Climate-related naturally occurring epimutation and their roles in plant adaptation in A. thaliana.

DNA methylation has been proposed to be an important mechanism that allows plants to respond to their environments sometimes entirely uncoupled from genetic variation. To understand the genetic basis, biological functions and climatic relationships of DNA methylation at a population scale in Arabidopsis thaliana, we performed a genome-wide association analysis with high-quality single nucleotide polymorphisms (SNPs), and found that ~56% on average, especially in the CHH sequence context (71%), of the differentially methylated regions (DMRs) are not tagged by SNPs. Among them, a total of 3235 DMRs are significantly associated with gene expressions and potentially heritable. 655 of the 3235 DMRs are associated with climatic variables, and we experimentally verified one of them, HEI10 (HUMAN ENHANCER OF CELL INVASION NO.10). Such epigenetic loci could be subjected to natural selection thereby affecting plant adaptation, and would be expected to be an indicator of accessions at risk. We therefore incorporated these climate-related DMRs into a gradient forest model, and found that the natural A. thaliana accessions in Southern Europe that may be most at risk under future climate change. Our findings highlight the importance of integrating DNA methylation that is independent of genetic variations, and climatic data to predict plants' vulnerability to future climate change.

Patients' response during the co-circulation of multiple respiratory diseases in China-based on the self-regulation common-sense model.

Background: Following the COVID-19 pandemic, another large-scale respiratory epidemic has emerged in China, causing significant social impact and disruption. The article is to explore the patients' psychological and behavioral responses to the enhancement of healthcare quality. Methods: Based on the five dimensions of the Self-Regulation Common-Sense Model, we developed an interview outline to explore the process by which patients identify disease symptoms to guide action plans and coping strategies. The researchers used a semi-structured interview format to simultaneously collect data online and offline. This study gathered data from 12 patients with mixed respiratory infections, comprising 58% females and 42% males; the average age was 30.67 years (SD 20.00), with 91.7% infected with two pathogens and 8.3% with three. The data analysis employed the KJ method, themes were inductively analyzed and categorized from semi-structured interview results, which were then organized into a coherent visual and logical pathway. Key results: The study identified 5 themes: (1) Autonomous Actions Prior to Seeking Medical Care; (2) Decision-Making in Seeking Hospital Care; (3) Disease Shock; (4) Public Crisis Response; (5) Information Cocoon. Conclusion: The pandemic of respiratory infectious diseases has not ceased in recent years. Following the COVID-19 pandemic, China is now facing a trend of concurrent epidemics involving multiple respiratory pathogens. This study centers on patients' health behaviors, exploring the potential relationships among various factors that affect these behaviors. The aim is to provide references and grounds for the improvement of healthcare services when such public health events reoccur.

GwasWA: A GWAS one-stop analysis platform from WGS data to variant effect assessment.

Using the accumulated whole-genome sequencing (WGS) data and assessing the functional effects of genetic variants, particularly non-coding variants, help identify new and rare variants and decipher the molecular mechanisms underlying diseases and traits but presents significant challenges. GwasWA is a comprehensive and efficient platform to identify causal variants and assess their functional effects based on WGS data. It covers the entire workflow from downloading and processing WGS data to detecting associated variants and assessing their functional effects with optimized configurations, standardized input/output formats, personalized analysis options, data visualization, and parallel processing capability that is crucial for large-scale studies. Applying GwasWA to real datasets identified three novel genes related to seed size and revealed the regulatory mechanism underlying the linkage between a human non-coding variant, rs80067372, and tumor necrosis factor levels. These results highlight the capability of GwasWA to detect novel variants based on WGS data and provide comprehensive insights into the molecular mechanisms underlying the association of variants with diseases and traits, thus contributing to medicine and biology. GwasWA and its documentation are freely available at https://github.com/unicorn-23/GwasWA.

The association of depressive symptoms and medication adherence in asthma patients: The mediation effect of medication beliefs.

