Short Video-Based Mental Health Intervention for Depressive Symptoms in Junior High School Students: A Cluster Randomized Controlled Trial.

Purpose: Digital interventions for adolescent mental health are emerging in high-income countries, but have faced challenges and are scarce in China. This study investigated the effect of a short video-based mental health intervention on depressive symptoms in Chinese adolescents. Methods: A three-arm cluster randomized controlled trial was conducted in four junior high schools in Shanghai from December 2020 to December 2021 with the measurement at baseline, 6 months after study entry, and 12 months. Outcomes were collected by self-completed questionnaires administered by teachers masked to allocation. The primary outcome was depressive symptoms assessed by the Depression Self-Rating Scale for Children (DSRSC). Mixed effects models were used to compare psychologist-led intervention (n=428 students) and teacher-led intervention (n=385) including six short video-based sessions to usual school provision (n=751). Results: Using intention-to-treat analyses, psychologist-led intervention showed more reduction in depressive symptoms compared to usual school provision at 6 months (coefficient -1.00, 95% CI -1.94 to -0.05), but not at 12 months. Using per-protocol analyses among participants who watched at least three video episodes, both psychologist-led (-1.14, -2.20 to -0.09) and teacher-led intervention (-1.23, -2.45 to -0.02) reduced depressive symptoms compared to usual school provision at 6 months, and the effect of teacher-led intervention persisted at 12 months (-1.58, -3.13 to -0.03). Further exploration found that compared with urban students, the between-group differences for depressive symptoms in rural students were more significant (p<0.05 for interaction) and the effects were maintained at 12 months. Conclusion: The short video-based mental health intervention showed potential to reduce depressive symptoms among Chinese adolescents, and the effects were more significant if the minimum video viewing frequency was reached.

Hemispherectomy Outcome Prediction Scale: a validity study.

OBJECTIVE: Hemispherectomy is highly effective for patients with medically refractory epilepsy (MRE) arising from a single hemisphere. Recently, the Hemispherectomy Outcome Prediction Scale (HOPS) was developed as a prediction tool for seizure freedom after hemispherectomy. The authors' goal was to perform a validation study to determine the generalizability of the HOPS score. METHODS: The authors present an observational, retrospective, 20-year, single-institution, two-surgeon experience using the lateral peri-insular hemispherectomy approach to validate the HOPS score. Variables used to derive the HOPS score included seizure onset age, semiology, PET hypometabolism, seizure substrate, and history of prior epilepsy resection. Multivariable logistic regression, multiple imputation, and Bayesian analyses were used to determine validity. RESULTS: The authors' cohort comprised 60 patients; 55% of patients were male and 78% were Caucasian. The median age at first hemispherectomy surgery was 72 months. At 1 year postoperatively, 80% of patients had Engel class I outcomes, analogous to most contemporary series. All patients who experienced seizure recurrence after hemispherectomy did so within the first 2 years postoperatively. Sixteen (27%) and 10 (17%) patients had contralateral MRI findings and hypometabolism on PET, respectively. Both a multivariable logistic regression model using HOPS score variables (model p = 0.2588) and a revised model that included contralateral MRI findings (model p = 0.4715) were not statistically significant in this cohort. Bayesian analysis also did not validate the HOPS score. CONCLUSIONS: While seizure outcome prediction tools may be helpful for counseling patients about postoperative outcomes, rigorous validity and reliability testing are required. Prospective, standardized, and longitudinal evaluation of patients undergoing hemispherectomy are needed.

Surface-enhanced Raman spectroscopy charged probes under inverted superhydrophobic platform for detection of agricultural chemicals residues in rice combined with lightweight deep learning network.

In this study, surface-enhanced Raman spectroscopy (SERS) charged probes and an inverted superhydrophobic platform were used to develop a detection method for agricultural chemicals residues (ACRs) in rice combined with lightweight deep learning network. First, positively and negatively charged probes were prepared to adsorb ACRs molecules to SERS substrate. An inverted superhydrophobic platform was prepared to alleviate the coffee ring effect and induce tight self-assembly of nanoparticles for high sensitivity. Chlormequat chloride of 15.5-0.05 mg/L and acephate of 100.2-0.2 mg/L in rice were measured with the relative standard deviation of 4.15% and 6.25%. SqueezeNet were used to develop regression models for the analysis of chlormequat chloride and acephate. And the excellent performances were obtained with the coefficients of determination of prediction of 0.9836 and 0.9826 and root-mean-square errors of prediction of 0.49 and 4.08. Therefore, the proposed method can realize sensitive and accurate detection of ACRs in rice.

