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Abstract The mislocalization of proteins leads to breast cancer, one of the world’s most prevalent cancers, which can be identified from immunohistochemical images. Here, based on the deep learning framework, location prediction models were constructed using the features of breast immunohistochemical images. Ultimately, six differentially localized proteins that with stable differentially predictive localization, maximum localization differences, and whose predicted results are not affected by removing a single image are obtained (CCNT1, NSUN5, PRPF4, RECQL4, UTP6, ZNF500). Further verification reveals that these proteins are not differentially expressed, but are closely associated with breast cancer and have great classification performance. Potential mechanism analysis shows that their co-expressed or co-located proteins and RNAs may affect their localization, leading to changes in interactions and functions that further causes breast cancer. They have the potential to help shed light on the molecular mechanisms of breast cancer and provide assistance for its early diagnosis and treatment.

Abstract Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.

Endoplasmic reticulum (ER) stress is a state in which misfolded or unfolded proteins accumulate in the lumen of the ER as a result of some exogenous or endogenous factors. It plays a crucial role in the pathogenesis of malignancies, affecting cell survival, proliferation, and metastasis in cancer. ER stress genes could provide new ideas for potential therapeutic targets in cancer. In our study, we aimed to construct an ER stress-related genes (ERGs) model for hepatocellular carcinoma (HCC). ERGs with differential expression and significant survival were screened to construct a prognostic model. The effectiveness of the model was successfully validated by external datasets. High and low-risk groups were classified based on risk scores. Functional analysis showed risk groups involved in the unfolded protein response, DNA repair, and other differential pathways. When compared to patients with low risk, the prognosis for HCC patients in the high-risk group might be worsened by disruptions in these pathways. Importantly, we considered genomic druggability and predicted drugs. Sorafenib-induced autophagy in HCC cells through an ES stress mechanism. Sorafenib was more sensitive for high-risk patients. In brief, our model predicted the prognosis of HCC and provided novel treatment strategies for the study of other cancers.

Conservation tillage is believed to promote soil aggregate stability, carbon (C) and nitrogen (N) sequestration, but the underlying mechanisms remain unclear. In this study, soil samples from an 18-year experiment including conventional tillage with straw removal (CT), deep scarification with straw mulching (DS), and no-tillage with straw mulching (NT) were used to obtain different fractions based on a comprehensive wet-sieving method of aggregate and particle size. The results showed that NT and DS increased soil organic carbon (SOC) and N by 9.3–16.4% and 10.8–25.8%, respectively, in addition to increasing the weight proportion of macroaggregates and the contribution of macroaggregate-associated C and N to total SOC and N. The C change in the total POM accounted for 77.4% and 79.9% of the total SOC increase by NT and DS, while the MAOM only accounted for 29.2% and 25.2%, respectively. Meanwhile, microaggregates-within-macroaggregates accounted for 96.9% and 90.5% of the SOC increase by NT and DS, respectively. The total SOC and N were positively correlated with the C and N of the macroaggregates and subfractions. In conclusion, the formation of macroaggregates drives soil C and N sequestration under conservation tillage, and POM and mM were important functional pools in this process.

BackgroundTillage practices can substantially affect soil properties depending on crop stage. The interaction between tillage and crop growth on arbuscular mycorrhizal fungi (AMF) communities remains unclear. We investigated the interactions between four tillage treatments (CT: conventional tillage, RT: reduced tillage, NT: no tillage with mulch, and SS: subsoiling with mulch), maintained for 25 years, and two wheat growth stages (elongation stage and grain filling stage) on AMF diversity and community composition.ResultsThe AMF community composition strongly changed during wheat growth, mainly because of changes in the relative abundance of dominant genera Claroideoglomus, Funneliformi, Rhizophagu, Entrophospora, and Glomus. Co-occurrence network analysis revealed that the grain filling stage had a more complex network than the elongation stage. Redundancy analysis results showed that keystone genera respond mainly to changes in soil organic carbon during elongation stage, whereas the total nitrogen content affected the keystone genera during grain filling. Compared with CT, the treatments with mulch, i.e., NT and SS, significantly changed the AMF community composition. The change of AMF communities under different tillage practices depended on wheat biomass and soil nutrients. NT significantly increased the relative abundances of Glomus and Septoglomus, while RT significantly increased the relative abundance of Claroideoglomus.ConclusionOur findings indicate that the relative abundance of dominant genera changed during wheat growth stages. Proper tillage practices (e.g., NT and SS) benefit the long-term sustainable development of the Loess Plateau cropping systems.