BACKGROUND: The significant role of depression in influencing medication beliefs, which are pivotal cognitive factors that strongly influence medication adherence, has been established. Poor adherence to asthma-controlled medication poses an significant barrier to achieving optimal asthma management. OBJECTIVE: To explore the potential mediating effects of medication beliefs on the relationship between depressive symptoms and medication adherence in patients with asthma. METHODS: Demographic and clinical characteristics, depressive symptoms, medication adherence, and medication beliefs were collected using questionnaires. Structural equation modeling, was utilized to model medication beliefs as mediators in the relationship between depressive symptoms and medication adherence. Bootstrapping was performed to analyze the mediation- and contrast-specific indirect effects of the two medication beliefs. RESULTS: Among the patients who participated in the study, 29.6 % with depressive symptoms were more prone to poor adherence and exhibited skepticism toward asthma medications. Depression had a direct effect (direct effect = -0.275, 95%CI: -0.369 to -0.190) and an indirect effect on adherence mediated by medication beliefs (indirect effect = -0.168, 95%CI: -0.224 to -0.121). The specific mediation effect of concern belief was stronger than that of necessity belief (difference = -0.076, 95%CI: -0.132 to -0.029). CONCLUSION: Depressive symptoms have a direct impact on medication adherence as well as an indirect effect mediated by beliefs about medication, particularly concerns belief.

PlantCADB: A Comprehensive Plant Chromatin Accessibility Database.

Chromatin accessibility landscapes are essential for detecting regulatory elements, illustrating the corresponding regulatory networks, and, ultimately, understanding the molecular basis underlying key biological processes. With the advancement of sequencing technologies, a large volume of chromatin accessibility data has been accumulated and integrated for humans and other mammals. These data have greatly advanced the study of disease pathogenesis, cancer survival prognosis, and tissue development. To advance the understanding of molecular mechanisms regulating plant key traits and biological processes, we developed a comprehensive plant chromatin accessibility database (PlantCADB) from 649 samples of 37 species. These samples are abiotic stress-related (such as heat, cold, drought, and salt; 159 samples), development-related (232 samples), and/or tissue-specific (376 samples). Overall, 18,339,426 accessible chromatin regions (ACRs) were compiled. These ACRs were annotated with genomic information, associated genes, transcription factor footprint, motif, and single-nucleotide polymorphisms (SNPs). Additionally, PlantCADB provides various tools to visualize ACRs and corresponding annotations. It thus forms an integrated, annotated, and analyzed plant-related chromatin accessibility resource, which can aid in better understanding genetic regulatory networks underlying development, important traits, stress adaptations, and evolution.PlantCADB is freely available at https://bioinfor.nefu.edu.cn/PlantCADB/.

Climate-responsive DNA methylation is involved in the biosynthesis of lignin in birch.

Lignin is one of the most important secondary metabolites and essential to the formation of cell walls. Changes in lignin biosynthesis have been reported to be associated with environmental variations and can influence plant fitness and their adaptation to abiotic stresses. However, the molecular mechanisms underlying this association remain unclear. In this study, we evaluated the relations between the lignin biosynthesis and environmental factors and explored the role of epigenetic modification (DNA methylation) in contributing to these relations if any in natural birch. Significantly negative correlations were observed between the lignin content and temperature ranges. Analyzing the transcriptomes of birches in two habitats with different temperature ranges showed that the expressions of genes and transcription factors (TFs) involving lignin biosynthesis were significantly reduced at higher temperature ranges. Whole-genome bisulfite sequencing revealed that promoter DNA methylation of two NAC-domain TFs, BpNST1/2 and BpSND1, may be involved in the inhibition of these gene expressions, and thereby reduced the content of lignin. Based on these results we proposed a DNA methylation-mediated lignin biosynthesis model which responds to environmental factors. Overall, this study suggests the possibility of environmental signals to induce epigenetic variations that result in changes in lignin content, which can aid to develop resilient plants to combat ongoing climate changes or to manipulate secondary metabolite biosynthesis for agricultural, medicinal, or industrial values.

A CD8+ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

Background: Skin cutaneous melanoma (SKCM) is the most frequently encountered tumor of the skin. Immunotherapy has opened a new horizon in melanoma treatment. We aimed to construct a CD8+ T cell-associated immune gene prognostic model (CDIGPM) for SKCM and unravel the immunologic features and the benefits of immunotherapy in CDIGPM-defined SKCM groups. Method: Single-cell SKCM transcriptomes were utilized in conjunction with immune genes for the screening of CD8+ T cell-associated immune genes (CDIGs) for succeeding assessment. Thereafter, through protein-protein interaction (PPI) networks analysis, univariate COX analysis, and multivariate Cox analysis, six genes (MX1, RSAD2, IRF2, GBP2, IFITM1, and OAS2) were identified to construct a CDIGPM. We detected cell proliferation of SKCM cells transfected with IRF2 siRNA. Then, we analyzed the immunologic features and the benefits of immunotherapy in CDIGPM-defined groups. Results: The overall survival (OS) was much better in low-CDIGPM group versus high CDIGPM group in TCGA dataset and GSE65904 dataset. On the whole, the results unfolded that a low CDIGPM showed relevance to immune response-correlated pathways, high expressions of CTLA4 and PD-L1, a high infiltration rate of CD8+ T cells, and more benefits from immunotherapy. Conclusion: CDIGPM is an good model to predict the prognosis, the potential immune escape from immunotherapy for SKCM, and define immunologic and molecular features.

