Search

Abstract Depression, a pervasive global mental disorder, profoundly impacts daily lives. Despite numerous deep learning studies focused on depression detection through speech analysis, the shortage of annotated bulk samples hampers the development of effective models. In response to this challenge, our research introduces a transfer learning approach for detecting depression in speech, aiming to overcome constraints imposed by limited resources. In the context of feature representation, we obtain depression-related features by fine-tuning wav2vec 2.0. By integrating 1D-CNN and attention pooling structures, we generate advanced features at the segment level, thereby enhancing the model's capability to capture temporal relationships within audio frames. In the realm of prediction results, we integrate LSTM and self-attention mechanisms. This incorporation assigns greater weights to segments associated with depression, thereby augmenting the model's discernment of depression-related information. The experimental results indicate that our model has achieved impressive F1 scores, reaching 79% on the DAIC-WOZ dataset and 90.53% on the CMDC dataset. It outperforms recent baseline models in the field of speech-based depression detection. This provides a promising solution for effective depression detection in low-resource environments.

Alzheimer’s disease (AD) is a debilitating progressively neurodegenerative disease. The best-characterized hallmark of AD, which is marked by behavioral alterations and cognitive deficits, is the aggregation of deposition of amyloid-beta (Aβ) and hyper-phosphorylated microtubule-associated protein Tau. Despite decades of experimental progress, the control rate of AD remains poor, and more precise deciphering is needed for potential therapeutic targets and signaling pathways involved. In recent years, phosphoinositide 3-kinase (PI3K) and Akt have been recognized for their role in the neuroprotective effect of various agents, and glycogen synthase kinase 3 (GSK3), a downstream enzyme, is also crucial in the tau phosphorylation and Aβ deposition. An overview of the function of PI3K/Akt pathway in the pathophysiology of AD is provided in this review, along with a discussion of recent developments in the pharmaceuticals and herbal remedies that target the PI3K/Akt signaling pathway. In conclusion, despite the challenges and hurdles, cumulative findings of novel targets and agents in the PI3K/Akt signaling axis are expected to hold promise for advancing AD prevention and treatment.

Abstract Switch defective/sucrose non-fermentable (SWI/SNF) chromatin remodeling complexes are multi-subunit machineries that establish and maintain chromatin accessibility and gene expression by regulating chromatin structure. However, how the remodeling activities of SWI/SNF complexes are regulated in eukaryotes remains elusive. B-cell lymphoma/leukemia protein 7 A/B/C (BCL7A/B/C) have been reported as subunits of SWI/SNF complexes for decades in animals and recently in plants; however, the role of BCL7 subunits in SWI/SNF function remains undefined. Here, we identify a unique role for plant BCL7A and BCL7B homologous subunits in potentiating the genome-wide chromatin remodeling activities of SWI/SNF complexes in plants. BCL7A/B require the catalytic ATPase BRAHMA (BRM) to assemble with the signature subunits of the BRM-Associated SWI/SNF complexes (BAS) and for genomic binding at a subset of target genes. Loss of BCL7A and BCL7B diminishes BAS-mediated genome-wide chromatin accessibility without changing the stability and genomic targeting of the BAS complex, highlighting the specialized role of BCL7A/B in regulating remodeling activity. We further show that BCL7A/B fine-tune the remodeling activity of BAS complexes to generate accessible chromatin at the juvenility resetting region (JRR) of the microRNAs MIR156A/C for plant juvenile identity maintenance. In summary, our work uncovers the function of previously elusive SWI/SNF subunits in multicellular eukaryotes and provides insights into the mechanisms whereby plants memorize the juvenile identity through SWI/SNF-mediated control of chromatin accessibility.