Abstract Background Cell‐cell communication by carcinoma‐derived exosomes can influence the tumor microenvironment (TME) and regulate cancer progression. Based on the overexpression of microRNA‐21‐5p (miR‐21) in plasma from patients diagnosed with esophageal squamous cell carcinoma (ESCC) and exosomes from ESCC cell lines identified earlier, this study aimed to explore the influence of exosomal miR‐21 within the TME. Method ScRNA‐Seq and Bulk RNA‐Seq were integrated to elucidate the communication between cancer and endothelial cells. The functionality and mechanisms by which exo‐miR‐21 derived from carcinoma regulate endothelial cell‐mediated angiogenesis were assessed using a cocultivation model of EC9706 cells and recipient human umbilical vein endothelial cells (HUVECs), through blood vessel formation experiments, luciferase reporter assays, RT‐qPCR, and western blot analysis. Result A total of 3842 endothelial cells were extracted from the scRNA‐seq data of ESCC samples and reclustered into five cell subtype. Cell‐cell communication analysis revealed cancer cells presented a strong interaction with angiogenesis‐like endothelial cells in secreted signaling. MiR‐21 was unregulated in ESCC and the carcinoma‐derived exo‐miR‐21 was significantly raised in HUVECs. The exo‐miR‐21 promoted the proliferation and migration of HUVECs while also enhancing, closed mesh count, and junction number in HUVECs. Mechanistically, dual‐luciferase reporter assay revealed that PTEN was the target of miR‐21. Meanwhile, p‐Akt was significantly increased and suppressed by inhibition of miR‐21 and PI3K inhibitor LY294002. Conclusion Exo‐miR‐21‐mediated communication between endothelial and cancer cells plays a pivotal role in promoting the angiogenesis of ESCC. Therefore, controlling exo‐miR‐21 could serve as a novel therapeutic strategy for ESCC by targeting angiogenesis.

The application of potassium fertilizer application and straw return are effective agronomic measures for increasing crop productivity; however, information on how straw return—when substituting potassium fertilizer—affects crop yield, efficiency, and quality in dryland remains limited. In this study, an experiment on a dryland summer maize and winter wheat rotation system was initiated in 2007. This study included four treatments: CK (no fertilizer and no straw return), NP (nitrogen and phosphorus fertilizer application without straw return), NPK (nitrogen, phosphorus, and potassium fertilizer application without straw return), and NPS (NP treatment with straw return, substituting potassium fertilizer as used in the NPK treatment). These treatments were employed to assess grain yield and fertilizer agronomic efficiency in 2015–2020. Additionally, we evaluated the content of nitrogen (N), phosphorus (P), and potassium (K), as well as the protein content and protein yield in maize and wheat grains and the protein components in wheat grains in 2019–2020. The results showed that compared to the CK treatment, NP, NPK, and NPS treatments not only significantly increased the yield, protein yield, and fertilizer agronomic efficiency in both maize and wheat but also increased the content of protein and protein components in wheat grains. Compared to the NP treatment, the NPK treatment significantly increased the contents of N, K, globulin, and gluten in wheat grains by 5.11%, 21.59%, 10.06%, and 15.14%. Compared to NPK treatment, NPS treatment significantly increased the average yield of summer maize by 21.33% and 20.91%, respectively, as well as the annual yield by 9.99% and 13.59%, the N fertilizer agronomic efficiency of summer maize by 132.47%, and the annual N and P fertilizer agronomic efficiency by 42.83% and 64.36%, over the five-year period. The NPS treatment also significantly increased the summer maize protein yield and annual protein yield by 10.43% and 23.08%, as well as the content of protein components, the protein content, and P content in wheat grains by 4.93–13.58%, 7.81%, and 28.89%, respectively. In conclusion, NPS treatment can not only enhance crop yield, protein yield, and fertilizer agronomic efficiency in summer maize annually but also has the advantage of promoting wheat quality. NPS is an efficient strategy to improve crop yield, efficiency, and quality in a dryland maize–wheat rotation system.