Statistic and Network Features of RGB and Hyperspectral Imaging for Determination of Black Root Mold Infection in Apples.

Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging and hyperspectral imaging (HSI) are combined to detect the infection degree of BRM in apple fruits. First, RGB and HSI images of healthy, mildly, moderately, and severely infected fruits are measured, and those with effective wavelengths (EWs) are screened from HSI by random frog. Second, the statistic and network features of images are extracted by using color moment and convolutional neural network. Meanwhile, random forest (RF), K-nearest neighbor, and support vector machine are used to construct classification models with the above two features of RGB and HSI images of EWs. Optimal results with the 100% accuracy of training set and 96% accuracy of prediction set are obtained by RF with the statistic and network features of the two images, outperforming the other cases. The proposed method furnishes an accurate and effective solution for determining the BRM infection degree in apples.

Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation.

In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR). Then, random frog and elliptical envelope were adopted to select effective wavelengths, and partial least squares regression (PLSR) and support vector regression were used to establish analysis models. The optimal transformation was from KR, and PLSR and effective wavelengths of the transformed spectra obtained excellent performance with coefficient of determination of test of 0.6987 and 0.8317 and root-mean-square error of test of 0.3359 and 2.2239, respectively. The result was better than that of the rice spectra and was close to that of the husked rice spectra. When the moisture content was integrated into the regression model of amylase activity, a better result was obtained. Thus, the proposed method can detect amylase activity and moisture content in rice accurately.

Multi-scale convolution neural network with residual modules for determination of drugs in human hair using surface-enhanced Raman spectroscopy with a gold nanorod film self-assembled by inverted evaporation.

Detection of illegal drug users is crucial in controlling drug-related crimes, reducing drug prevalence, and protecting human lives to ensure social stability. In this study, surface-enhanced Raman spectroscopy (SERS) and deep learning networks were employed to determine methamphetamine, ketamine, and morphine in human hair. Drugs were obtained from hair through alkaline hydrolysis and liquid-liquid extraction, and gold nanorods were employed to prepare the self-assembled film as the SERS substrate by inverted evaporation. The film showed good uniformity and excellent sensitivity, with a relative standard deviation of 15.6% and a detection limit of at least 10-10 M in the SERS detection of crystal violet. The spectra of methamphetamine, ketamine, and morphine at 0.05-1.0, 0.1-2.0, and 0.1-2.0 ng/mg were obtained, and the three drugs could be detected. Inception, a multi-scale feature extraction network, was combined with residual modules (Inception-ResNet) to develop the identification models of drugs, and the effect of spectral input form as a vector or matrix was explored. Inception-ResNet with input form of matrix outweighed other methods with 100.00%, 100.00%, and 99.23% accuracies in the training, validation, and prediction sets, respectively. In brief, SERS and Inception-ResNet with the spectra in matrix form provide an efficient and accurate determination of drugs in human hair, enabling the retrospective evaluation of drug use, and the method will be anticipated to detect excitant, poison, and toxic chemicals in human hair.

Quantitative analysis of pH value in soil using laser-induced breakdown spectroscopy coupled with a multivariate regression method.

The quantitative analyses of pH value in soil have been performed using laser-induced breakdown spectroscopy (LIBS) technology. The aim of this work was to obtain a reliable and accurate method for rapid detection of pH value in soil. Seventy-four samples were used as a calibration set, and 24 samples were used as a prediction set. To eliminate the matrix effect, the multivariate models of partial least-squares regression (PLSR) and least-squares support vector regression (LS-SVR) were used to construct the models. The intensities of nine emission lines of C, Ca, Na, O, H, Mg, Al, and Fe elements were used to fit the models. For the PLSR model, the correlation coefficient was 0.897 and 0.906 for the calibration and prediction set, respectively. Furthermore, the analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficients for calibration and prediction set were improved to 0.991 and 0.987. The prediction mean absolute error was pH 0.1 units, and the root mean square error of the prediction was only 0.079. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method for determining pH value in soil.

Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model.

The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard-Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares-support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.

Quantitative analysis of nitrogen in ammonium phosphate fertilizers using laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) coupled with the linear multivariate calibration method was applied to analyze nitrogen (N) quantitatively in ammonium phosphate fertilizers. The intensity of lines N (NI:742.4, 744.2, 746.8, 856.7, 859.4, 862.9, 870.3, 871.2, 871.8 nm) and O (OI:777.2, 844.6, 882.0 nm) were used as independent variables for the models. To verify the accuracy of the models, the unary, binary, ternary, and quaternary variables were chosen to establish the linear regression equations. The results of the linear models showed that the quaternary model was better than the other three models. The correlation coefficient of the quaternary linear model was 0.981 and the maximum relative error of the validation samples was 4.32%.

