Lycium barbarum; LB) is an important agricultural product with a high content of pharmacologically important secondary metabolites such as phenylpropanoids. A close relative, black wolfberry , endemic to the salinized deserts of northwestern China, is used only locally. The two fruits exhibit many morphological and phytochemical differences, but genetic mechanisms underlying them remain poorly explored. In order to identify the genes of interest for further studies, we studied transcriptomic (Illumina HiSeq) and metabolomic (LC-MS) profiles of the two fruits during five developmental stages (young to ripe). As expected, we identified much higher numbers of significantly differentially regulated genes (DEGs) than metabolites. The highest numbers were identified in pairwise comparisons including the first stage for both species, but total numbers were consistently somewhat lower for the LR. The number of differentially regulated metabolites in pairwise comparisons of developmental stages varied from 66 (stages 3 vs 4) to 133 (stages 2 vs 5) in both species. We identified a number of genes  and metabolites  that may be of interest to future functional studies of stress adaptation in plants. As LB is also highly suitable for combating soil desertification and alleviating soil salinity/alkalinity/pollution, its potential for human use may be much wider than its current, highly localized, relevance.Red wolfberry (or goji berry,  Although the clinical efficacy remains to be fully confirmed, there is some evidence that goji extracts may be beneficial for the prevention and treatment of age-related disorders, diabetes, hyperlipidaemia, cancer, hepatitis, immune disorders, thrombosis, and male infertility3. Specifically, Lycium fruits have relatively high content of bioactive components believed to be pharmacologically important, e.g. possessing immuno-enhancement and antioxidative activities, such as polyphenols, phenylpropanoids, carotenoids and polysaccharides6.Goji berry , used in China as food and medicine for millennia, recently achieved almost global popularity due to it being advertised as a healthy, ageing-preventing food. In the traditional Chinese medicine, goji is traditionally consumed for its alleged anti-aging, tranquilizing and Yin strengthening propertiesLycium barbarum and L. chinense (Chinese boxthorn) but nearly 90% of all commercially available goji berries belong to the former species2. Although the native range of this species is probably in the Mediterranean Basin, a majority of global commercial production takes place in arid and semi-arid areas of two provinces in Northwest China, Ningxia and Xinjiang1. Lycium ruthenicum (Russian box thorn or black wolfberry), a very close relative of L. barbarum and L. chinense7, is a wild perennial thorny shrub native to Northwest China, whose resistance to the harsh environment of saline deserts makes it a popular choice plant for combating soil desertification and for alleviating soil salinity/alkalinity9. It is also used in the local folk medicine and as food8, and studies indicate that it has notable pharmaceutical effects10. As these two species, and their fruits, are referred to by a range of (often overlapping) names1, to avoid confusion we refer to the fruit of L. barbarum (LB) as red wolfberry, and fruit of L. ruthenicum (LR) as black wolfberry. Despite their close phylogenetic relationship, the fruits of these two species exhibit distinct phenotypic profiles of during all developmental stages, including their shape, size, colour, taste, nutritional value and pharmacological properties11. As opposed to the red and elongated mature red wolfberry, black wolfberry is dark-purple or black, round, and smaller. It is also known that metabolic phenotypes of these two fruits differ significantly, particularly in the content of fatty acids, phenols and antioxidant capacities, which are much higher in black wolfberry, while the content of carotenoids, sugars, amino acids and osmolytes is higher in the red wolfberry4.Two closely related species are sometimes sold as goji berries, 12, so genetic mechanisms underpinning these phenotypic differences remain only partially understood. The objective of this study was to contribute to our understanding of the complexity of ripening processes of these two fruits in different environments. To achieve this, we collected L. barbarum and L. ruthenicum fruits at five developmental stages, from young to ripe fruit, and sequenced their transcriptomes and metabolomes. These data shall help us better understand genetic underpinnings of both within- and between-species phenotypic differences that fruits of these two species exhibit during their respective ripening processes.Fruit ripening is a complex developmental process, coordinated by a network of interacting genes and signalling pathwaysst and August 20th 2017 from nine wild L. ruthenicum (LR) shrubs growing in the vicinity of Bayan Taolaisu Wooden, Ejina, Alxa, Inner Mongolia, China  and nine 5-year-old cultured L. barbarum (LB) shrubs from the germplasm nursery of the Ningxia Academy of Agriculture and Forestry Science, Lu Hua Tai plantations, Xixia District, Yinchuan, Ningxia, China  . The extracted total RNA was treated with RQ1 DNase (Promega), and its quality and quantity then determined by measuring the absorbance at A260/A280 and A260/A230 using Smartspec plus (BioRad) spectrophotometer. RNA integrity was further verified by agarose gel electrophoresis. For each sample, 10 μg of total RNA was used for RNA-seq library preparation. Polyadenylated mRNAs were purified and concentrated with oligo (dT)-conjugated magnetic beads (Invitrogen) before being used for directional RNA-seq library preparation. The cDNA libraries were prepared from the purified mRNAs using the TruSeq Stranded Total RNA LT Sample Prep Kit . Briefly: purified mRNAs were iron-fragmented at 95 °C, followed by end repair and 5′ adaptor ligation. Reverse transcription was then performed with RT primer included in the kit, harbouring a 3′ adaptor sequence and a randomized hexamer. The cDNAs were purified and amplified, and PCR products in the range 200–500 bp were purified, quantified and stored at −80 °C until the sequencing step. For high-throughput sequencing, the libraries were prepared following the manufacturer’s instructions and Illumina HiSeq. 2000 system used for 150 nt single-end sequencing.13: firstly we discarded all reads containing more than two N bases, then reads were processed by clipping the adaptor, removing low quality bases, and discarding short (<16 nt) reads. Uniquely localized clean reads were used to analyse the data quality with dupRadar14 with default parameters, and to calculate the read number and FPKM value (fragments per kilobase of transcripts per million mapped fragments) for each unigene according to reads and their genomic location15. De novo transcriptome assembly was conducted using Trinity program16, with default settings. The assembled transcriptomes were clustered using Corset17, a software designed for obtaining gene-level counts from any de novo transcriptome assembly. Unigenes were annotated by querying them against several public databases: Nr (NCBI non-redundant protein sequences), Nt (NCBI non-redundant nucleotide sequences), Pfam (Protein family)18, KOG/COG (Clusters of Orthologous Groups of proteins)20, Swiss-Prot 21, KO (KEGG Ontology)22, and GO (Gene Ontology)23. To further analyse the metabolic pathways different between the two sets of samples, all unigenes were queried against the KEGG pathway database. All BLASTx24 searches were performed with the e-value of 1E−5.Raw reads were filtered using Cutadapt 1.7.125. The fold–changes in gene expression were also estimated with this package, and False Discovery Rate (FDR) thresholds were set to 0.01 and 0.7 R2. After obtaining the expression level of all genes in all of the