There are many schools of Chinese martial arts routines and complex movements; research on this topic is more geared toward Taijiquan (a kind of traditional Chinese shadow boxing), which is a more well-known type of martial arts. Therefore, the purpose of this paper is to visually analyze the research of Chinese martial arts routines based on the knowledge graph method and to propose a knowledge graph method based on the fuzzy set theory, which is called the transF model throughout this paper. The transF model used the fuzzy relational operation of vectors to not only reduce the computational complexity, but to also better integrate multi-dimensional data, especially when the training set is not particularly sufficient. For the visual analysis of Chinese martial arts routines, this paper selected the 16-year data from 2005 to 2020 as the analysis sample, analyzed high-yield institutions and high-yield authors, and conducted a centrality analysis of the whole dataset. From the structure of the knowledge graph, traditional martial arts are the core part of Chinese martial arts, with a centrality of 0.14. Competitive martial arts are the main branch of Chinese martial arts and the third core after Tai Chi and traditional martial arts, with a centrality of 0.41, which is higher than that of traditional martial arts. This shows its importance in martial arts research.

The occurrence of tree flash faults in hybrid overhead–underground lines presents a significant challenge to the smooth operation of power systems. However, research on localizing such faults is relatively scarce. This study conducts theoretical analyses on the formation of tree flash faults, constructs a tree flash fault discharge test platform, and simulates the discharge process. The tree flash fault discharge traveling wave signals were obtained through a high-frequency current acquisition system. Additionally, this paper establishes a model for the current traveling wave of tree flash faults and analyzes transmission attenuation. To enhance the bi-terminal traveling wave localization method, we introduce modal decomposition and the Hilbert–Huang transform. Modal decomposition is used to disentangle signals and derive the instantaneous frequencies of modal signal components through the Hilbert–Huang transform. This process helps determine the time at which the initial wavefront reaches the terminals of the mixed-line transmission. The simulation analysis carried out using PSCAD/EMTDC v4.6.3 demonstrates that this method effectively calibrates the wavefront timing of tree flash fault signals without requiring knowledge of their wave velocity along the mixed-line transmission. Therefore, this approach achieves precise localization of tree flash faults efficiently.

IntroductionLung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.MethodsThis study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.ResultsFinally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.ConclusionTo summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

IntroductionCocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction.MethodsIn this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein–protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established.ResultsFour key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction.DiscussionThis study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.

Abstract Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

There is currently a lack of efficient heat transfer analysis methodologies for spiral channel regenerative cooling that has been increasingly applied in liquid rocket engines. To figure out the heat transfer characteristics of the spiral channel regenerative cooling thrust chamber, a simple 1D method based on the traditional semi-empirical formula after correcting the flow velocity is proposed. The accuracy of this approach is verified by the 3D numerical simulation. The verified method is further used to analyze the distribution of inner wall temperature in the test case and optimize the channel’s parameters. The research shows that the maximum inner wall temperature cooled by the spiral channel is 8.5% lower than that of the straight channel under the same channel size and boundary condition, indicating that the application of the spiral channel significantly improves the cooling effect. In addition, the 1D model combined with the second-order response surface model is employed to optimize the channel width, channel height, pitch, and inner wall thickness aiming for the best cooling effect. The calculated maximum temperature of the inner wall after the structure optimization is 586.6 K, which is 29.8% lower than the initial structure before optimization.

Objetivo: Evaluación sistemática de la eficacia clínica de la punción estereotáctica mínimamente invasiva para la evacuación de hematomas intracraneales en pacientes con hemorragia cerebral. Material y métodos: Se realizaron búsquedas en estudios relevantes en PubMed, Web of Science, MEDLINE, Infraestructura Nacional de Conocimiento de China, base de datos Wanfang y base de datos VIP. El metanálisis se realizó después de la selección de criterios de inclusión y exclusión, la extracción de datos y la evaluación de la calidad de la literatura. Resultados: Se incluyeron 15 estudios en los que participaron 1.312 sujetos, 673 en el grupo experimental y 639 en el grupo control. En comparación con la Craneotomía tradicional o el tratamiento, el aclaramiento estereotáctico mínimamente invasivo de hematomas intracraneales tiene una alta eficiencia clínica total en pacientes con hemorragia intracerebral y puede acortar significativamente el tiempo de hospitalización de los pacientes con hemorragia intracerebral. El nivel de actividad de la vida diaria postoperatoria (ADL) aumentó significativamente, la incidencia de complicaciones postoperatorias disminuyó y la mortalidad disminuyó. Sin embargo, no hubo diferencia significativa en el grado de déficit neurológico postoperatorio. Conclusión: En comparación con la Craneotomía tradicional o el tratamiento conservador, la Craneotomía estereotáctica mínimamente invasiva tiene un mayor efecto clínico en el tratamiento de la hemorragia cerebral y puede mejorar la capacidad de la vida diaria de los pacientes después de la operación.