Univariate and Multivariate Analysis of Phosphorus Element in Fertilizers Using Laser-Induced Breakdown Spectroscopy.

Rapid detection of phosphorus (P) element is beneficial to the control of compound fertilizer production process and is of great significance in the fertilizer industry. The aim of this work was to compare the univariate and multivariate analysis of phosphorus element in compound fertilizers and obtain a reliable and accurate method for rapid detection of phosphorus element. A total of 47 fertilizer samples were collected from the production line; 36 samples were used as a calibration set, and 11 samples were used as a prediction set. The univariate calibration curve was constructed by the intensity of characteristic line and the concentration of P. The linear correlation coefficient was 0.854 as the existence of the matrix effect. In order to eliminate the matrix effect, the internal standardization as the appropriate methodology was used to increase the accuracy. Using silicon (Si) element as an internal element, a linear correlation coefficient of 0.932 was obtained. Furthermore, the chemometrics model of partial least-squares regression (PLSR) was used to analysis the concentration of P in fertilizer. The correlation coefficient was 0.977 and 0.976 for the calibration set and prediction set, respectively. The results indicated that the LIBS technique coupled with PLSR could be a reliable and accurate method in the quantitative determination of P element in complex matrices like compound fertilizers.

Long non-coding RNA MALAT1 interacts with transcription factor Foxo1 to regulate SIRT1 transcription in high glucose-induced HK-2 cells injury.

BACKGROUND: Tubular injury is considered as a crucial pathological feature of diabetic nephropathy. LncRNA MALAT1 is involved in diabetic complications. Hence the role of MALAT1 in high glucose-induced renal tubular epithelial cells (HK-2) injury deserves investigation. METHODS: The diabetic mice model was established with streptozotocin (STZ) injection. The expression of NEAT1, SIRT1, and Foxo1 mRNA and protein was determined with qRT-PCR and western blot, respectively. The serum creatinine and urinary albumin were examined by enzyme linked immunosorbent assay (ELISA). Interaction between MALAT1 and Foxo1 was detected with RIP and RNA pull-down assay, respectively. Dual luciferase reporter assay was used to evaluate the binding between Foxo1 and SIRT1. RESULTS: LncRNA MALAT1 was up-regulated in kidney tissues of diabetic mice and in HK-2 cells treated with high glucose, while the expression of SIRT1 was decreased. Interaction between MALAT1 and Foxo1 was observed in HK-2 cells and the interaction was promoted by high glucose treatment. Foxo1 activated SIRT1 transcription by binding to its promoter, and MALAT1 repressed SIRT1 expression through targeting Foxo1. CONCLUSION: LncRNA MALAT1 interacts with transcription factor Foxo1 to represses SIRT1 transcription in high glucose incubated HK-2 cells, which promotes high glucose-induced HK-2 cells injury.

Cancer Subtype Discovery Based on Integrative Model of Multigenomic Data.

One major goal of large-scale cancer omics study is to understand molecular mechanisms of cancer and find new biomedical targets. To deal with the high-dimensional multidimensional cancer omics data (DNA methylation, mRNA expression, etc.), which can be used to discover new insight on identifying cancer subtypes, clustering methods are usually used to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrate these data and map them into an effective low-dimensional subspace. In this paper, we develop a dimension-reduction and data-integration method for indentifying cancer subtypes, named Scluster. First, Scluster, respectively, projects the different original data into the principal subspaces by an adaptive sparse reduced-rank regression method. Then, a fused patient-by-patient network is obtained for these subgroups through a scaled exponential similarity kernel method. Finally, candidate cancer subtypes are identified using spectral clustering method. We demonstrate the efficiency of our Scluster method using three cancers by jointly analyzing mRNA expression, miRNA expression, and DNA methylation data. The evaluation results and analyses show that Scluster is effective for predicting survival and identifies novel cancer subtypes of large-scale multi-omics data.

Post-floral Erection of Stalks Provides Insight into the Evolution of Fruit Orientation and Its Effects on Seed Dispersal.