samples, differentially expressed genes (DEGs) between the two fruits in the same developmental stages were analysed using Edge R26 with TMM normalization27. The following parameters were used to set the threshold for identifying DEGs: FDR < 0.01 and |fold change| ≥ 2.0. For each gene, the p-value was obtained on the basis of the model of negative binomial distribution. Benjamini-Hochberg procedure28 was used to control the false discovery rate (FDR) and infer the q-value .As our samples represented a time-series experiment, to explore temporal profiles of DEGs during the fruit development, we analysed the gene expression patterns using a time-series analysis tool maSigPro, an R package designed for identification of significantly different temporal expression profiles in RNA-seq data and microarray experiments29 and Java Treeview30 software programs. To predict gene functions and calculate the functional category distribution frequency, KEGG analyses were employed using DAVID bioinformatics resources31. Functional networks were constructed by calculating the Pearson correlation coefficient (PCC) of the DEGs, and Cytoscape 3.0.2 was used to display the co-expression network32. Reliability of the RNA-seq data was corroborated by studying the expression of five randomly selected DEGs by qPCR , mass-to-charge ratio (MZ), observations (samples) and peak intensity were calculated. Multivariate analysis (PCA) was performed using SIMCA-P 13.0 software . Metabolites were assigned to pathways using the KEGG database. Significantly different metabolic pathways were identified using the PLS-DA , and the following criteria: VIP (Variable Importance in the Projection) value > 1 and P-value < 0.05. Data were processed and analysed by the Wuhan Metware Biotechnology Co., Ltd. .Five fruit samples were used for each stage . About 100 mg of fruit tissue was crushed using a mixer mill  with a zirconia bead for 1.5 min at 30 Hz and extracted overnight at 4 °C with 0.6 ml 70% aqueous methanol. Following centrifugation at 10,000 g for 10 min, the extracts were absorbed  and filtrated  before LCMS analysis. The sample extracts were analysed using an LC-ESI-MS/MS system . The analytical conditions were as follows: HPLC column, Waters ACQUITY UPLC HSS T3 C18 ; and the mobile phase consisted of solvent A (pure water with 0.04% acetic acid) and solvent B (acetonitrile with 0.04% acetic acid). Sample measurements were performed with a gradient program that employed the starting conditions of 95% of solvent A and 5% of B solvent. Within 10 min, a linear gradient to 5% A / 95% B was programmed, and 5% A / 95% B was maintained for 1 min. Subsequently, a composition of 95% A / 5.0 % B was adjusted within 0.1 min and maintained for 2.9 min. The column oven was set to 40 °C, and the injection volume was 4 μl. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS. LIT and triple quadrupole (QQQ) scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (Q TRAP), API 4500 Q TRAP LC/MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in positive and negative ion mode and controlled by Analyst 1.6.3 software (AB Sciex). The ESI source operation parameters were as follows: ion source, turbo spray; source temperature 550 °C; ion spray voltage (IS) 5500 V (positive ion mode)/-4500 V (negative ion mode); ion source gas I, gas II, and curtain gas were set at 50, 60, and 30.0 psi, respectively. The collision gas was high. Instrument tuning and mass calibration were performed with 10 and 100 μmol/L polypropylene glycol solutions in QQQ and LIT modes, respectively. QQQ scans were acquired as MRM experiments with collision gas (nitrogen) set to 5 psi. DP and CE for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period. Feature extraction and pre-processing of the raw data were conducted using XCMSt-test, with P < 0.05 as the threshold.All values are presented as mean ± SD. The significance of differences between means was determined in Excel using Student’s L. barbarum and L. ruthenicum at five developmental stages, from young fruit (≈10 days post-flowering) to mature (ripe) fruit (34–45 days post-flowering), and studied their transcriptome and metabolome.We collected fruits of L. barbarum and L. ruthenicum, with three biological replicates (three fruits from three trees) at each time point: 2 species × 5 time-points × 3 biological replicates. Samples were labelled LB/LR(1–5)-(1–3), where LB is L. barbarum and LR is L. ruthenicum, 1–5 are developmental stages of fruit ,–3,L. barAmong all 326,276 unigenes queried against public databases, a total of 193,021 (59.15%) matched genes and/or proteins in at least one database, and 12,171 (3.73%) were annotated in all databases. The largest number of unigenes  was annotated in the NT database, and the lowest number  in the KOG database.st stage, and in the 2nd vs. 5th stage comparison . Heatmap analysis of DEGs in LB shows that fairly different sets of genes were highly upregulated in the early developmental stages (1 + 2) and in later stages (3 to 5)  Fig. . Sample  5) Fig. . Particu 5) Fig. .Table 2TFor a more in-depth analysis of the data, we focused on the comparison of most significantly regulated pathways in successive developmental stages. In the first pairwise comparison  vs S2, ‘st and 2nd stage (117). These were assigned to a large number of pathways; with the largest number of metabolites assigned to ‘b. of secondary metabolites’ , followed by ‘protein digestion and absorption’ and ‘b. of amino acids’  to 129 (stages 2 vs 5). The largest number in successive stage comparisons was observed between 1ds’ Fig.  vs. S3, As regards individual metabolites, in the Heatmap analysis of DEGs in LR shows that almost completely different sets of genes were highly upregulated in the early developmental stages 1 + 2) and in ripe fruit (stage 5), with an apparent transcriptomic reset occurring after the second stage Fig. . Sample  and in rst and 2nd stage (117). These were assigned to a large number of pathways; with the largest number of metabolites assigned to ‘b of secondary metabolites’, followed by ‘protein digestion and absorption’, ‘b. of amino acids’, and ‘flavonoid b’. In the The number of differentially regulated metabolites in pairwise comparisons of developmental stages in LR varied from 66 (stages 3 vs 4) to 133 (stages 2 vs 5)  shows that 928 DEGs were shared by all five pairs Fig. . The hignd-highest in th-highest in rd-highest in nd-highest in th-highest in th-highest in rd-highest in th-highest in th-highest in th-highest in th-highest in nd-highest in th-highest in th-highest in rd to 4th-highest during stages 3 to 5). Linoleic acid metabolism was highly enriched in middle stages  was among the handful of most highly upregulated genes in the first three stages , but it was also not identified as DEG in later stages. CCR4-NOT transcription complex subunit 7/8 (CNOT7/8) also exhibited a very similar expression pattern: highly upregulated in first three stages, and not a DEG in stages 4 and 5. Several flavonoid and phenylpropanoid biosynthesis-associated genes exhibited a reversed developmental stage-specific expression pattern, with relatively low expression in early stages, and very high in later stages. Examples are: bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase (DFR), which was slightly upregulated in LR in rd-highest upregulated gene in th-highest upregulated DEG in th in flavonoid 3′,5′-hydroxylase (F3′5′H) was not a DEG in the first two stages, highly upregulated in th-highest upregulated gene in th-highest in Flavonoid O-methyltransferase (OMT) was not a DEG in the Leucoanthocyanidin dioxygenase  was not a DEG in chalcone synthase paralogues  were also not highly regulated in CHS2: not a DEG, CHS: −1.14 in CHS2: 5.32, 7.84, 6.00; and CHS: 4.67, 7.01, 6.82; respectively). We selected these genes for qPCR analysis, and the results are highly congruent