Stomach, liver, and colon cancers are the most common digestive system cancers leading to mortality. Cancer leader genes were identified in the current study as the genes that contribute to tumor initiation and could shed light on the molecular mechanisms in tumorigenesis. An integrated procedure was proposed to identify cancer leader genes based on subcellular location information and cancer-related characteristics considering the effects of nodes on their neighbors in human protein–protein interaction networks. A total of 69, 43, and 64 leader genes were identified for stomach, liver, and colon cancers, respectively. Furthermore, literature reviews and experimental data including protein expression levels and independent datasets from other databases all verified their association with corresponding cancer types. These final leader genes were expected to be used as diagnostic biomarkers and targets for new treatment strategies. The procedure for identifying cancer leader genes could be expanded to open up a window into the mechanisms, early diagnosis, and treatment of other cancer types.

Detecting defects in grounded flat steel is essential for ensuring the safety and reliability of transmission tower grounding devices. However, traditional inspection methods, such as physical excavation and verification, are costly and time-consuming. This paper proposes a corrosion defect detection method for flat steel transmission tower grounding devices based on electromagnetic ultrasonic SH waves. In addition, using commercial software, a three-dimensional finite element simulation model of grounded flat steel with simulated pitting corrosion defects is constructed. The specified displacements applied to multiple surface sources mimic the horizontal shear vibrations generated by the electromagnetic ultrasonic transducer on the surface of the grounded flat steel during actual inspection. A simulation was used to investigate the propagation and attenuation characteristics of shear-horizontal ultrasonic SH0guided waves for simulated corrosion defects with various geometric configurations in grounded flat steel. The simulation investigated the propagation and attenuation characteristics of the SH0 wave in grounded flat steel and the detection of various defects for linear analysis of the results. The simulation results show that the attenuation of the electromagnetic ultrasonic guided wave is small, at only 0.0016 dB/mm, and the displacement amplitude of the echo signal decreases with the increase of the SH0 wave propagation distance. Increasing the depth and length of corrosion defects increases the echo signal amplitude. At the same time, the width of corrosion defects has little effect on the echo amplitude. Finally, a flat steel defect detection experiment was conducted, and the experimental results fit with the simulation to verify the accuracy of the simulation model. This detection method introduces a new idea for the on-site detection and quantitative identification of corrosion defects in grounded flat steel, which has significant reference value and can provide a more effective and economical method for ensuring the safety and dependability of transmission tower grounding devices.

Recent studies have shown that a super-resolution generative adversarial network (SRGAN) can significantly improve the quality of single-image super-resolution. However, existing SRGAN methods also have certain drawbacks, such as an insufficient feature utilization, a large number of parameters. To further enhance the visual quality, we thoroughly studied three key components of SRGAN, i.e., the network architecture, adversarial loss, and perceptual loss, and propose a DenseNet with Residual-in-Residual Bottleneck Block (RRBB), called a residual bottleneck dense network (RBDN), for single-image super-resolution. First, to improve the utilization of features between the various layers of the network, we adopted a dense cascading connection between layers. At the same time, to reduce the computational cost, we added a bottleneck structure to each layer, greatly reducing the number of network parameters and accelerating the convergence speed of the training process. Second, the proposed RRBB, as the basic network building unit, removes the batch normalization (BN) layer and employs the ELU function to reduce the opposite effects in the absence of BN. In addition, we applied an improved overall loss function during the model training process to stably train the model and further improve the realism of the reconstructed high-resolution image. To prove the superiority of our proposed model, we conducted a comprehensive and objective evaluation of the Peak Signal-to-Noise Ratio, structural similarity, learned perceptual image patch similarity, and other evaluation indicators obtained from the three test sets, i.e., Set5, Set14, and BSD100, from the recent state-of-the-art model. Finally, we conducted qualitative and quantitative analyses of the results obtained in terms of the evaluation indicators, the authenticity of the restored HR images, and textural details, which show the superiority of the RBDN model.