MAG2 and MAL Regulate Vesicle Trafficking and Auxin Homeostasis With Functional Redundancy.

Auxin is a central phytohormone and controls almost all aspects of plant development and stress response. Auxin homeostasis is coordinately regulated by biosynthesis, catabolism, transport, conjugation, and deposition. Endoplasmic reticulum (ER)-localized MAIGO2 (MAG2) complex mediates tethering of arriving vesicles to the ER membrane, and it is crucial for ER export trafficking. Despite important regulatory roles of MAG2 in vesicle trafficking, the mag2 mutant had mild developmental abnormalities. MAG2 has one homolog protein, MAG2-Like (MAL), and the mal-1 mutant also had slight developmental phenotypes. In order to investigate MAG2 and MAL regulatory function in plant development, we generated the mag2-1 mal-1 double mutant. As expected, the double mutant exhibited serious developmental defects and more alteration in stress response compared with single mutants and wild type. Proteomic analysis revealed that signaling, metabolism, and stress response in mag2-1 mal-1 were affected, especially membrane trafficking and auxin biosynthesis, signaling, and transport. Biochemical and cell biological analysis indicated that the mag2-1 mal-1 double mutant had more serious defects in vesicle transport than the mag2-1 and mal-1 single mutants. The auxin distribution and abundance of auxin transporters were altered significantly in the mag2-1 and mal-1 single mutants and mag2-1 mal-1 double mutant. Our findings suggest that MAG2 and MAL regulate plant development and auxin homeostasis by controlling membrane trafficking, with functional redundancy.

KIAA1429 is a potential prognostic marker in colorectal cancer by promoting the proliferation via downregulating WEE1 expression in an m6A-independent manner.

N6-methyladenosine (m6A), the most abundant mRNA modification in mammals, is involved in the metabolism of mRNA. KIAA1429 is regarded as the largest m6A methyltransferase and plays an important role in m6A modification. However, the prognostic value and function of KIAA1429 in colorectal cancer (CRC) are unclear. Quantitative real-time PCR and immunohistochemical assays were performed to evaluate the expression of KIAA1429 in CRC tissues. Kaplan-Meier survival curves and log-rank tests were used to assess the association between KIAA1429 expression and the prognosis of patients with CRC. CCK-8 assays, colony formation assays, cell cycle assays, and xenograft experiments were performed to investigate the effect of KIAA1429 on cell proliferation. RNA immunoprecipitation, methylated RNA immunoprecipitation assays, and RNA stability assays were conducted to explore the underlying mechanism. KIAA1429 was significantly upregulated in CRC tissues compared with adjacent normal tissues. Patients with higher expression of KIAA1429 had shorter overall survival than those with lower expression. Functionally, KIAA1429 promoted CRC cell proliferation in vitro and in vivo. Mechanistically, KIAA1429 negatively regulated the expression of WEE1 by decreasing its stability in an m6A-independent manner by binding to the third segment in the 3'-UTR of WEE1 mRNA. Moreover, butyrate decreased the expression of KIAA1429 by downregulating the level of the transcription factor NFκB1. Our findings indicated that KIAA1429 plays an oncogenic role in CRC cells by inhibiting the expression of WEE1 in an m6A-independent manner and is associated with poor survival in CRC patients. These results suggested that KIAA1429 might be a potential prognostic marker for CRC.

BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method.

SUMMARY: Processing raw reads of RNA-sequencing (RNA-seq) data, no matter public or newly sequenced data, involves a lot of specialized tools and technical configurations that are often unfamiliar and time-consuming to learn for non-bioinformatics researchers. Here, we develop the R package BP4RNAseq, which integrates the state-of-art tools from both alignment-based and alignment-free quantification workflows. The BP4RNAseq package is a highly automated tool using an optimized pipeline to improve the sensitivity and accuracy of RNA-seq analyses. It can take only two non-technical parameters and output six formatted gene expression quantification at gene and transcript levels. The package applies to both retrospective and newly generated bulk RNA-seq data analyses and is also applicable for single-cell RNA-seq analyses. It, therefore, greatly facilitates the application of RNA-seq. AVAILABILITY AND IMPLEMENTATION: The BP4RNAseq package for R and its documentation are freely available at https://github.com/sunshanwen/BP4RNAseq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Revisiting genome-wide association studies from statistical modelling to machine learning.