with the RNA-seq data .Among the most highly differentially expressed genes, some were developmental stage-specific , but some were consistently highly differentially regulated throughout all five studied stages  in nd and 7th highest upregulated DEGs in nd and 4th highest in nd and 6th highest in Plant disease resistance protein RPM1 was also highly upregulated in all five stages , with a temporal profile of increasingly high downregulation, starting from −7.0 in the Proline iminopeptidase, associated with arginine and proline metabolism, was highly downregulated in LR in all stages: rd-highest), th-highest), rd-highest). Finally, 5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase (metE) was consistently extremely highly downregulated in LR in all stages: 2nd-highest in rd-highest in nd-highest in GTP-binding nuclear protein Ran  and replication factor A1 (RFA1) (−8.0 to −12.0). Some growth and stress-related genes were also consistently highly downregulated in LR: heterogeneous nuclear ribonucleoprotein A1/A3  and heat shock 70 kDa protein 1/8 (HSPA1_8) nd-highest), shikimate hydroxycinnamoyltransferase (HCT), was also consistently highly downregulated in LR: rd-highest), th-highest). However, a key regulator of anthocyanin biosynthesis, transcription factor MYB114, was highly upregulated in LR during all five developmental stages: 6.11, 4.69, 7.47, 9.05, and 8.95  of most highly differentially regulated metabolites between the two species, exhibited some variation among the five fruit development stages. Intriguingly, Fructose 1-phosphate was the most highly upregulated metabolite in LR, compared to LB, during all five stages: log2 Fold change = 6.3, 7.6, 7.7, 8.1, and 6.5 (stages 1 to 5 respectively). As regards the metabolites highly upregulated in LB, there was more variation among stages: in the The list  as our study . This similarity, high correlation coefficients among biological replicates, and additional confirmatory qPCR experiments, all corroborate that the transcriptome sequencing experiment and data analysis were conducted to a high standard. As gene expression (mRNA levels) can be variable over very short time-periods, RNA-seq often produces conflicting signals35, which is a likely explanation for some apparently simultaneously highly upregulated and highly downregulated pathways  in the interspecific pairwise comparisons. This relative volatility in gene expression levels is also the most likely explanation for several observed instances of incongruence between the transcriptomic and metabolomic data; such as much higher variability in the successive intraspecific pairwise comparisons observed in the transcriptomic dataset.Given the absence of a published genome for this species, almost 60% of unigenes matching against genes and/or proteins in at least one database is a satisfactory annotation rate. For example, a recent transcriptomic study of 12. Although we observed similar patterns in intraspecific pairwise comparisons of successive stages in the transcriptomic data, the total numbers of regulated DEGs were consistently smaller in LR, except for the 4th vs. 5th stage comparison, where a higher number of DEGs was identified (783). As this was not reflected in the metabolomic data, it is likely that this is an annotation artefact produced by a higher data availability for LB.A very large number of differentially regulated genes and metabolites belonging to a broad range of metabolic pathways reflects the fact that fruit ripening is a complex developmental process, characterized by a series of transitions that are coordinated by a network of interacting genes and signalling pathwaysL. barbarum. A major abiotic stressor, drought, has received ample scientific attention, as it is rather common, and affects the productivity and growth of numerous economically important plants38. Although we are far from a full understanding of the complexities of these mechanisms, some genes/metabolites/metabolic pathways have been singled out as particularly highly affected by abiotic stress, examples of which are abscisic acid (ABA), hormone signal transduction, metabolisms of proteins, carbohydrates, nucleic acids and lipids, etc.39. Salt stress can indirectly impair photosynthesis by depressing chlorophyll biosynthesis and the citrate cycle, so acetyl-CoA acyltransferase 1 (AAT1) is known to be strongly downregulated in response to salt stress40, which is in perfect agreement with very high downregulation in all stages observed in our results. A gene involved in the biosynthesis of amino acids, but also a number of important secondary metabolites in plants, metE, exhibited a temporal profile of increasingly high downregulation. Due to its varied metabolic roles there are multiple possible explanations for this, but as downregulation of this enzyme was observed in response to drought stress in other plants41, its regulatory pattern may also be related to the higher drought exposure of LR plants in our experimental setup.As LR is native to the salinized deserts of northwestern China, its genetics and physiology should bear strong markings of the evolution in an environment where drought and salt stress are very common. Indeed, it generally exhibits higher resistance to abiotic and biotic stressors common in that environment, such as high soil salinity, drought and local pests, than 43. Increased level of raffinose in post-colour breaking stages of fruit development in LR was observed recently, and associated with osmoregulation requirements3. In our study, raffinose was also increased in LR (3.6-fold), as was another raffinose family oligosaccharide, galactinol (11.7-fold). A heightened synthesis of galactinol has been reported in plants in response to a range of abiotic stressors, so it is believed to function as an osmoprotectant in drought-stress tolerance of plants44. Intriguingly, another carbohydrate highly enriched (8.09-fold) in LR, trehalose, is a disaccharide of glucose that functions as an osmoprotectant under abiotic stress in bacteria, fungi, and invertebrates, but it generally does not accumulate in detectable levels in most plants, with the exception of desiccation-tolerant “resurrection plants”46. Trehalose and galactinol also featured prominently in both species in the lists of most highly regulated metabolites in the intraspecific comparisons between different stages, suggesting a prominent role of these metabolites in both species. This is likely to be a reflection of a high level of adaptation to arid habitats in both species.In agreement with this observation are also the results of comparative interspecific individual metabolite analyses, where a number of metabolites associated with abiotic stress topped the list of most highly significantly differentially regulated metabolites between the ripe fruits of the two species . Plants counteract the deleterious effects of drought by accumulating osmolytes, such as amino acids, amines and some soluble carbohydrates , which have a vital role for the stability of cellular structures under adverse environmental conditions47. Sucrose is another highly significantly upregulated carbohydrate that  protects membranes and proteins in bacteria during drying48. A recent study found that sucrose was the most abundant sugar in both Lycium fruits before colour-breaking, but glucose and fructose were significantly elevated post colour-breaking in both fruits3. Therefore, the high levels of sucrose in ripe LR fruits observed in our study are likely to be a reflection of adaptation to drought and/or salinity stress.Among other examples of osmolytes abundant in LR are sucrose (37-fold) and betaine (28-fold). A member of the betaine family, glycine betaine, is an important organic osmolyte that protects cells against osmotic stress caused by drought or high soil salinityCitrullus lanatus)49. The authors suggested that