Abstract Background Lung cancer is a leading cause of death from cancer worldwide, especially non-small cell lung cancer (NSCLC). The marker of progression in lung adenocarcinoma, the main type of NSCLC, has been rarely studied. Programmed death 1 (PD-1) is an effective drug target for the treatment of NSCLC. Our study aimed to examine the PD-1 role in the disease process. The study of the effect of polymorphisms on the progression of lung adenocarcinoma in the Han population of Northeast China may provide a valuable reference for the research and application of these drugs. Methods Chi-square test, Wilcoxon rank sum test, and classification efficiency assessment were used to test SNPs of PD-1 in 287 patients and combined with clinical information. Results We successfully identified biomarkers (rs2227981, rs2227982, and rs3608432) that could distinguish between lung adenocarcinoma patients of early stages and late stages. Multiple clinical indicators showed significant differences among different SNPs and cancer stages. Furthermore, this gene was confirmed to effectively distinguish the stages of lung adenocarcinoma with RNA-seq data in TCGA. Conclusions Out study indicated that the PD-1 gene and the SNPs on it could be used as markers for distinguishing lung adenocarcinoma staging in the Northeast Han population. Our investigation into the link between PD-1 polymorphisms and lung adenocarcinoma would help to provide guidance for the treatment and prognosis of lung adenocarcinoma.

Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. First, the mRNAs, lncRNAs and miRNAs with significant differences in the expression levels between tumor and non-tumor tissues were obtained. MRNA DNN models were established and optimized to mine candidate mRNAs that significantly contributed to the DNN models and were in the center of an interaction network. Another DNN model was then constructed and potential ceRNAs were screened out based on the contribution of each RNA to the model. Finally, three modules comprised of miRNAs and their regulated mRNAs and lncRNAs with the same regulation direction were identified as regulatory modules that regulated the initiation of lung adenocarcinoma through ceRNAs relationships. They were validated by literature and functional enrichment analysis. The effectiveness of these regulatory modules was evaluated in an independent lung adenocarcinoma dataset. Regulatory modules for lung adenocarcinoma identified in this study provided a reference for regulatory mechanisms during carcinogenesis.

Milling plays a significant role in the machining of aluminum alloy parts. However, in the milling process, the material easily adheres to the tip of the milling tool due to the high ductility and thermal deformation of the aluminum alloy; the machining efficiency and quality are hard to further improve. Hence, in order to promote the machining performances, three textures (linear, wavy, and micropitted) are prepared, respectively, on rake face of milling tools using the laser processing system. Then, milling load, top-burrs, and surface quality are chosen to discuss the machining performance of textured milling tools. In particular, the top-burrs are investigated by relative area ratio of top-burrs (RARB) which is calculated from the micrograph after binarization. The obtained results evidently show that the wavy textured milling tool reduces the milling load by an amount of 10.7% compared to the nontextured milling tool. This is due to the smaller contact area that reduces the internal friction of the tool-chip contact area and thus reduces the load. And surface roughness improves by an amount of 23.8% because the wavy texture has the largest proportion of the unit area, and it can effectively improve the storage capacity of debris. Therefore, the wavy texture proposed in this research is of great reference value for the optimization of the machining performance of the milling tool.

Background and objective MicroRNAs were important regulators of tumors and the expression of miRNA-221 was associated with malignant proliferation and invasion in many tumors. The aim of this study is to explore the expression of miRNA-221 in non-small cell lung cancer (NSCLC) tissues and its correlation with prognosis. Methods We retrospectively analyzed the clinical characteristics of 117 NSCLC patients who underwent surgery in our hospital from November 2005 to January 2007. Real-time PCR was used to detect the expression of miRNA-221. Chi-square test was utilized to analyze the relationship between miRNA-221 expression and clinicopathologic features. Survival curves were plotted by using the Kaplan-Meier method and compared by the Log-rank test. A Cox proportional hazard regression model was applied to examine the joint effect of covariants. A P-value less than 0.05 was evaluated as statistically significant. Results The patients were divided into two groups according to the relatively expression of miRNA-221. No statistically significance was observed between the expression of miRNA-221 and the clinicopathologic parameters, including gender (χ2=0.070, P=0.791), histology (χ2=0.414, P=0.520), p-TNM stage (χ2=0.068, P=0.794) and history of smoking (χ2=0.206, P=0.650). Kaplan-Meier analysis showed that high expression of miRNA-221 was closely associated with a shorter survival time (P

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.

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