High-Voltage Spinel Cathode Materials: Navigating the Structural Evolution for Lithium-Ion Batteries.

High-voltage LiNi0.5Mn1.5O4 (LNMO) spinel oxides are highly promising cobalt-free cathode materials to cater to the surging demand for lithium-ion batteries (LIBs). However, commercial application of LNMOs is still challenging despite decades of research. To address the challenge, the understanding of their crystallography and structural evolutions during synthesis and electrochemical operation is critical. This review aims to illustrate and to update the fundamentals of crystallography, phase transition mechanisms, and electrochemical behaviors of LNMOs. First, the research history of LNMO and its development into a LIB cathode material is outlined. Then the structural basics of LNMOs including the classic and updated views of the crystal polymorphism, interconversion between the polymorphs, and structure-composition relationship is reviewed. Afterward, the phase transition mechanisms of LNMOs that connect structural and electrochemical properties are comprehensively discussed from fundamental thermodynamics to operando dynamics at intra- and inter-particle levels. In addition, phase evolutions during overlithiation as well as thermal-/electrochemical-driven phase transformations of LNMOs are also discussed. Finally, recommendations are offered for the further development of LNMOs as well as other complex materials to unlock their full potential for future sustainable and powerful batteries.

Silicon dioxide nanoparticles enhance plant growth, photosynthetic performance, and antioxidants defence machinery through suppressing chromium uptake in Brassica napus L.

Chromium (Cr) is a highly toxic heavy metal that is extensively released into the soil and drastically reduces plant yield. Silicon nanoparticles (Si NPs) were chosen to mitigate Cr toxicity due to their ability to interact with heavy metals and reduce their uptake. This manuscript explores the mechanisms of Cr-induced toxicity and the potential of Si NPs to mitigate Cr toxicity by regulating photosynthesis, oxidative stress, and antioxidant defence, along with the role of transcription factors and heavy metal transporter genes in rapeseed (Brassica napus L.). Rapeseed plants were grown hydroponically and subjected to hexavalent Cr stress (50 and 100 µM) in the form of K2Cr2O7 solution. Si NPs were foliar sprayed at concentrations of 50, 100 and 150 µM. The findings showed that 100 µM Si NPs under 100 µM Cr stress significantly increased the leaf Si content by 169% while reducing Cr uptake by 92% and 76% in roots and leaves, respectively. The presence of Si NPs inside the plant leaf cells was confirmed by using energy-dispersive spectroscopy, inductively coupled plasma‒mass spectrometry, and confocal laser scanning microscopy. The study's findings showed that Cr had adverse effects on plant growth, photosynthetic gas exchange attributes, leaf mesophyll ultrastructure, PSII performance and the activity of enzymatic and nonenzymatic antioxidants. However, Si NPs minimized Cr-induced toxicity by reducing total Cr accumulation and decreasing oxidative damage, as evidenced by reduced ROS production (such as H2O2 and MDA) and increased enzymatic and nonenzymatic antioxidant activities in plants. Interestingly, Si NPs under Cr stress effectively increased the NPQ, ETR and QY of PSII, indicating a robust protective response of PSII against stress. Furthermore, the enhancement of Cr tolerance facilitated by Si NPs was linked to the upregulation of genes associated with antioxidant enzymes and transcription factors, alongside the concurrent reduction in metal transporter activity.

Moderate potassium ferrate dosage enhances methane production from the anaerobic digestion of waste activated sludge.

The annual increase of waste activated sludge (WAS) has become an urgent problem to be solved in sewage plants worldwide. Anaerobic digestion (AD) of WAS is an attractive choice to maximize the resource utilization rate. Nevertheless, the disintegration of sludge complex polymers is difficult, resulting in a low bioconversion rate. Potassium ferrate (PF), as a green oxidant with strong oxidizing property, has attracted great attention in WAS pretreatment recently. The effects of PF pretreatment on WAS hydrolysis and its dosage-response on methane production were investigated in the present study. Results show that as PF dosage raise from 0 to 50 g-K2FeO4/ kg-TS (total solids), the methane yield enhanced significantly by 40.3% from 0.083 to 0.12 L/g-VSadded (volatile solids). Nevertheless, the further increase in PF dosage resulted in decreased methane production. Especially with the PF dosage of 500 g-K2FeO4/ kg-TS, methane production is even slightly lower than the control reactor without PF oxidation. The mechanism analysis showed that although the dissolution of polysaccharides and proteins was enhanced with the high dosage of PF, the accompanying released humic-like substances and high concentration of ferric ions should be the main reasons inhibiting methane production.