the accumulation of citrulline during the drought stress is a unique phenomenon in C3-plants. Although reports of the association between high citrulline accumulation and drought appear to be limited to watermelon species49, citrulline accumulation was associated with higher disease resistance in citrus fruits50. Glutamate and arginine are both precursors for citrulline synthesis49, and arginine is believed to play a role in the fine-tuning of stress defense mechanisms51. Arginine is also a precursor for nitric oxide and polyamines, which are important metabolites in stress responses52. For example, high arginine accumulation in response to long-term drought stress was also observed in chickpea53. Intriguingly, we observed a strong accumulation of DL-arginine in the ripe fruit of both species, but further studies are needed to assess whether this may be related to abiotic stress adaptation in both species.Several metabolites related with amino acid metabolism- were also highly significantly more abundant in the ripe LR fruit: citrulline (31.1-fold), arginine (31.1-fold) and glutamate (29.5-fold). A massive accumulation of these three metabolites  in response to a drought stress was observed in wild watermelon (Proline iminopeptidase (pip), associated with arginine and proline metabolism, was highly downregulated in LR in all stages. As proline (and pip) concentrations tend to increase under stress in a broad range of living organisms55, we would expect the pip gene to be upregulated. It should be noted that we did observe a high accumulation of L-proline in the ripe LR fruit. However, a downregulation of pip in response to drought stress was also observed in Bombax ceiba36, and proline accumulation was triggered in Calotropis procera in response to salt stress, but not drought stress56, which indicates that this discrepancy may not be mere molecular noise, and might deserve further investigation.Expression patterns of some genes were (apparently) not in agreement with the hypothesis of increased stress-related gene expression in LR. 9. Flavonoids have high antioxidant potential and possess a number of properties putatively beneficial from the pharmacological perspective: antitumorigenic, anti-inflammatory, prevention and treatment of cardiovascular and neurodegenerative diseases, obesity, dental health, etc.60. Phenylpropanoids, a group of phenylalanine-derived physiologically active secondary metabolites, are the key mediators of plant responses towards abiotic  and biotic (pests) stimuli, also with important  antioxidant and free radical scavenging properties61. Many functional pathways, genes and metabolites associated with phenylpropanoid and flavonoid biosynthesis exhibited upregulation in LR berries, especially in later developmental stages. Developmental stage-specific expression pattern, with relatively low expression in early stages, and very high in later stages was exhibited by the paralogues of DFR and CHS, as well as F3′5′H, OMT and LDOX genes, all of which take part in the biosynthesis of anthocyanin, natural pigment of plants, responsible for red, blue and purple colours63. These genes are commonly upregulated in later stages of fruit ripening62; for example, high expression of DFR increases the accumulation of anthocyanin content during fruit ripening64. Importantly, a key positive regulator of the anthocyanin biosynthesis, transcription factor MYB11465, was highly upregulated in LR during all five developmental stages. At the metabolomic level, a metabolite associated with flavone and flavonol biosynthesis pathway, rutin, was significantly more abundant in ripe LR fruits than in LB fruits (5.88-fold). Increased rutin content was observed in plants in response to a drought stress66, salinity stress67, and also to biotic stress (pests)68. Intriguingly, this metabolite was not mentioned in a recent comparative metabolomic study of these two Lycium fruits3. As rutin derivatives have antioxidant potential and show low cytotoxicity in human and animal cells, which makes them promising potential candidates for use as nutraceuticals69, and as there are indications that rutin may suppress lipid accumulation in humans70, high rutin content in LR berries may be interesting from the nutritional and pharmaceutical perspective.LB and LR fruits are generally relatively rich in pharmacologically important secondary metabolites synthesized via the phenylpropanoid/flavonoid pathway, such as anthocyanin, betalain, flavone, flavonoid, isoquinoline, etc.L. ruthenicum may exhibit higher resistance to abiotic  than L. barbarum11 and that berries of L. ruthenicum may have much greater medicinal value than berries of L. barbarum3. Although we can tentatively conclude only that our results are in agreement with these indications, it will be necessary to corroborate our comparative analyses results in future studies with different experimental setups before any conclusions about genetic and metabolic adaptations of these two species to environmental stress can be made with confidence. Regardless of this, our analyses enabled us to identify a number of genes  and metabolites  that may be of interest to future functional studies of stress adaptation in plants. In the light of the rapid growth in global popularity of “health food” and “organic food” products71, we expect that both of these species shall continue to receive increasing scientific attention. Additionally, L. ruthenicum has high suitability for combating soil desertification and for alleviating soil salinity/alkalinity9, which is a major problem both in China and globally73, and there are indications that it may also have a very high capacity for removal of petroleum from contaminated soil74. Therefore, this indicates that L. ruthenicum may have much higher potential for human use, than its current, highly localized, relevance appears to imply.The interpretation of our findings is hampered by different environmental parameters at the two sampled locations, as well as by the fact that LB has undergone generations of anthropogenic selection for higher growth, whereas the genome of LR is likely to be shaped solely by non-anthropogenic factors. As it was difficult to disentangle genetic from environmental variables, and anthropogenic from non-anthropogenic variables in our study, we limited the discussion of our results to the most highly pronounced transcriptomic and metabolomic differences between the two species. Previous studies found indications that Supplementary Figures.Supplementary Results.Supplementary Dataset S1.Supplementary Dataset S2.Supplementary Dataset S3.Supplementary Dataset S4.Supplementary Dataset S5.Supplementary Dataset S6.


We investigated the ability of the ascorbic acid (AA) and menadione (MD) combination, the well-known reactive oxidative species- (ROS-) generating system, to induce autophagy in human U251 glioblastoma cells. A combination of AA and MD (AA+MD), in contrast to single treatments, induced necrosis-like cell death mediated by mitochondrial membrane depolarization and extremely high oxidative stress. AA+MD, and to a lesser extent MD alone, prompted the appearance of autophagy markers such as autophagic vacuoles, autophagosome-associated LC3-II protein, degradation of p62, and increased expression of beclin-1. While both MD and AA+MD increased phosphorylation of AMP-activated protein kinase (AMPK), the well-known autophagy promotor, only the combined treatment affected its downstream targets, mechanistic target of rapamycin complex 1 (mTORC1), Unc 51-like kinase 1 (ULK1), and increased the expression of several autophagy-related genes. Antioxidant N-acetyl cysteine reduced both MD- and AA+MD-induced autophagy, as well as changes in AMPK/mTORC1/ULK1 activity and cell death triggered by the drug combination. Pharmacological and genetic autophagy silencing abolished the toxicity of AA+MD, while