Abstract Background Mandibulofacial dysostosis with microcephaly (MFDM, OMIM# 610536) is a rare monogenic disease that is caused by a mutation in the elongation factor Tu GTP binding domain containing 2 gene (EFTUD2, OMIM* 603892). It is characterized by mandibulofacial dysplasia, microcephaly, malformed ears, cleft palate, growth and intellectual disability. MFDM can be easily misdiagnosed due to its phenotypic overlap with other craniofacial dysostosis syndromes. The clinical presentation of MFDM is highly variable among patients. Methods A patient with craniofacial anomalies was enrolled and evaluated by a multidisciplinary team. To make a definitive diagnosis, whole‐exome sequencing was performed, followed by validation by Sanger sequencing. Results The patient presented with extensive facial bone dysostosis, upward slanting palpebral fissures, outer and middle ear malformation, a previously unreported orbit anomaly, and spina bifida occulta. A novel, pathogenic insertion mutation (c.215_216insT: p.Tyr73Valfs*4) in EFTUD2 was identified as the likely cause of the disease. Conclusions We diagnosed this atypical case of MFDM by the detection of a novel pathogenetic mutation in EFTUD2. We also observed previously unreported features. These findings enrich both the genotypic and phenotypic spectrum of MFDM.

Abstract Aggressiveness and drug resistance are major challenges in the clinical treatment of glioblastoma (GBM). Our previously research reported a novel candidate oncogene ribosomal protein L22 like 1 (RPL22L1). The aim of this study was to elucidate the potential role and mechanism of RPL22L1 in progression and temozolomide (TMZ) resistance of GBM. Online database, tissue microarrays and clinical tissue specimens were used to evaluate the expression and clinical implication of RPL22L1 in GBM. We performed cell function assays, orthotopic and subcutaneous xenograft tumor models to evaluate the effects and molecular mechanisms of RPL22L1 on GBM. RPL22L1 expression was significantly upregulated in GBM and associated with poorer prognosis. RPL22L1 overexpression enhanced GBM cell proliferation, migration, invasion, TMZ resistance and tumorigenicity, which could be reduced by RPL22L1 knockdown. Further, we found RPL22L1 promoted mesenchymal phenotype of GBM and the impact of these effects was closely related to EGFR/STAT3 pathway. Importantly, we observed that STAT3 specific inhibitor (Stattic) significantly inhibited the malignant functions of RPL22L1, especially on TMZ resistance. RPL22L1 overexpressed increased combination drug sensitive of Stattic and TMZ both in vitro and in vivo. Moreover, Stattic effectively restored the sensitive of RPL22L1 induced TMZ resistance in vitro and in vivo. Our study identified a novel candidate oncogene RPL22L1 which promoted the GBM malignancy through STAT3 pathway. And we highlighted that Stattic combined with TMZ therapy might be an effective treatment strategy in RPL22L1 high-expressed GBM patients.

Abstract Aiming at the problem of crosstalk between microstrip lines, a method of reducing crosstalk by using cross‐shape resonators (CSR) structures is proposed. On the premise of not changing the spacing of microstrip lines, this method adds CSR structures between the coupled microstrip lines to increase the capacitive coupling and thus to suppress the far‐end crosstalk. Based on the analysis of the equivalent circuit of CSR structure, the parameters simulation and verification are carried out by ADS and HFSS software. Through HFSS simulation and physical test of the designed CSR structure, the results show that: the CSR structure can significantly reduce the far‐end crosstalk by about 15 dB in the frequency of 0–10 GHz, and the maximum can reach 43 dB. Compared with 3W crosstalk reduction method and rectangular‐shape resonators crosstalk reduction method, the crosstalk reduction effect is improved.

Abstract Brain metastasis (BM) frequently occurs in advanced non-small cell lung cancer (NSCLC) and is associated with poor clinical prognosis. Due to the location of metastatic lesions, the surgical resection is limited and the chemotherapy is ineffective because of the existence of the blood brain barrier (BBB). Therefore, it is essential to enhance our understanding about the underlying mechanisms associated with brain metastasis in NSCLC. In the present study, we explored the RNA-Seq data of brain metastasis cells from the GEO database, and extracted RNA collected from primary NSCLC tumors as well as paired brain metastatic lesions followed by microRNA PCR array. Meanwhile, we improved the in vivo model and constructed a cancer stem cell-derived transplantation model of brain metastasis in mice. Our data indicated that the level of miR-596-3p is high in primary NSCLC tumors, but significantly downregulated in the brain metastatic lesion. The prediction target of microRNA suggested that miR-596-3p was considered to modulate two genes essential in the brain invasion process, YAP1 and IL-8 that restrain the invasion of cancer cells and permeability of BBB, respectively. Moreover, in vivo experiments suggested that our model mimics the clinical aspect of NSCLC and improves the success ratio of brain metastasis model. The results demonstrated that miR-596-3p significantly inhibited the capacity of NSCLC cells to metastasize to the brain. Furthermore, these finding elucidated that miR-596-3p exerts a critical role in brain metastasis of NSCLC by modulating the YAP1-IL8 network, and this miRNA axis may provide a potential therapeutic strategy for brain metastasis.

Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma.Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan–Meier (K-M) analysis was performed to determine each cohort’s survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT–qPCR and IHC were used to identify the consistency of the signature in tumor tissue.Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT–PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model.Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy.

Abstract Aiming at the problem of far‐end crosstalk between microstrip lines, a method to improve crosstalk by using the TL‐shaped defective microstrip structure (DMS) is proposed. This method optimizes the ratio of the capacitive coupling and the inductive coupling between coupled microstrip lines by etching TL‐shaped DMSs on the microstrip lines, reducing the strength of electromagnetic coupling, thereby achieving an improvement in crosstalk. The quantitative equivalent model, S‐parameters, and full‐wave EM simulations are used to analyze the frequency response of the TL‐shaped DMS and crosstalk between the microstrip lines. The results of High Frequency Structure Simulator software simulation and sample texts show that the TL‐shaped DMS can effectively improve the far‐end crosstalk while ensuring the transmission of signals on the microstrip line. In the frequency range of 0–8 GHz, the far‐end crosstalk can be improved by about 11 dB, and the maximum can be improved by 42 dB. Compared with other DMSs, the TL‐shaped DMS has better crosstalk improvement effect and practical application potential.

ObjectivesThis study examined the effectiveness of Byrd's EarWell system for the treatment of auricular helix adhesion.MethodsThe newborns with helix adhesion were treated with ear molding. The photos of pinna were taken before, during, and after the treatment. The immediate and long-term outcomes, as well as the complications, were assessed by two independent plastic surgeons.Study designA retrospective study. Data on family history, neonatal weight, gestational age, delivery method, laterality, gender, age of initiating treatment, medical comorbidities, duration of treatment, and follow-up time were collected.Study site and periodFrom 2019 to 2021, infants treated with the EarWell System in the Eye and ENT Hospital of Fudan University were enrolled in this study.ResultsA total of 46 newborns (66 ears) with helix adhesion were included. The average onset time of treatment was 4.57 ± 3.63 weeks. The average duration of treatment was 7.40 ± 2.05 weeks. 97.0% ears’ (64/66) immediate results were excellent or good. During long-term follow-up, 95.5% ears achieved excellent or good outcomes. Age of initiation treatment significantly affected immediate (p = 0.001) and long-term (p = 0.004) outcomes.ConclusionsEarWell System was an effective method to correct auricular helix adhesion. Using this approach, patients with helix adhesion could avoid surgeries. Age of initiation treatment was the predictor of successful correction.

Rampant terrorism poses a serious threat to the national security of many countries worldwide, particularly due to separatism and extreme nationalism. This paper focuses on the development and application of a temporal self-exciting point process model to the terror data of three countries: the US, Turkey, and the Philippines. To account for occurrences with the same time-stamp, this paper introduces the order mark and reward term in parameter selection. The reward term considers the triggering effect between events in the same time-stamp but different order. Additionally, this paper provides comparisons between the self-exciting models generated by day-based and month-based arrival times. Another highlight of this paper is the development of a model to predict the number of terror events using a combination of simulation and machine learning, specifically the random forest method, to achieve better predictions. This research offers an insightful approach to discover terror event patterns and forecast future occurrences of terror events, which may have practical application towards national security strategies.