Calcium nanoparticles (Ca-NPs) improve drought stress tolerance in Brassica napus by modulating the photosystem II, nutrient acquisition and antioxidant performance.

Foliar-application of nano-particles enhanced the foliar nutrient status and crop growth and yield. It is hypothesized that being second messenger molecule, supplementation of Ca2+ via calcium nanoparticles (Ca-NPs) can trigger various signaling pathways of physiological processes which can lead to alleviate the adverse effects of drought stress on the growth of canola (Brassica napus L.). Nano-enabled foliar-application could be an ideal strategy for advancing agricultural productivity. The present study explored the role of calcium nanoparticles (Ca-NPs) in alleviating drought stress in hydroponic Brassica napus (B. napus) plants. The foliar applied Ca-NPs were spherically shaped with an average size of 86 nm. Foliar application of 100 mg L-1 Ca-NPs enhanced biomass of canola plants and considered as optimal dose. Ca-NPs at 100 mg L-1 has a greater favorable impact on mesophyll ultrastructure, PSI and PSII efficacy, gas exchange parameters, chlorophyll content, and mineral absorption. The Ca-NPs treatment increased NPQ and Y(NPQ) under drought condition, indicating a higher PSII protective response to stressed conditions with better heat dissipation as a photoprotective component of NPQ. Ca-NPs application also reduced oxidative stress damage as measured by a reduction in reactive oxygen species (ROS) generation in terms of hydrogen peroxide and malondialdehyde (H2O2 and MDA). Furthermore, Ca-NPs induced drought tolerance response corresponded to an increased in key antioxidative defense enzymes (SOD, POD, CAT, APX), as well as non-enzymatic components (protease, lipoxygenase, proline, total soluble protein contents, endogenous hormonal biosynthesis), and secondary metabolite expression in B. napus plants. Taken together, the results of this study offer new insights into the physiological and molecular mechanisms by which B. napus responds to Ca-NPs exposure.

Pramipexole Inhibits Neuronal Apoptosis in Rats with Parkinson's Disease.

To explore the inhibition of pramipexole on the neuronal apoptosis and its influences on the expressions of brain tissue brain-derived neurotrophic factor (BDNF), and serum miR-103a and miR-30b and inflammatory factors in rats with Parkinson's disease. A total of 36 Sprague-Dawley rats were randomly divided into normal group (n = 12), model group (n = 12) and pramipexole group (n = 12). Compared with that in normal group, the positive expression of BDNF was substantially increased in model group and pramipexole group, and its positive expression in pramipexole group was notably higher than that in model group. The WB results revealed that compared with those in normal group, the relative protein expression levels of Bax and Bcl-2 were markedly increased and decreased, respectively, in the other two groups, and that pramipexole group exhibited a remarkable decline in the relative protein expression level of Bax and a considerable increase in that of Bcl-2, compared with model group. The relative expression levels of miR-103a and miR-30b in model and pramipexole groups were markedly higher than those in normal group, and pramipexole group had remarkably higher relative expression levels of miR-103a and miR-30b than model group. It was found through ELISA that model and pramipexole groups had markedly raised IL-1ß and IL-18 content compared with normal group, and their content in pramipexole group was remarkably lower than that in model group. Based on the TUNEL results, compared with that in normal group, the apoptosis rate of cells rose substantially in the other two groups, and the apoptosis rate in pramipexole group was notably lower than that in model group. Pramipexole may up-regulate the expressions of BDNF, miR-103a and miR-30b to inhibit the apoptosis and inflammation in Parkinson's disease model rats.

Decarbonising the iron and steel sector for a 2 °C target using inherent waste streams.

The decarbonisation of the iron and steel industry, contributing approximately 8% of current global anthropogenic CO2 emissions, is challenged by the persistently growing global steel demand and limitations of techno-economically feasible options for low-carbon steelmaking. Here we explore the inherent potential of recovering energy and re-using materials from waste streams, high-temperature slag, and re-investing the revenues for carbon capture and storage. In a pathway based on energy recovery and resource recycling of glassy blast furnace slag and crystalline steel slag, we show that a reduction of 28.5 ± 5.7% CO2 emissions to the sectoral 2 °C target requirements in the iron and steel industry could be realized in 2050 under strong decarbonization policy consistent with low warming targets. The technological schemes applied to engineer this high-potential pathway could generate a revenue of US$35 ± 16 and US$40 ± 18 billion globally in 2035 and 2050, respectively. If this revenue is used for carbon capture and storage implementation, equivalent CO2 emission to the 2 °C sectoral target requirements is expected to be reduced before 2050, without any external investments.