autophagy upregulation enhanced the toxicity of both AA+MD and MD. Therefore, by upregulating oxidative stress, inhibiting mTORC1, and activating ULK1, AA converts MD-induced AMPK-dependent autophagy from nontoxic to cytotoxic. These results suggest that AA+MD or MD treatment in combination with autophagy inducers could be further investigated as a novel approach for glioblastoma therapy. Glioblastoma multiforme (GBM) represents the most prevalent and aggressive brain tumor in the adult population . The staMenadione  is a lipid-soluble synthetic analog of vitamin K, metabolized by the human body into menaquinone (vitamin K2). Numerous studies showed potent anticancer activity of MD in various cancer cell lines, with both antiproliferative and direct cytotoxic action resulting in caspase-dependent , caspaseMacroautophagy (hereafter autophagy) represents a lysosome-dependent cellular degradation program for the removal of superfluous and dysfunctional organelles and proteins. Cytoplasmic components and organelles are sequestered within autophagosomes that subsequently fuse with lysosomes into autolysosomes, where the engulfed content is degraded and recycled. During the process of sequestration, a lipidated form of microtubule-associated protein 1 light chain 3 protein (LC3-II) is formed and recruited to the autophagosomal membrane. Therefore, the amount of LC3-II protein in the cell serves as an early autophagy marker. Molecules and cellular components selectively targeted for autophagic degradation are labeled by p62  and delivered to autophagosomes through an LC3-interacting region of p62. Subsequent degradation of this cargo receptor and specific autophagy substrate in the autolysosomes represents another important determinant of the ongoing autophagic process. The autophagy pathway is tightly controlled by the serine/threonine kinase activity of the main autophagy repressor, mechanistic target of rapamycin complex 1 (mTORC1). mTORC1 inhibits autophagy during nutrient-rich conditions by suppressing the activity of the autophagosome biogenesis-initiating complex consisting of Unc-51-like autophagy-activating kinase 1 (ULK1), autophagy-related 13 (Atg13), and focal adhesion kinase family interacting protein of 200 kD . Their aThe ability of both MD –32 and cThe present study demonstrates, for the first time, that the treatment of U251 human glioblastoma cells with AA increased MD-mediated AMPK-dependent autophagy through oxidative stress-dependent mTORC1 inhibition and subsequent ULK1 activation. Moreover, using both pharmacological and genetic approaches, we show that, in contrast to mild nontoxic autophagy triggered by MD, its increase by AA cotreatment contributed to the necrotic demise of glioblastoma cells.4Cl), bafilomycin A1 (BAF), trehalose (TREH), and N-acetylcysteine (NAC) are all purchased from Sigma-Aldrich .Ascorbic acid, menadione, Z-VAD, Q-VAD, staurosporine (STS), 3-methyladenine (3MA), ammonium chloride (NHThe absorbance of colored substrates (CV and MTT) was measured in an automated microplate reader . Flow cytometric analyses of apoptosis/necrosis, caspase activation, mitochondrial membrane potential, total intracellular production of ROS, mitochondrial superoxide, and intracellular acidic vesicles were conducted using FACSCalibur flow cytometer equipped with CellQuest Pro software . SDS-PAGE electrophoresis was performed on Hoefer's  standard vertical protein electrophoresis unit, while proteins were transferred to nitrocellulose membranes using Bio-Rad's  transfer unit. RT-qPCR analysis was performed on a Realplex2 Mastercycler . Light microscopy was conducted using an universal inverted microscope  equipped with Leica Microsystems DFC320 camera, while ultrastructural morphology of cells was analysed by Morgagni 268D electron microscope  using a MegaView III CCD camera equipped with iTEM software .2, in the RPMI cell culture medium  supplemented with 5% fetal calf serum (FCS), 2 mM L-glutamine, 10 mM sodium pyruvate, and penicillin/streptomycin . Cells were prepared for experiments using the conventional trypsinization procedure with trypsin/EDTA and seeded in 96-well flat-bottom plates (1 × 104 cells/well) for the 5 cells/well) for flow cytometric analysis, or 60 mm Petri dishes (1 × 106 cells) for the electron microscopy, immunoblot, and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR).The human glioblastoma cell line U251 was obtained from the European Collection of Animal Cell Cultures . The cells were incubated at 37°C, in a humidified atmosphere with 5% CO4Cl, and BAF, autophagy inducer TREH, or antioxidant NAC as described in figure legends.The cells were rested in a cell culture medium for 24 h and then treated with AA, MD, and their combination, in the presence or absence of caspase inhibitors (Z-VAD and Q-VAD), apoptosis inducer STS, autophagy inhibitors 3MA, NHThe number of adherent cells and mitochondrial dehydrogenase activity, as the indicators of cell viability, was assessed by crystal violet (CV) and 3--2,5-diphenyl-tetrazolium bromide (MTT) reduction assay, respectively.For the CV test, cell cultures were washed with phosphate buffer saline (PBS) to remove nonadherent dead cells. The adherent, viable cells were fixed with methanol and stained with 10% CV solution  at room temperature for 15 minutes. After rigorous washing with water, CV was dissolved in 33% acetic acid. The absorbance of dissolved dye, corresponding to the number of adherent (viable) cells, was measured in an automated microplate reader at 570 nm .For the MTT assay, cells were washed with PBS to avoid nonspecific reaction of MTT with AA, MTT  solution was added at a final concentration of 0.5 mg/ml, and cultures were incubated for an additional hour. The solution was then removed, and cells were lysed with DMSO . The absorbance of the purple-colored formazan, which is in direct proportion to the number of metabolically active cells, was monitored by the automated microplate reader at 570 nm . The results of both assays were presented as % viability relative to untreated control cultures, taken as 100% viable.https://www.combosyn.com).To describe the type of interaction between ascorbate and menadione, cells were treated with each agent alone and their appropriate combinations. The number of viable cells was determined by the CV assay. The values of the combination index (CI) reflecting synergistic (CI < 1), additive (CI = 1), or antagonistic interactions (CI > 1) were calculated according to the method designed by Chou and Talalay  using Coμg/ml) and PI (20 μg/ml), and incubated in the dark for 15 minutes at 37°C. The green (FL1) and red (FL2) fluorescence was measured using FACSCalibur flow cytometer equipped with CellQuest Pro software  to evaluate the number of viable (annexin−/PI−), early apoptotic (annexin+/PI−), and late apoptotic/necrotic cells (annexin+/PI+).Apoptotic and necrotic cell death was analyzed by double staining with fluorescein isothiocyanate- (FITC-) conjugated annexin V  and propidium iodide . Annexin V-FITC binds to phosphatidylserine exposed on the external surface of the cell in the early stage of apoptosis, while PI is a cell impermeable DNA-intercalating dye that labels cells with the damaged cell membrane. Cells were detached by trypsinization, stained with annexin V-FITC (2 μg/ml) for 30 minutes at 37°C. The increase in green fluorescence (FL1) as a measure of caspase activity was determined using FACSCalibur flow cytometer equipped with Cell Quest Pro software. The results were expressed as the % of cells containing active caspases.Activation of caspases, the cysteine proteases involved in the execution of apoptotic cell death, was measured by flow cytometry after staining the cells with a cell-permeable, FITC-conjugated pancaspase inhibitor ApoStat . Cells were detached by trypsinization and stained with ApoStat (0.5 μg/ml in a JC-1 