Background: Glioma is one of the most aggressive cancer types affecting the central nerve system, with poor overall survival (OS) rates. The present study aimed to construct a novel immune-related signature to predict prognosis and the efficiency of immunotherapy in patients with glioma.Methods: The mRNA expression data and other clinical information of patients with glioblastoma multiforme (GBM) and low grade glioma (LGG) were obtained from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. The immune-related genes were obtained from the Immunology Database and Analysis Portal database. Subsequently, an immune-related signature was created following the results obtained from the Least Absolute Shrinkage and Selection Operator regression model. To validate the predictability of the signature, Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves were created. Moreover, both univariate and multivariate analyses were carried out using the OS between this signature and other clinicopathologic factors, and a nomogram was constructed. In addition, the association between signature, immune cell infiltration, tumor mutation burden and immunophenoscore were determined.Results: Results of the present study using 118 GBM and LGG samples uncovered 15 immune-related genes that were also differently expressed in glioma samples. These were subsequently used to construct the immune-related signature. This signature exhibits the ability to predict prognosis, the infiltration of immune cells in the tumor microenvironment and the response of patients with glioma to immunotherapy.Conclusion: Results of the present study demonstrated that the aforementioned novel immune-related signature may accurately predict prognosis and the response of patients with glioma to immunotherapy.

Software defect prediction technology can effectively improve software quality. Depending on the code metrics, machine learning models are built to predict potential defects. Some researchers have indicated that the size metric could cause confounding effects and bias the prediction results. However, evidence shows that the real confounder should be the development cycle and number of developers, which could bring confounding effects when using code metrics for prediction. This paper proposes an improved confounding effect model, introducing a new confounding variable into the traditional model. On multiple projects, we experimentally analyzed the effect extent of the confounding variable. Furthermore, we verified that controlling confounding variables helps improve the predictive model’s performance.

Abstract Background Accumulating evidence demonstrates the oncogenic roles of lncRNA (long non-coding RNA) molecules in a wide variety of cancer types including glioma. Equally important, However, tumorigenic functions of lncRNA in glioma remain largely unclear. A recent study suggested lncRNA SNHG15 played a role for regulating angiogenesis in glioma but its role in the tumor microenvironment (TME) was not investigated. Methods First, we showed that SNHG15 was upregulated in GBM cells and associated with a poor prognosis for the patients of GBM using public databases. Next, we collected temozolomide sensitive (TMZ-S) and resistant (TMZ-R) clinical samples and demonstrated that co-culturing TMZ-R cells with HMC3 (microglial) cells promoted M2-polarization of HMC3 and the secretion of pro-GBM cytokines TGF-β and IL-6. Results Comparative qPCR analysis of TMZ-S and TMZ-R cells showed that a significantly higher level of SNHG15, coincidental with a higher level of Sox2, β-catenin, EGFR, and CDK6 in TMZ-R cells. Subsequently, using bioinformatics tool, a potential mechanistic route for SNHG15 to promote GBM tumorigenesis was by inhibiting tumor suppressor, miR-627-5p which leads to activation of CDK6. Gene-silencing technique was employed to demonstrate that suppression of SNHG15 indeed led to the suppression of GBM tumorigenesis, accompanied by an increase miR-627-5p and decreased its two oncogenic targets, CDK6 and SOX-2. In addition, SNHG15-silenced TMZ-R cells became significantly sensitive towards TMZ treatment and less capable of promoting M2-phenotype in the HMC3 microglial cells. We then evaluated the potential anti-GBM activity of CDK6 inhibitor, palbociclib, using TMZ-R PDX mouse models. Palbociclib treatment significantly reduced tumorigenesis in TMZ-R/HMC3 bearing mice and SNHG15 and CDK6 expression was significantly reduced while miR-627-5p level was increased. Additionally, palbociclib treatment appeared to overcome TMZ resistance as well as reduced M2 markers in HMC3 cells. Conclusion Together, we provided evidence supporting the usage of CDK6 inhibitor for TMZ-resistant GBM cases. Further investigation is warranted for the consideration of clinical trials. Graphical abstract

Abstract Objective Soybean seeds are an important source of vegetable proteins for both food and industry worldwide. Conglycinins (7S) and glycinins (11S), which are two major families of storage proteins encoded by a small family of genes, account for about 70% of total soy seed protein. Mutant alleles of these genes are often necessary in certain breeding programs, as the relative abundance of these protein subunits affect amino acid composition and soy food properties. In this study, we set out to test the efficiency of the CRISPR/Cas9 system in editing soybean storage protein genes using Agrobacterium rhizogenes-mediated hairy root transformation system. Results We designed and tested sgRNAs to target nine different major storage protein genes and detected DNA mutations in three storage protein genes in soybean hairy roots, at a ratio ranging from 3.8 to 43.7%. Our work provides a useful resource for future soybean breeders to engineer/develop varieties with mutations in seed storage proteins.