Application of Laser-Induced Breakdown Spectroscopy Coupled With Spectral Matrix and Convolutional Neural Network for Identifying Geographical Origins of Gentiana rigescens Franch.

Accurate geographical origin identification is of great significance to ensure the quality of traditional Chinese medicine (TCM). Laser-induced breakdown spectroscopy (LIBS) was applied to achieve the fast geographical origin identification of wild Gentiana rigescens Franch (G. rigescens Franch). However, LIBS spectra with too many variables could increase the training time of models and reduce the discrimination accuracy. In order to solve the problems, we proposed two methods. One was reducing the number of variables through two consecutive variable selections. The other was transforming the spectrum into spectral matrix by spectrum segmentation and recombination. Combined with convolutional neural network (CNN), both methods could improve the accuracy of discrimination. For the underground parts of G. rigescens Franch, the optimal accuracy in the prediction set for the two methods was 92.19 and 94.01%, respectively. For the aerial parts, the two corresponding accuracies were the same with the value of 94.01%. Saliency map was used to explain the rationality of discriminant analysis by CNN combined with spectral matrix. The first method could provide some support for LIBS portable instrument development. The second method could offer some reference for the discriminant analysis of LIBS spectra with too many variables by the end-to-end learning of CNN. The present results demonstrated that LIBS combined with CNN was an effective tool to quickly identify the geographical origin of G. rigescens Franch.

Programmable light-driven swimming actuators via wavelength signal switching.

Light-driven swimming actuators with different motion modes could lead to many previously unachievable applications. However, controllable navigation often requires focusing light precisely on certain positions of the actuator, which is unfavorable for accurate dynamical operation or in microscale applications. Here, we present a type of programmable swimming actuators that can execute wavelength-dependent multidirectional motions via the Marangoni effect. Several multi­degree of freedom swimming motions have been realized: Forward-and-backward and zigzag actuators can execute one-dimensional (1D) and 2D linear motion, respectively; bidirectional gear rotation as angular motion can be regulated to obtain tunable speeds; and the turning actuator as a "freighter" is able to turn left, right, and go straight for precise maze navigation. A mechanical measurement system is established to quantitatively measure the driving force of the motion directly. The accessible wavelength-selective strategy presented here can inspire further explorations of simple and practical light-driven materials and systems.

AutoEncoder-Based Computational Framework for Tumor Microenvironment Decomposition and Biomarker Identification in Metastatic Melanoma.

Melanoma is one of the most aggressive cancer types whose prognosis is determined by both the tumor cell-intrinsic and -extrinsic features as well as their interactions. In this study, we performed systematic and unbiased analysis using The Cancer Genome Atlas (TCGA) melanoma RNA-seq data and identified two gene signatures that captured the intrinsic and extrinsic features, respectively. Specifically, we selected genes that best reflected the expression signals from tumor cells and immune infiltrate cells. Then, we applied an AutoEncoder-based method to decompose the expression of these genes into a small number of representative nodes. Many of these nodes were found to be significantly associated with patient prognosis. From them, we selected two most prognostic nodes and defined a tumor-intrinsic (TI) signature and a tumor-extrinsic (TE) signature. Pathway analysis confirmed that the TE signature recapitulated cytotoxic immune cell related pathways while the TI signature reflected MYC pathway activity. We leveraged these two signatures to investigate six independent melanoma microarray datasets and found that they were able to predict the prognosis of patients under standard care. Furthermore, we showed that the TE signature was also positively associated with patients' response to immunotherapies, including tumor vaccine therapy and checkpoint blockade immunotherapy. This study developed a novel computational framework to capture the tumor-intrinsic and -extrinsic features and identified robust prognostic and predictive biomarkers in melanoma.

Detection of chlorpyrifos and carbendazim residues in the cabbage using visible/near-infrared spectroscopy combined with chemometrics.