staining buffer) for 20 minutes at 37°C. The green monomers and red aggregates were detected by FACSCalibur flow cytometer and analyzed using CellQuest Pro software. The results were presented as the fold change in red/green fluorescence ratio , with the decrease in FL2/FL1 reflecting mitochondrial depolarization.Mitochondrial membrane potential was assessed using JC-1 . This lipophilic cation, capable of entering selectively into mitochondria, has the property of altering aggregation status depending on mitochondrial membrane potential—it changes reversibly from green monomeric form to orange/red aggregates as the membrane potential increases. Cells were detached by trypsinization and stained with JC-1 (5 μM) was added to the cell cultures at the beginning of treatment, while MitoSOX Red (5 μM) was added during the last 10 minutes of treatment. At the end of incubation, the cells were detached by trypsinization and washed in PBS. The mean intensity of green (FL1) or red (FL2) fluorescence, corresponding to total ROS production or mitochondrial superoxide generation, respectively, was determined using a FACSCalibur flow cytometer and CellQuest Pro software. The results were presented as the fold change relative to the FL1 (DHR) or FL2 (MitoSOX Red) value of the untreated cells, which was arbitrarily set to 1.The total intracellular production of ROS and mitochondrial superoxide generation were determined using dihydrorhodamine 123 (DHR) and MitoSOX Red , respectively, as previously described . DHR is μM) for 1 hour at 37°C, washed in PBS, trypsinized, and analyzed using a FACSCalibur flow cytometer and CellQuest Pro software. The results were presented as a fold increase in the mean fluorescence intensity relative to the FL2 value obtained in untreated cells, which was arbitrarily set to 1.The intracellular acidic vesicles  were stained with LysoTracker Red DND-99  and analyzed by flow cytometry. LTR consists of a red fluorophore DND-99 linked to a weak base that is only partially protonated at neutral pH, allowing LTR to freely permeate cell membranes and selectively accumulate in acidic vesicles due to their low pH. The cells were incubated with LTR . Rabbit anti-human antibodies against microtubule-associated protein 1 light-chain 3B (LC3B), mTOR, phospho-mTOR (Ser2448), AMPKα, phospho-AMPKα (Thr-172), p70S6 kinase (S6K), phospho-S6K (Thr389), ULK1, phospho-ULK1 (Ser317), beclin-1, cleaved caspase 3, poly (ADP-ribose) polymerase-1 (PARP1), and β-actin , mouse anti-human antibodies against p62/sequestosome 1 , and TFEB  were used as primary antibodies, whereas peroxidase-conjugated goat anti-rabbit IgG  and goat anti-mouse IgG  were used as secondary antibodies. The specific protein bands were visualized by chemiluminescence using the Amersham reagent  on a ChemiDoc Imaging System . The signal intensity was quantified by densitometry using ImageJ software , and the ratio between phosphorylated and corresponding total protein signals or actin  was calculated. The results were presented relative to the signal intensity of the untreated control, which was arbitrarily set to 1.Immunoblotting was used to assess the expression/activation of specific proteins. At the end of treatment, the dead cells were removed with cold PBS and the remaining adherent cells were lysed in lysis buffer  on ice for 30 minutes. The cell lysates were centrifuged at 14000μg of RNA was used in the reverse transcription reaction using M-MuLV reverse transcriptase and random hexamer primers  according to the manufacturer's instructions. RT-qPCR analysis was performed in a Realplex2 Mastercycler  using 96-well reaction plates , Maxima Hot start PCR Master Mix , and the TaqMan primers/probes for human ATG3 (Hs00223937_m1), ATG13 (Hs00207186_m1), forkhead box O1 , forkhead box O3F , UV radiation resistance-associated gene , Bax-interacting factor 1/endophilin-B1 , autophagy/beclin-1 regulator 1 , γ-aminobutyric acid receptor-associated protein , phosphatidylinositol 3-kinase catalytic subunit type 3 , beclin-1 (Hs00186838_m1), p62/sequestosome 1 (Hs00177654_m1), and 18S ribosomal RNA  . The thermal cycle conditions were 95°C for 4 min, followed by 40 cycles of 15 s at 95°C and 1 min at 60°C. All reactions were performed in triplicates. The average cycle of threshold (Ct) values of 18 s RNA as housekeeping gene was subtracted from the Ct values of target genes to obtain ΔCt, and relative gene expression was determined as 2−ΔCt. The results were presented relative to the control value, which was arbitrarily set to 1.RT-qPCR was used to determine the expression of ATG genes. Total RNA was extracted from cells with TRIzol Reagent , and 1 For the analysis of ultrastructural morphology by TEM, cells were trypsinized, fixed in 3% glutaraldehyde in cacodylate buffer, postfixed in 1% osmium tetroxide, dehydrated in graded alcohols, and then embedded in epoxy medium . The ultrathin sections were stained in uranyl acetate and lead citrate and examined using a Morgagni 268D electron microscope . The images were acquired using a MegaView III CCD camera equipped with iTEM software .μl) and siRNA oligomers (100 nM), as well as Lipofectamine 2000 (2.8 μg/ml), were dissolved and incubated for 5 minutes in Opti-MEM medium without FCS and antibiotics. The equal amounts of medium with siRNA or plasmid and medium with Lipofectamine 2000 were mixed and incubated for 20 minutes, allowing the formation of RNA-liposome or plasmid-liposome complexes. Afterwards, the cells were incubated with siRNA/Lipofectamine 2000 or plasmid/Lipofectamine 2000 mixture for 8 h. Cells were rested for 24 h following transfection and then treated with AA and MD.Small interfering RNA (siRNA) targeting human LC3 and beclin-1, as well as scrambled control siRNA, were obtained from Santa Cruz Biotechnology . The empty control (pcDNA3) and transcription factor EB- (TFEB-) encoding plasmid vectors were kindly donated by Dr Viktor I. Korolchuk . The transfection of U251 cells with plasmids and siRNA was performed in 96-well microplates for cell viability assays, or in 6-well plates for flow cytometry, using Lipofectamine 2000 . The plasmids  for 24 h  only slightly decreased the number of adherent viable cells and mitochondrial dehydrogenase activity measured by CV  and/or MD , RT-qPCR analysis demonstrated the ability of combined treatment to stimulate the expression of autophagy transcription factors FOXO1 and FOXO3, as well as proautophagic regulators ATG13, BIF, and UVRAG  and a decrease in levels of major oncometabolite succinate [2O2, 2-methoxyestradiol, and mitochondrial electron transport chain inhibitors) caused autophagy-mediated cell death in U87 GBM cells, but failed to significantly affect redox homeostasis and induce autophagy in primary mouse astrocytes [Numerous studies show that AA+MD combination selectively targets various cancer cells, including GBM , 71, oveuccinate , 40 appeuccinate , due to uccinate , thus enuccinate  and subsuccinate . The lowuccinate  and glutuccinate  could couccinate  or diffeuccinate , 78. Theuccinate  demonstrtrocytes , 78. OveIn conclusion, the present study demonstrates that ROS-dependent modulation of mTORC1 and ULK1 activity triggers an excessive autophagic response that contributes to the synergistic induction of necrotic death in U251 cells exposed to a combination of AA and MD . While t


Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines, we conjecture that the success of deep neural networks owes much to three characteristics, i.e. layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand, our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation. Deep learning  has becoThough deep neural networks are powerful, they have many deficiencies. First, DNNs have too many hyper-parameters, and the learning performance depends seriously on careful parameter tuning. Indeed, even when several authors all use convolutional neural networks , they arIn order to tackle complicated learning tasks, learning models are likely to have to go deep. Current deep models, however, are always built upon neural networks. As discussed above, there are good reasons to explore non-NN-style deep models or, in other words, to consider whether deep learning can be realized with other modules, as they have their own advantages and may exhibit great potential if they are able to go deep. In particular, considering that neural networks are multiple layers of parameterized differentiable non-linear modules, whereas not all properties in the world are differentiable or best modeled as differentiable, in this paper we attempt to address the question of whether deep learning can be realized with non-differentiable modules.The answer to the question may help understand important issues such as (1) do deep models have to be DNNs, or must deep models be constructed with differentiable modules? Note that there is research involving non-differentiable activation functions in DNNs; however, it usually uses differentiable functions that give an upper bound to the non-differentiable ones for relaxation in the optimization/learning process, and thus they actually still work with differentiable modules. (2) Is it possible to train deep models without backpropagation? Note that backpropagation and gradient-based adjustment require differentiability. (3) Is it possible to enable deep models to win tasks on which other models such as random forest or XGBoost are now superior? Actually, the machine-learning community has developed lots of learning modules, but many of them are non-differentiable and can hardly be tuned by gradient-based adjustment; it would be interesting to know whether it is possible to construct deep models based on these modules.In this paper, we extend our preliminary research , which pThe section entitled ‘Ensemble learning and diversity' briefly introduces ensemble learning and ‘What is crucial for deep models?' explains our design motivations by analyzing why deep learning works. The section entitled ‘The gcForest approach' proposes our approach, followed by experiments reported in the section entitled ‘Experiments'. The section entitled ‘Real application' briefly presents a real application and the following section discusses some related work. The section entitled ‘Future issues' raises some issues for future exploration, followed by a section with some concluding remarks.Ensemble learning  is a macE denotes the error of an ensemble, To construct a good ensemble, the individual learners should be accurate and diverse. Combining only accurate learners is often inferior to combining some accurate learners with some relatively weaker ones, because the complementarity is more important than pure accuracy. Actually, a beautiful equation has been theoretically derived from error-ambiguity decomposition :(1)\dociversity , and ‘whIn practice, diversity enhancement is based on randomness injection during training. Roughly speaking, there are four major categories of strategies . The firIt is widely recognized that the representation learning ability is crucial for the success of deep neural networks. What is crucial for representation learning in DNNs? We believe that the answer is layer-by-layer processing. Figure Considering that, if other issues are fixed, large model complexity  generally leads to strong learning ability, maybe it sounds reasonable to attribute the successes of DNNs to the huge model complexity. This, however, cannot explain the fact that shallow networks are not as successful as deep ones, as one can increase the complexity of shallow networks to almost arbitrarily high by adding a nearly infinite number of hidden units. Consequently, we believe that the model complexity itself cannot explain the success of DNNs. Instead, we conjecture that the layer-by-layer processing is one of the most important factors behind DNNs because shallow networks, no matter how large their complexity can be, cannot do layer-by-layer processing. This conjecture offers important inspiration for the design of gcForest.Then, how do we explain the fact that traditional learning models that can do layer-by-layer processing, e.g. decision trees and boosting machines, are not as successful as DNNs? We believe that the distinguishing factor lies in the fact that, in contrast to DNNs where new features are generated as illustrated in Fig. Overall, we conjecture that behind the mystery of DNNs there are three crucial characteristics, i.e. layer-by-layer processing, in-model feature transformation, and sufficient model complexity. To verify our conjecture, in the next section we will try to endow non-NN-style deep models with these characteristics.To realize layer-by-layer processing, gcForest employs a cascade structure, as illustrated in Fig. d is the number of input features) and selecting the one with the best Gini value for splitting. The number of trees in each forest is a hyper-parameter.Each level is an ensemble of decision-tree forests, i.e. an ensemble of ensembles. Here, we include different types of forests to encourage diversity. For simplicity, suppose that we use two completely random forests and two random forests . Each coGiven an instance, each forest can produce an estimate of class distribution, by counting the percentage of different classes of training examples at the leaf node where the concerned instance falls, and then averaging across all trees in the same forest, as illustrated in Fig. The estimated class distribution forms a class vector, which is then concatenated with the original feature vector to input to the next level. For example, suppose there are three classes; each of the four forests will produce a 3D class vector; consequently, the next level will receive 12 (= 3 × 4) augmented features.Note that here we take the simplest form of class vectors, i.e. the class distribution at the leaf nodes into which the concerned instance falls. It is evident that such a small number of augmented features may deliver very limited augmented information, and it is very likely to be drowned out when the original feature vectors are high-dimensional. We will show in experiments that such a simple feature augmentation is already beneficial, and it is expected that more profit can be obtained if more augmented features are involved. Actually, it is apparent that more features may be incorporated, such as class distribution of the parent nodes, which express prior distribution, the sibling nodes, which express complementary distribution, etc. We leave these possibilities for future exploration.k-fold cross validation. In detail, each instance will be used as training data k − 1 times, resulting in k − 1 class vectors, which are then averaged to produce the final class vector as augmented features for the next level of the cascade. After expanding a new level, the performance of the whole cascade can be estimated on the validation set, and the training procedure will terminate if there is no significant performance gain; thus, the number of cascade levels can be automatically determined. Note that the training error rather than cross-validation error can also be used to control the cascade growth when the training cost is concerned or limited computation resources are available. In contrast to most deep neural networks whose model complexity is fixed, gcForest adaptively decides its model complexity by terminating training when adequate. This enables it to be applicable to different scales of training data, not limited to large-scale ones.To reduce the risk of overfitting, the class vector produced by each forest is generated by Deep neural networks are powerful in handling feature relationships, e.g. convolutional neural networks are effective on image data where spatial relationships among the