metabolism, steroid hormone biosynthesis, and valine, leucine and isoleucine degradation showed significant expression differences between DFU patients and normal controls. Conclusion: Bioinformatics analysis results suggest that BGN and CCND1 are potential biomarkers for predicting DFU; CXCL12, TLR4, JAK2, PPARA, UBC, DCN, KDR, and ARNTL are the hub genes of DFU, while CXCL8, CXCL12, TXN, SLIT3, KRT14, KIT, and NEO1 are the hub genes related to wound healing of DFU.

The Hox proteins Ubx and AbdA collaborate with the transcription pausing factor M1BP to regulate gene transcription.

In metazoans, the pausing of RNA polymerase II at the promoter (paused Pol II) has emerged as a widespread and conserved mechanism in the regulation of gene transcription. While critical in recruiting Pol II to the promoter, the role transcription factors play in transitioning paused Pol II into productive Pol II is, however, little known. By studying how Drosophila Hox transcription factors control transcription, we uncovered a molecular mechanism that increases productive transcription. We found that the Hox proteins AbdA and Ubx target gene promoters previously bound by the transcription pausing factor M1BP, containing paused Pol II and enriched with promoter-proximal Polycomb Group (PcG) proteins, yet lacking the classical H3K27me3 PcG signature. We found that AbdA binding to M1BP-regulated genes results in reduction in PcG binding, the release of paused Pol II, increases in promoter H3K4me3 histone marks and increased gene transcription. Linking transcription factors, PcG proteins and paused Pol II states, these data identify a two-step mechanism of Hox-driven transcription, with M1BP binding leading to Pol II recruitment followed by AbdA targeting, which results in a change in the chromatin landscape and enhanced transcription.

Inhibition of Notch signaling by the p105 and p180 subunits of Drosophila chromatin assembly factor 1 is required for follicle cell proliferation.

Chromatin assembly factor 1 (CAF1), a histone chaperone that mediates the deposition of histone H3/H4 onto newly synthesized DNA, is involved in Notch signaling activation during Drosophila wing imaginal disc development. Here, we report another side of CAF1, wherein the subunits CAF1-p105 and CAF1-p180 (also known as CAF1-105 and CAF1-180, respectively) inhibit expression of Notch target genes and show this is required for proliferation of Drosophila ovarian follicle cells. Loss-of-function of either CAF1-p105 or CAF1-p180 caused premature activation of Notch signaling reporters and early expression of the Notch target Hindsight (Hnt, also known as Pebbled), leading to Cut downregulation and inhibition of follicle cell mitosis. Our studies further show Notch is functionally responsible for these phenotypes observed in both the CAF1-p105- and CAF1-p180-deficient follicle cells. Moreover, we reveal that CAF1-p105- and CAF1-p180-dependent Cut expression is essential for inhibiting Hnt expression in follicle cells during their mitotic stage. These findings together indicate a novel negative-feedback regulatory loop between Cut and Hnt underlying CAF1-p105 and CAF-p180 regulation, which is crucial for follicle cell differentiation. In conclusion, our studies suggest CAF1 plays a dual role to sustain cell proliferation by positively or negatively regulating Drosophila Notch signaling in a tissue-context-dependent manner.

Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation.

It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.

Epithelial Tumors Originate in Tumor Hotspots, a Tissue-Intrinsic Microenvironment.

Malignant tumors are caused by uncontrolled proliferation of transformed mutant cells that have lost the ability to maintain tissue integrity. Although a number of causative genetic backgrounds for tumor development have been discovered, the initial steps mutant cells take to escape tissue integrity and trigger tumorigenesis remain elusive. Here, we show through analysis of conserved neoplastic tumor-suppressor genes (nTSGs) in Drosophila wing imaginal disc epithelia that tumor initiation depends on tissue-intrinsic local cytoarchitectures, causing tumors to consistently originate in a specific region of the tissue. In this "tumor hotspot" where cells constitute a network of robust structures on their basal side, nTSG-deficient cells delaminate from the apical side of the epithelium and begin tumorigenic overgrowth by exploiting endogenous Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling activity. Conversely, in other regions, the "tumor coldspot" nTSG-deficient cells are extruded toward the basal side and undergo apoptosis. When the direction of delamination is reversed through suppression of RhoGEF2, an activator of the Rho family small GTPases, and JAK/STAT is activated ectopically in these coldspot nTSG-deficient cells, tumorigenesis is induced. These data indicate that two independent processes, apical delamination and JAK/STAT activation, are concurrently required for the initiation of nTSG-deficient-induced tumorigenesis. Given the conservation of the epithelial cytoarchitecture, tumorigenesis may be generally initiated from tumor hotspots by a similar mechanism.

Drosophila Model in Cancer: An Introduction.

Cancer is a cumulative manifestation of several complicated disease states that affect multiple organs. Over the last few decades, the fruit fly Drosophila melanogaster, has become a successful model for studying human cancers. The genetic simplicity and vast arsenal of genetic tools available in Drosophila provides a unique opportunity to address questions regarding cancer initiation and progression that would be extremely challenging in other model systems. In this chapter we provide a historical overview of Drosophila as a model organism for cancer research, summarize the multitude of genetic tools available, offer a brief comparison between different model organisms and cell culture platforms used in cancer studies and briefly discuss some of the latest models and concepts in recent Drosophila cancer research.