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of diseases, to develop new drugs, to advance precision medicine and to boost breeding. However, the potential of GWAS is still underexploited due to methodological limitations. Many challenges have emerged, including detecting epistasis and single-nucleotide polymorphisms (SNPs) with small effects and distinguishing causal variants from other SNPs associated through linkage disequilibrium. These issues have motivated advancements in GWAS analyses in two contrasting cultures-statistical modelling and machine learning. In this review, we systematically present the basic concepts and the benefits and limitations in both methods. We further discuss recent efforts to mitigate their weaknesses. Additionally, we summarize the state-of-the-art tools for detecting the missed signals, ultrarare mutations and gene-gene interactions and for prioritizing SNPs. Our work can offer both theoretical and practical guidelines for performing GWAS analyses and for developing further new robust methods to fully exploit the potential of GWAS.

Identifying Antifreeze Proteins Based on Key Evolutionary Information.

Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

Comparative drought resistance of temperate grassland species: testing performance trade-offs and the relation to distribution.

To improve projections of consequences of increasing intensity and frequency of drought events for grasslands, we need a thorough understanding of species performance responses to drought, of performance trade-offs and how drought resistance is related to species distributions. However, comparative and quantitative assessments of whole-plant drought resistance that allow to rigorously address these aspects are lacking for temperate grassland species. We conducted a common garden experiment with 40 common temperate grassland species to compare species survival and growth under intense drought and well-irrigated conditions. Overall, survival and growth were significantly reduced under drought, with the effect varying across species. Species ranking of drought damage and survival remained consistent with progressing drought. No performance trade-offs emerged between optimal growth and drought resistance of survival ('growth-stress tolerance' trade-off hypothesis), or between growth under well-watered and dry conditions ('growth rates' trade-off hypothesis). Species local- and large-scale association with moisture (Ellenberg F value and rainfall niche) was not related to their drought resistance. Overall, our results imply that trade-offs and differences of species fundamental drought resistance are not the main drivers of hydrological niche differentiation, species coexistence and their distribution across moisture gradients. The comparative experimental assessment of species whole-plant drought responses we present provides a basis to increase our understanding of current grassland responses to variation of moisture regimes and for projecting consequences of future changes.

KIAA1429 regulates cell proliferation by targeting c-Jun messenger RNA directly in gastric cancer.

N6-methyladenosine (m6A) modification regulatory proteins are involved in the development of many types of cancer. KIAA1429 serves as a scaffold in bridging the catalytic core components of the m6A methyltransferase complex. The role of KIAA1429 in gastric cancer and its related mechanism has not been reported upon. The expression of KIAA1429 was detected in human gastric cancer tissues and cell lines by quantitative real-time polymerase chain reaction and western blot. The effects of KIAA1429 on gastric cancer proliferation were evaluated by cell counting kit assays, colony formation assays, flow cytometry assay, and in vivo experiments with nude mice. And messenger RNA (mRNA) high-throughput sequencing, RNA immunoprecipitation assay (RIP), luciferase assay, and a rescue experiment were used to identify the relationship between KIAA1429 and its specific targeted gene, c-Jun. We found that KIAA1429 was upregulated in gastric cancer tissues, and expressed lower in adjacent tissues. The upregulated KIAA1429 promoted proliferation and downregulated KIAA1429 was proved to inhibit proliferation of gastric cancer in vitro and in vivo. Then, we identified the potential KIAA1429 regulating gene as c-Jun by mRNAs high-throughput sequencing and RIP assay. By luciferase assay, we verified that KIAA1429 regulated the expression of c-Jun in an m6A-independent manner. Finally, the overexpression of c-Jun rescued the inhibition of proliferation caused by KIAA1429 knockdown in gastric cancer cells. KIAA1429 could act as an oncogene in gastric cancer by stabilizing c-Jun mRNA in an m6A-independent manner. This highlights the functional role for KIAA1429 as a potential prognostic biomarker and therapeutic target in gastric cancer.

Methyltransferase like 3 promotes colorectal cancer proliferation by stabilizing CCNE1 mRNA in an m6A-dependent manner.

m6A modification is the most prevalent RNA modification in eukaryotes. As the critical N6-methyladenosine (m6A) methyltransferase, the roles of methyltransferase like 3 (METTL3) in colorectal cancer (CRC) are controversial. Here, we confirmed that METTL3, a critical m6A methyltransferase, could facilitate CRC progression in vitro and in vivo. Further, we found METTL3 promoted CRC cell proliferation by methylating the m6A site in 3'-untranslated region (UTR) of CCNE1 mRNA to stabilize it. Moreover, we found butyrate, a classical intestinal microbial metabolite, could down-regulate the expression of METTL3 and related cyclin E1 to inhibit CRC development. METTL3 promotes CRC proliferation by stabilizing CCNE1 mRNA in an m6A-dependent manner, representing a promising therapeutic strategy for the treatment of CRC.