Contamination of agricultural plants and food in the environment caused by pesticide residues has gained great attention of the world. Pesticide residues on vegetables constitute a potential risk to human health. A visible/near-infrared (Vis/NIR) spectroscopy combined with chemometric methods was employed to quantitatively determine chlorpyrifos and carbendazim residues in the cabbage (Brassica chinensis L.). Preprocessing methods were used for spectra denoising. Partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) were applied as the quantification models. Feature variables were selected by successive projection algorithms (SPA), random frog and regression coefficients in PLSR. As for the samples with chlorpyrifos residues, LS-SVM models based on the global spectra achieved best model performance. The best performance for carbendazim content prediction was achieved by the LS-SVM models based on the original global spectra. And modeling with SPA selected feature variables for carbendazim determination was as good as modeling with the global spectra. The results indicated that Vis/NIR spectroscopy coupled with chemometrics could be an efficient way for the assessment of the pesticide residues in vegetables, and was significant for detection of environmental pollution and ensuring food safety.

On-site CO2 bio-sequestration in anaerobic digestion: Current status and prospects.

The advantages of anaerobic digestion (AD) technology in organic solid waste treatment for bioenergy recovery are evidenced in worldwide. Recently, more attention has been paid to on-site biogas research, as well as biogenic CO2 sequestration from AD plant, to promote "carbon neutral". Single-phase and two-phase AD system can be incorporated with various CO2 bioconversion technologies through H2 mediated CO2 bioconversion (in-situ and ex-situ biogas upgrading), or other emerging strategies for CO2 fixation without exogenous H2 injection; these include in-situ direct interspecies electron transfer reinforcement, electromethanogenesis, and off-gas reutilization. The existing and potential scenarios for on-site CO2 bio-sequestration within the AD framework are reviewed from the perspectives of metabolic pathways, functional microorganisms, the limitations on reaction kinetics. This review concluded that on-site CO2 bio-sequestration is a promising solution to reduce greenhouse gas emissions and increase renewable energy recovery.

Embryonic exposure to prothioconazole induces oxidative stress and apoptosis in zebrafish (Danio rerio) early life stage.

Triazole fungicides are extensively applied in general agriculture for fungal control and have negative impacts on aquatic organisms. Prothioconazole, a widely used triazole fungicide, is toxic to zebrafish, but systematic research on the negative effects caused by prothioconazole in zebrafish embryos is limited. In this study, we studied the developmental toxicology, oxidative stress and apoptosis caused by prothioconazole in zebrafish embryos. Exposure to 0.850 mg/L prothioconazole impacts embryo survival and hatching. Prothioconazole exposure caused embryo malformation, especially yolk-sac and pericardial edemas, and prothioconazole-induced apoptosis was observed. Additionally, exposure to a high prothioconazole concentration up-regulated the expression levels of oxidative stress defense-related genes and p53. The bax to bcl2 ratio increased along with exposure time and prothioconazole concentration. Prothioconazole induced apoptosis during the early life stages of zebrafish and may trigger oxidative-stress and p53-dependent pathway responses. Our findings increase our understanding of the molecular mechanisms of oxidative stress and cell death caused by prothioconazole.

Exposure to prothioconazole induces developmental toxicity and cardiovascular effects on zebrafish embryo.

Prothioconazole is a fungicide that has been widely used in general agriculture and livestock husbandry. This study evaluated the acute toxicity of prothioconazole to zebrafish embryos by assessing their hatching rate and malformation when exposed to different concentrations of prothioconazole. The 96 h-LC50 value of zebrafish embryos was 1.70 mg/L. Upon exposure to 0.85 mg/L, the mortality rate of the embryos significantly increased while their hatching rate decreased significantly. At prothioconazole concentrations higher than 0.43 mg/L, developmental morphologic abnormalities such as heart and yolk-sac edema, spine curvature, tail deformity, shortened body length and decreased eye area were observed. The heart rate of embryos decreased in a dose-dependent fashion during the exposure time. Prothioconazole exposure also resulted in increased rates of cardiac malformation detected by significant increase in the distance between the sinus venosus and bulbus arteriosus and the pericardium area. Moreover, the expression levels of genes related to cardiac development (amhc, vmhc, fli1, hand2, gata4, nkx2.5, tbx5 and atp2a2a) were significantly altered after exposure to prothioconazole. Indeed, this study revealed the adverse effects on the developmental and cardiovascular system of zebrafish embryo caused by prothioconazole. It further elucidated the risk of prothioconazole exposure to vertebrate cardiovascular toxicity. As such, it provides a theoretical foundation for pesticide risk management measures.

EPMDA: Edge Perturbation Based Method for miRNA-Disease Association Prediction.