That stalks reorient after flowering to face upwards is a common phenomenon in many flowering plants, indicating the potential importance of fruit orientation on seed dispersal. But this idea has not been subject to an empirical test. We examined this hypothesis by analysing the evolutionary correlation between fruit orientation and other characters and by investigating the effects of fruit orientation on seed dispersal. We found that 1) in a sub-alpine plant community, upward fruit orientation strongly correlates with fruits that act as seed containers, which are often of dry type and are dispersed by non-animal vectors; 2) as exemplified by the Campanulaceae s. str., fruit orientation strongly correlates with dehiscence position. Upwardly-oriented capsules dehisce at the apex, whereas pendent ones dehisce at the base, in both cases ensuring that seeds are released from an upright position; 3) in manipulation experiments on Silene chungtienensis, upward fruits (the natural state) exhibit much greater dispersal distances and more dispersive pattern than pendent ones, and have a more even distribution of dispersal direction than horizontal ones. Our results suggest that fruit orientation may have important function in seed dispersal, which may be the reason why the phenomenon that stalk erection after flowering occurs widely.

High glucose induces renal tubular epithelial injury via Sirt1/NF-kappaB/microR-29/Keap1 signal pathway.

OBJECTIVE: Diabetic nephropathy (DN) is a serious complication that commonly confronted by diabetic patients. A common theory for the pathogenesis of this renal dysfunction in diabetes is cell injury, inflammation as well as oxidative stress. In this content, the detailed molecular mechanism underlying high glucose induced renal tubular epithelial injury was elaborated. METHODS: An in vivo rat model of diabetes by injecting streptozotocin (STZ) and an in vitro high glucose incubated renal tubular epithelial cell (HK-2) model were used. Expression levels of Keap1, nuclear Nrf2 and p65 were determined by western blotting. Level of microR-29 (miR-29) was assessed using quantitative RT-PCR. Combination of p65 and miR-29 promotor was assessed using chromatin immunoprecipitation. Keap1 3'-UTR activity was detected using luciferase reporter gene assay. Cell viability was determined using MTT assay. RESULTS: In diabetic rat, miR-29 was downregulated and its expression is negatively correlated with both of serum creatinine and creatinine clearance. In high glucose incubated HK-2 cell, deacetylases activity of Sirt1 was attenuated that leads to decreased activity of nuclear factor kappa B (NF-κB). NF-κB was demonstrated to regulate miR-29 expression by directly binding to its promotor. The data of luciferase assay showed that miR-29 directly targets to Keap1 mRNA. While high glucose induced down regulation of miR-29 contributed to enhancement of Keap1 expression that finally reduced Nrf2 content by ubiquitinating Nrf2. Additionally, overexpression of miR-29 effectively relieved high glucose-reduced cell viability. CONCLUSION: High glucose induces renal tubular epithelial injury via Sirt1/NF-κB/microR-29/Keap1 signal pathway.

Long non-coding MIAT mediates high glucose-induced renal tubular epithelial injury.

BACKGROUND AND OBJECTIVE: Long non-coding RNAs (lncRNAs) constitute a novel class of non-coding RNAs that take part in occurrence and development of diabetes complication via regulating gene expression. However, litter is known about lncRNAs in the setting of diabetes induced nephropathy. The aim of this study was to examine whether lncRNA-myocardial infarction-associated transcript (MIAT) is involved in diabetes induced renal tubules injury. METHODS: Adult Wister rats were randomly assigned to receive intraperitoneal STZ (65 mg/kg) to induce diabetes. Rats treated with equal volume of citrate buffer were as control. Renal function was evaluated by analysis of serum creatinine and blood urea nitrogen (BUN) every four weeks after STZ administration. Also tubules of all rats were collected for determination of MIAT and Nrf2 level at the corresponding phase. The in vitro high glucose-triggered human renal tubular epithelial cell line (HK-2) was used to explore the mechanism underling MIAT regulated high glucose-induced tubular damage. RESULTS: In diabetic rats, MIAT showed the lower level and its expression is negatively correlated with serum creatinine and BUN. Consistent with diabetic rat, exposed to high glucose, HK-2 cells expressed lower level of MIAT and Nrf2, and also showed reduction in cell viability. By pcDNA-MIAT plasmid transfection, we observed that MIAT overexpression reversed inhibitory action of Nrf2 expression by high glucose. Moreover, the data of RNA pull-down and RIP showed that MIAT controlled Nrf2 cellular through enhancing Nrf2 stability, which was confirmed by CHX and MG132 administration. Inhibitory effect of cell viability by silencing MIAT was also reversed by Nrf2 overexpression. CONCLUSION: In summary, our data suggested that MIAT/Nrf2 served as an important signaling pathway for high glucose induced renal tubular epithelial injury.