In this paper, a nonparametric spatial-temporal self-exciting point process is proposed to model clustering features in emergency calls. Gaussian kernel density functions are considered. The expectation-maximization algorithm is adopted for estimating the model. A simulation study is designed to carefully examine the performance of the proposed nonparametric method. The spatial-temporal patterns of the emergency calls in Montgomery County of Pennsylvania are studied using the proposed nonparametric model. The results demonstrate that the proposed nonparametric model captures the clustering phenomena present in the emergency calls from Montgomery County very well. Further, the proposed parameter estimation method results in robust and precise estimates.

Accurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery is modeled by the first-order RC equivalent circuit, and the multi-innovation least square (MILS) algorithm is used to perform online parameter identification of the model parameters. Then, the ensemble Kalman filter (EnKF) is introduced to estimate the state of charge. Finally, two typical experiments including constant current discharge experiment and cycling dynamic stress test are applied to evaluate the performance of the joint algorithm of MILS and EnKF. The experimental results show that the joint algorithm-based ensemble Kalman filter can achieve fast tracking and higher estimation accuracy for lithium-ion battery SOC.

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Software defect prediction technology can effectively detect potential defects in the software system. The most common method is to establish machine learning models based on software metrics for prediction. However, most of the prediction models are proposed without considering the confounding effects of size metric. The size metric has unexpected correlations with other software metrics and introduces biases into prediction results. Suitably removing these confounding effects to improve the prediction model’s performance is an issue that is still largely unexplored. This paper proposes a method that can causally remove the negative confounding effects of size metric. First, we quantify the confounding effects based on a causal graph. Then, we analyze each confounding effect to determine whether they are positive or negative, and only the negative confounding effects are removed. Extensive experimental results on eight data sets demonstrate the effectiveness of our proposed method. The prediction model’s performance can, in general, be improved after removing the negative confounding effects of size metric.

In Arabidopsis, the circadian rhythm is associated with multiple important biological processes and maintained by multiple interconnected loops that generate robust rhythms. The circadian clock central loop is a negative feedback loop composed of the core circadian clock components. TOC1 (TIMING OF CAB EXPRESSION 1) is highly expressed in the evening and negatively regulates the expression of CCA1 (CIRCADIAN CLOCK ASSOCIATED 1)/LHY (LATE ELONGATED HYPOCOTYL). CCA1/LHY also binds to the promoter of TOC1 and represses the TOC1 expression. Our recent research revealed that the histone modification complex comprising of LYSINE-SPECIFIC DEMETHYLASE 1 (LSD1)-LIKE 1/2 (LDL1/2) and HISTONE DEACETYLASE 6 (HDA6) can be recruited by CCA1/LHY to repress TOC1 expression. In this study, we found that HDA6, LDL1, and LDL2 can interact with TOC1, and the LDL1/2-HDA6 complex is associate with TOC1 to repress the CCA1/LHY expression. Furthermore, LDL1/2-HDA6 and TOC1 co-target a subset of genes involved in the circadian rhythm. Collectively, our results indicate that the LDL1/2-HDA6 histone modification complex is important for the regulation of the core circadian clock components.

BREVIPEDICELLUS (BP or KNAT1), a class-I KNOTTED1-like homeobox (KNOX) transcription factor in Arabidopsis thaliana, contributes to shaping the normal inflorescence architecture through negatively regulating other two class-I KNOX genes, KNAT2 and KNAT6. However, the molecular mechanism of BP-mediated transcription regulation remains unclear. In this study, we showed that BP directly interacts with the SWI2/SNF2 chromatin remodeling ATPase BRAHMA (BRM) both in vitro and in vivo. Loss-of-function BRM mutants displayed inflorescence architecture defects, with clustered inflorescences, horizontally orientated pedicels, and short pedicels and internodes, a phenotype similar to the bp mutants. Furthermore, the transcript levels of KNAT2 and KNAT6 were elevated in brm-3, bp-9 and brm-3 bp-9 double mutants. Increased histone H3 lysine 4 tri-methylation (H3K4me3) levels were detected in brm-3, bp-9 and brm-3 bp-9 double mutants. Moreover, BRM and BP co-target to KNAT2 and KNAT6 genes, and BP is required for the binding of BRM to KNAT2 and KNAT6. Taken together, our results indicate that BP interacts with the chromatin remodeling factor BRM to regulate the expression of KNAT2 and KNAT6 in control of inflorescence architecture.