In the recent few years, plenty of research has shown that microRNA (miRNA) is likely to be involved in the formation of many human diseases. So effectively predicting potential associations between miRNAs and diseases helps to understand the development and treatment of diseases. In this study, an edge perturbation based method is proposed for predicting potential miRNA-disease association (EPMDA). Different from the previous studies, we design an feature vector to describe each edge of a graph by structural Hamiltonian information. Moreover, the extracted features are used to train a multi-layer perception model to predict the candidate disease-miRNA associations. The experimental results on the HMDD dataset show that EPMDA achieves the AUC value of 0.9818 through 5-fold cross-validation, which improves the AUC values by approximately 3.5 percent compared to the latest method DeepMDA. For the leave-one-disease-out cross-validation, EPMDA achieves the AUC value of 0.9371, which improves the AUC values by approximately 7.4 percent compared to DeepMDA. In the case study, we verify the prediction performance of EPMDA on three human diseases. As a result, there are 42, 46, and 41 of the top 50 predicted miRNAs for these three diseases which are confirmed by the published experimental discoveries, respectively.

Systematic computational identification of prognostic cytogenetic markers in neuroblastoma.

BACKGROUND: Neuroblastoma (NB) is the most common extracranial solid tumor found in children. The frequent gain/loss of many chromosome bands in tumor cells and absence of mutations found at diagnosis suggests that NB is a copy number-driven cancer. Despite the previous work, a systematic analysis that investigates the relationship between such frequent gain/loss of chromosome bands and patient prognosis has yet to be implemented. METHODS: First, we analyzed two NB CNV datasets to select chromosomal bands with a high frequency of gain or loss. Second, we applied a computational approach to infer sample-specific CNVs for each chromosomal band selected in step 1 based on gene expression data. Third, we applied univariate Cox proportional hazards models to examine the association between the resulting inferred copy number values (iCNVs) and patient survival. Finally, we applied multivariate Cox proportional hazards models to select chromosomal bands that remained significantly associated with prognosis after adjusting for critical clinical variables, including age, stage, gender, and MYCN amplification status. RESULTS: Here, we used a computational method to infer the copy number variations (CNVs) of sample-specific chromosome bands from NB patient gene expression profiles. The resulting inferred CNVs (iCNVs) were highly correlated with the experimentally determined CNVs, demonstrating CNVs can be accurately inferred from gene expression profiles. Using this iCNV metric, we identified 58 frequent gain/loss chromosome bands that were significantly associated with patient survival. Furthermore, we found that 7 chromosome bands were still significantly associated with patient survival even when clinical factors, such as MYCN status, were considered. Particularly, we found that the chromosome band chr11p14 has high potential as a novel candidate cytogenetic biomarker for clinical use. CONCLUSION: Our analysis resulted in a comprehensive list of prognostic chromosome bands supported by strong statistical evidence. In particular, the chr11p14 gain event provided additional prognostic value in addition to well-established clinical factors, including MYCN status, and thereby represents a novel candidate cytogenetic biomarker with high clinical potential. Additionally, this computational framework could be readily extended to other cancer types, such as leukemia.

Cellular and compositional insight into the sludge dewatering process using enzyme treatment.

Removal of intracellular water in microbial cells remains a key issue for sludge disposal, and here, a novel method of enzymatic treatment with two enzymes, lysozyme and protease, was employed. Total internal reflection fluorescence microscope (TIRF) was applied to image the bacteria in sludge and quantify the evolution of sludge bacteria for the first time. The ratio of dead/live bacterial cells was always higher in the presence of lysozyme than in the presence of protease, indicating that lysozyme has higher activity in inducing bacterial cell degradation and releasing intracellular water. The compositions of extracellular polymeric substances (EPS) were further measured, and the results show that the dewatering performance of sludge is correlated both to the release of cell contents and the variations in EPS composition during cell degradation. Moreover, kinetic analysis demonstrated that the enzyme-catalyzed reaction was substantially completed within 1 h, i.e., the reaction was quite rapid during the first 1 h, and thereafter, it gradually reduced to stability. The mechanism of enzymatic treatment of sludge explored in this study thus not only enhanced the understanding of sludge deep dewatering but also provided significant methodological clues for the disposal of sludge.

Biochemical and histological alterations in adult zebrafish（Danio rerio）ovary following exposure to the tetronic acid insecticide spirotetramat.