Down-regulation of miR-186 contributes to podocytes apoptosis in membranous nephropathy.

BACKGROUND AND AIM: Podocytes apoptosis is the key process in the development of membranous nephropathy and miR-186 is reported to be related with cell apoptosis. Here we investigated the expression of miR-186 in membranous nephropathy (MGN) patients and the mechanism underlying the podocytes apoptosis. METHODS: Thirty patients with MGN and 30 healthy people were included in this study. The expression of miR-186 was detected in renal tissue and podocyte cells exposed to AngII by real-time PCR. Caspase-3 activity was used to evaluate podocytes apoptosis. TLR4 and P2×7 protein expression was quantified by western blotting. miR-186 inhibitor and miR-186 mimic were transfected into cells to investigate the mechanism underlying miR-186 in podocytes apoptosis. RESULTS: In MGN patients, the level of miR-186 was significantly down-regulated as well as the protein expression of TLR4 and P2×7 was up-regulated in renal tissue. In vitro experiments, TLR4 siRNA increased the expression of miR-186 and miR-186 inhibitor elevated the mRNA and protein expression of P2×7 in podocytes exposed to AngII. In addition, the level of cleaved-caspase-3 was up-regulated by miR-186 inhibitor. The TUNEL-positive cells and caspase-3 activity of podocytes induced by AngII were down-regulated by miR-186 mimic. CONCLUSIONS: We revealed that TLR4 is involved in the regulation of miR-186 expression, and the anti-apoptotic effect of miR-186 on podocytes is correlated with P2×7 regulation.

[Quantitative analysis of thiram by surface-enhanced raman spectroscopy combined with feature extraction Algorithms].

Three feature extraction algorithms, such as the principal component analysis (PCA), the discrete cosine transform (DCT) and the non-negative factorization (NMF), were used to extract the main information of the spectral data in order to weaken the influence of the spectral fluctuation on the subsequent quantitative analysis results based on the SERS spectra of the pesticide thiram. Then the extracted components were respectively combined with the linear regression algorithm--the partial least square regression (PLSR) and the non-linear regression algorithm--the support vector machine regression (SVR) to develop the quantitative analysis models. Finally, the effect of the different feature extraction algorithms on the different kinds of the regression algorithms was evaluated by using 5-fold cross-validation method. The experiments demonstrate that the analysis results of SVR are better than PLSR for the non-linear relationship between the intensity of the SERS spectrum and the concentration of the analyte. Further, the feature extraction algorithms can significantly improve the analysis results regardless of the regression algorithms which mainly due to extracting the main information of the source spectral data and eliminating the fluctuation. Additionally, PCA performs best on the linear regression model and NMF is best on the non-linear model, and the predictive error can be reduced nearly three times in the best case. The root mean square error of cross-validation of the best regression model (NMF+SVR) is 0.0455 micormol x L(-1) (10(-6) mol x L(-1)), and it attains the national detection limit of thiram, so the method in this study provides a novel method for the fast detection of thiram. In conclusion, the study provides the experimental references the selecting the feature extraction algorithms on the analysis of the SERS spectrum, and some common findings of feature extraction can also help processing of other kinds of spectroscopy.

Gene differential coexpression analysis based on biweight correlation and maximum clique.

Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.

The role of central nervous system on hypoglycemia and the feasibility of the brain theory in traditional Chinese medicine on treatment of diabetes mellitus.

The central nervous system (CNS) plays a key regulatory role in glucose homeostasis. In particular, the brain is important in initiating and coordinating protective counterregulatory responses when blood glucose levels fall. This may due to the metabolic dependency of the CNS on glucose, and protection of food supply to the brain. In healthy subjects, blood glucose is normally maintained within a relatively narrow range. Hypoglycemia in diabetic patients can increase the risk of complications, such as heart disease and diabetic peripheral neuropathy. The clinical research finds that the use of traditional Chinese medicine (TCM) has a positive effect on the treatment of hypoglycemia. Here the authors reviewed the current understanding of sensing and counterregulatory responses to hypoglycemia, and discuss combining traditional Chinese and Western medicine and the theory of iatrogenic hypoglycemia in diabetes treatment. Furthermore, the authors clarify the feasibility of treating hypoglycemia on the basis of TCM theory and CNS and have an insight on its clinical practice.

Robust PCA based method for discovering differentially expressed genes.

How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.