Machine learning and its applications in plant molecular studies.

The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies.

Genetic variants in SLC22A3 contribute to the susceptibility to colorectal cancer.

Previous a genome-wide association study (GWAS) of colorectal cancer in Japanese population has identified a risk region at the chromosome 6q26-q27 associated with colorectal cancer risk. However, the causal gene at this locus remained unclear. In our study, we enrolled a total of 14 candidate functional single nucleotide polymorphisms (SNPs) at 6q26-q27 (318 kb), and then genotyped them by TaqMan method in a Chinese population including 1,147 colorectal cancer cases and 1,203 controls. Among that, 5 SNPs were identified statistical association with colorectal cancer risk by logistic regression analysis. Of which, SNP rs420038 G > A in SLC22A3 was related to decreased risk of colorectal cancer (adjusted odds ratio (OR) = 0.79, 95% confidence interval (CI) = 0.67-0.94, p = 0.007), and also associated with lower expression of SLC22A3 (p = 0.040) using expression quantitative trait loci (eQTL) analysis. Moreover, by the luciferase assays, we found that compared to the G allele of rs420038, the A allele could suppress the activity of the promoter in SLC22A3. Furthermore, the expression of SLC22A3 was significantly higher in colorectal cancer tissues than that in paired normal tissues (p < 0.001). Meanwhile, the phenotypes of proliferation, migration, invasion, cell cycle and apoptosis of colorectal cancer cell were significantly affected by SLC22A3 in vitro. Our results revealed a novel susceptible locus, rs420038 in SLC22A3, which may be involved in colorectal cancer development and progression.

Homocysteine and Digestive Tract Cancer Risk: A Dose-Response Meta-Analysis.

BACKGROUND: Homocysteine, a key component in one-carbon metabolism, is of great importance in remethylation. Many epidemiologic studies have assessed the association between homocysteine and risk of digestive tract cancer, but the results are inconsistent. OBJECTIVE: The objective of our meta-analysis is to assess the association between homocysteine and digestive tract cancer risk. METHODS: Comprehensive searches were performed on the PubMed, Embase, Cochrane, and Web of Science databases up to September 25, 2018, to identify relevant studies. Thirteen studies were included in the meta-analysis. Odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs) were used to estimate the strength of the relationship between homocysteine and the risk of digestive tract cancer. RESULTS: The pooled OR of digestive tract cancer risk for patients with the highest categories of blood homocysteine levels versus the lowest categories was 1.27 (95% CI, 1.15, 1.39) with no significant heterogeneity observed (P = 0.798, I 2 = 0.0%). Moreover, the dose-response analysis revealed that each 5µmol/L increase in homocysteine increased the incidence of digestive tract cancer by 7%. CONCLUSION: Generally, our results indicated that elevated homocysteine was associated with higher risk of digestive tract cancer. That is, homocysteine concentration may be a potential biomarker for occurrence of digestive tract cancer.

Choline and betaine consumption lowers cancer risk: a meta-analysis of epidemiologic studies.

A number of human and animal in vitro or in vivo studies have investigated the relationship between dietary choline and betaine and cancer risk, suggesting that choline and betaine consumption may be protective for cancer. There are also a few epidemiologic studies exploring this relationship, however, with inconsistent conclusions. The PubMed and Embase were searched, from their inception to March 2016, to identify relevant studies and we brought 11 articles into this meta-analysis eventually. The pooled relative risks (RRs) of cancer for the highest versus the lowest range were 0.82 (95% CI, 0.70 to 0.97) for choline consumption only, 0.86 (95%CI, 0.76 to 0.97) for betaine consumption only and 0.60 (95%CI, 0.40 to 0.90) for choline plus betaine consumption, respectively. Significant protective effect of dietary choline and betaine for cancer was observed when stratified by study design, location, cancer type, publication year, sex and quality score of study. An increment of 100 mg/day of choline plus betaine intake helped reduce cancer incidence by 11% (0.89, 95% CI, 0.87 to 0.92) through a dose-response analysis. To conclude, choline and betaine consumption lowers cancer incidence in this meta-analysis, but further studies are warranted to verify the results.