As a new tetronic acid derivative insecticide, spirotetramat has been reported to be toxic to an array of aquatic organisms. However, the toxic effects of spirotetramat on zebrafish especially at ovary are still obscure. Hereby, the acute toxicity of spirotetramat towards zebrafish（Danio rerio），as well as the changes on biochemical and histological traits of ovary were investigated. The acute toxicity test results showed that the median lethal concentration (LC50) value of spirotetramat were 9.61 mg/L and 7.21 mg/L at 72 h and 96 h, respectively, suggesting spirotetramat has moderate toxicity to zebrafish. In the following sub-lethal toxicity test, the gene expression of superoxide dismutase (SOD), catalase (CAT), and gonadotropic hormone receptor (FSHR and LHR) together with the content of malondialdehyde (MDA) in ovary were measured at 14, 21, and 28 days after exposure to 36, 360 and 720 µg/L. Under high concentration treatment (360 and 720 µg/L), MDA content, the relative transcription CAT and SOD gene level increased significantly in ovary (p < 0.05). That indicated sub-lethal doses spirotetramat caused oxidative stress and lipid peroxidation in zebrafish ovary during the entire experimental period. Under the exposure to spirotetramat at 720 µg/L after 14 days, the relative transcript FSHR gene level was down regulated, and the relative transcript LHR gene level was up regulated. Moreover, spirotetramat affected the oocyte development especially on the diameter size and maturation during the ovary tissue biopsies at 28 days. Taken together, these findings revealed the adverse effects of spirotetramat on fish from the biochemical and histological aspects.

Predicting protein complexes using a supervised learning method combined with local structural information.

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Identification of crucial genes associated with Parkinson's disease using microarray data.

The present study aimed to examine potential crucial genes associated with Parkinson's disease (PD) in addition to the interactions and regulators of these genes. The chip data (GSE7621) were obtained from the Gene Expression Omnibus and standardized using the robust multi­array average in the Affy package of R software. The differentially expressed genes (DEGs) were then screened using the Samr package with a false discovery rate (FDR) <0.05 and |log2 fold change (FC)|>1. Crucial PD­associated genes were predicted using the Genetic Association Database in the Database for Annotation, Visualization and Integrated Discovery and sequence alignment. Furthermore, transcription factors (TFs) of the crucial PD­associated genes were predicted, and protein­protein interactions (PPIs) between the crucial PD­associated genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins. Additionally, another dataset of PD was used to validate the expression of crucial PD­associated genes. A total of 670 DEGs (398 upregulated and 272 downregulated genes) were identified in the PD samples. Of these, 10 DEGs enriched in pathways associated with the nervous system were predicted to be crucial in PD, including C­X­C chemokine receptor type 4 (CXCR4), deleted in colorectal cancer (DCC) and NCL adaptor protein 2 (NCK2). All 10 genes were associated with neuron development and differentiation. They were simultaneously modulated by multiple TFs, including GATA, E2F and E4 promoter­binding protein 4. The PPI networks showed that DCC and CXCR4 were hub proteins. The DCC­netrin 1­roundabout guidance receptor 2­slit guidance ligand 1 interaction pathway, and several genes, including TOX high mobility group box family member 4, kinase insert domain receptor and zymogen granule protein 16B, which interacted with CXCR4, were novel findings. Additionally, CXCR4 and NCK2 were upregulated in another dataset (GSE8397) of PD. These genes, interactions of proteins and TFs may be important in the progression of PD.

A new computational strategy for identifying essential proteins based on network topological properties and biological information.

Essential proteins are the proteins that are indispensable to the survival and development of an organism. Deleting a single essential protein will cause lethality or infertility. Identifying and analysing essential proteins are key to understanding the molecular mechanisms of living cells. There are two types of methods for predicting essential proteins: experimental methods, which require considerable time and resources, and computational methods, which overcome the shortcomings of experimental methods. However, the prediction accuracy of computational methods for essential proteins requires further improvement. In this paper, we propose a new computational strategy named CoTB for identifying essential proteins based on a combination of topological properties, subcellular localization information and orthologous protein information. First, we introduce several topological properties of the protein-protein interaction (PPI) network. Second, we propose new methods for measuring orthologous information and subcellular localization and a new computational strategy that uses a random forest prediction model to obtain a probability score for the proteins being essential. Finally, we conduct experiments on four different Saccharomyces cerevisiae datasets. The experimental results demonstrate that our strategy for identifying essential proteins outperforms traditional computational methods and the most recently developed method, SON. In particular, our strategy improves the prediction accuracy to 89, 78, 79, and 85 percent on the YDIP, YMIPS, YMBD and YHQ datasets at the top 100 level, respectively.