The chromatin remodeler BRAHMA (BRM) is a Trithorax Group (TrxG) protein that antagonizes the functions of Polycomb Group (PcG) proteins in fly and mammals. Recent studies also implicate such a role for Arabidopsis (Arabidopsis thaliana) BRM but the molecular mechanisms underlying the antagonism are unclear. To understand the interplay between BRM and PcG during plant development, we performed a genome-wide analysis of trimethylated histone H3 lysine 27 (H3K27me3) in brm mutant seedlings by chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq). Increased H3K27me3 deposition at several hundred genes was observed in brm mutants and this increase was partially supressed by removal of the H3K27 methyltransferase CURLY LEAF (CLF) or SWINGER (SWN). ChIP experiments demonstrated that BRM directly binds to a subset of the genes and prevents the inappropriate association and/or activity of PcG proteins at these loci. Together, these results indicate a crucial role of BRM in restricting the inappropriate activity of PcG during plant development. The key flowering repressor gene SHORT VEGETATIVE PHASE (SVP) is such a BRM target. In brm mutants, elevated PcG occupancy at SVP accompanies a dramatic increase in H3K27me3 levels at this locus and a concomitant reduction of SVP expression. Further, our gain- and loss-of-function genetic evidence establishes that BRM controls flowering time by directly activating SVP expression. This work reveals a genome-wide functional interplay between BRM and PcG and provides new insights into the impacts of these proteins in plant growth and development.

A new methodology, which combines nonparametric method based on local functional coefficient autoregressive (LFAR) form with chaos theory and regional method, is proposed for multistep prediction of chaotic time series. The objective of this research study is to improve the performance of long-term forecasting of chaotic time series. To obtain the prediction values of chaotic time series, three steps are involved. Firstly, the original time series is reconstructed in m-dimensional phase space with a time delay τ by using chaos theory. Secondly, select the nearest neighbor points by using local method in the m-dimensional phase space. Thirdly, we use the nearest neighbor points to get a LFAR model. The proposed model’s parameters are selected by modified generalized cross validation (GCV) criterion. Both simulated data (Lorenz and Mackey-Glass systems) and real data (Sunspot time series) are used to illustrate the performance of the proposed methodology. By detailed investigation and comparing our results with published researches, we find that the LFAR model can effectively fit nonlinear characteristics of chaotic time series by using simple structure and has excellent performance for multistep forecasting.

BACKGROUND: ABO blood groups have been suggested to contribute to the development of certain tumors; however, the associations between ABO blood groups and the incidence and metastases of cutaneous malignant melanomas have not been fully evaluated in Chinese populations. Thus, we investigated these associations with a case-controlled study in northeast of China. METHODS: A total of 482 patients with cutaneous malignant melanoma and 3,068 healthy- controls were enrolled for the study between 2001 and 2012 at The Tumor Hospital of Harbin Medical University. A multivariate logistic model was used to evaluate adjusted odds ratios (ORs) and 95% confidence intervals (CI) for the incidence and metastases of cutaneous malignant melanoma. RESULTS: Blood type A individuals had higher tumor incidence and metastasis compared to those with blood type O (OR = 1.575; 95% CI = 1.208-2.053, p = 0.001; OR = 2.004; 95% CI = 1.032-3.889, p = 0.040), after adjusting for age, gender, smoking status and alcohol consumption. CONCLUSIONS: Blood type A was associated with higher incidence and metastasis of cutaneous malignant melanoma but future studies are needed to examine the mechanisms linking cutaneous malignant melanoma to ABO blood types.