raw pixels are critical; recurrent neural networks are effective on sequence data where sequential relationships are critical. Inspired by this recognition, we enhance the cascade forest with a procedure of multi-grained scanning.As Fig. For instances extracted from the windows, we simply assign them with the label of the original training example. Some label assignments are inherently incorrect. For example, suppose that the original training example is a positive image about ‘car'; it is clear that many extracted instances do not contain a car, and therefore they are incorrectly labeled as positive. This is actually related to flipping output , an apprNote that when transformed feature vectors are too long to be accommodated, feature sampling can be performed, e.g. by subsampling the instances generated by sliding window scanning, as completely random trees do not rely on feature split selection whereas random forests are quite insensitive to feature split selection. The feature sampling process is also related to random subspace , an apprFigure m training examples, a window with a size of 100 features will generate a data set of 301 × m 100D training examples. These data will be used to train a completely random forest and a random forest, each containing 500 trees. If there are three classes to be predicted, a 1806D feature vector will be obtained as described in the section entitled ‘Cascade forest structure'. The transformed training set will then be used to train the first grade of the cascade forest.Figure A to Level 1C in Fig. Similarly, sliding windows with sizes of 200 and 300 features will generate 1206D and 606D feature vectors, respectively, for each original training example. The transformed feature vectors, augmented with the class vector generated by the previous grade, will then be used to train the second and third grades of the cascade forests, respectively. This procedure will be repeated till convergence of validation performance. In other words, the final model is actually a cascade of cascades, where each cascade consists of multiple levels each corresponding to a grain of scanning, e.g. the first cascade consists of Level 1Given a test instance, the multi-grained scanning procedure will be gone through to get the corresponding transformed feature representation, and then the cascade will be gone through till the last level. The final prediction will be obtained by aggregating the four 3D class vectors at the last level, and the class with the maximum aggregated value will be output.Table We compare gcForest with deep neural networks and several other popular learning algorithms. The implementations are based on Python, with neural networks from Keras and traditional learning algorithms from Sklearn.d raw features, we use feature windows with sizes of ⌊d/16⌋, ⌊d/8⌋, ⌊d/4⌋; if the raw features have panel structure (such as images), the feature windows also have panel structure, as shown in Fig. In all experiments gcForest is using the same cascade structure: Each level consists of four completely random forests and four random forests, each containing 500 trees, as described in the section entitled ‘Cascade forest structure'. Three-fold cross validation is used for class vector generation. The number of cascade levels is automatically determined. In detail, we split the training set into two parts, i.e. a growing set and an estimating set.  Then we use the growing set to grow the cascade, and the estimating set to estimate the performance. If growing a new level does not improve the performance, the growth of the cascade terminates and the estimated number of levels is obtained. Then, the cascade is retrained based on merging the growing and estimating sets. For all experiments we take 80% of the training data for the growing set and 20% for the estimating set. For multi-grained scanning, three window sizes are used. For For deep neural network configurations, we use ReLU for the activation function, cross-entropy for the loss function, Adadelta for optimization, and a dropout rate of 0.25 or 0.5 for hidden layers according to the scale of the training data. The network structure hyper-parameters, however, cannot be fixed across tasks, otherwise the performance will be embarrassingly unsatisfactory. For example, a network attained 80% accuracy on the ADULT dataset but achieved only 30% accuracy on YEAST with the same architecture (only the numbers of input/output nodes were changed to suit the data). Therefore, for DNNs, we examine a variety of architectures on the validation set and pick the one with the best performance, then re-train the network on the training set and report the test accuracy.We run experiments on a broad range of tasks, with data types of image, audio, time series, text, etc.The MNIST dataset  containskNN (k = 3) test accuracies are 76.00%/83.33%/92.50%, SVM (rbf kernel) 80.50%/82.50%/85.00%, and random forest 91.00%/93.33%/95.00%, whereas gcForest attains 91.00%/96.67%/97.50% by simply using the default settings.The ORL dataset  containsThe GTZAN dataset  containsThe sEMG dataset  consistsThe IMDB dataset  containsWe also evaluate gcForest on UCI datasets  with a rThe CIFAR-10 dataset  containsAs we discussed in the section entitled ‘The gcForest approach', currently we include only a 10-dim augmented feature vector from each forest, and such a small number of augmented features will be easily drowned out by the original long feature vector. Nevertheless, though the test accuracy of gcForest with the default setting, 61.78%, is inferior to state-of-the-art DNNs, it is already the best among non-DNN approaches. Moreover, the performance of gcForest can be further improved via task-specific tuning, e.g. by including more grains (i.e. using more sliding window sizes in multi-grained scanning) like gcForest(5grains), which uses five grains and attains 63.37%. It is also interesting to see that the performance undergoes a significant improvement to 69.00% with gcForest(gbdt), which simply replaces the final level with GBDT . The secOur experiments use a PC with two Intel E5 2695 v4 CPUs (18 cores), and the running efficiency of gcForest is good. For example, for the IMDB dataset (25 000 examples with 5000 features), it takes 267.1 seconds per cascade level, and automatically terminates with nine cascade levels, amounting to 2404 seconds or 40 minutes. In contrast, an MLP compared on the same dataset requires 50 epochs for convergence and 93 seconds per epoch, amounting to 4650 seconds or 77.5 minutes for training; 14 seconds per epoch (with a batch size of 32) if using a GPU , amounting to 700 seconds or 11.6 minutes. Multi-grained scanning will increase the cost of gcForest; however, the different grains of scanning are inherently parallel. Also, both completely random forest and random forest are parallel ensemble methods . Thus, tFigure To study the separate contribution of the cascade forest structure and multi-grained scanning, we compare gcForest with a cascade forest on the MNIST, GTZAN and sEMG datasets. The test accuracies with/without multi-grained scanning are 99.26%/65.67%/71.30% and 98.02%/52.33%/48.15% on MNIST/GTZAN/sEMG, respectively. It is evident that, when there are spatial or sequential feature relationships, multi-grained scanning helps improve performance.To study the contribution of completely random forest, we compare gcForest with its variant, which replaces completely random forests by random forests. The test accuracies with/without completely random forests are 99.26%/89.16%/97.40% and 99.11%/87.62%/96.65% on MNIST/IMDB/LETTER, respectively. It shows that completely random forest helps no matter whether multi-grained scanning is applied (MNIST) or not (IMDB), or whether data are low-dimensional (LETTER).The final model structure of gcForest is a cascade of cascades, where each cascade consists of multiple levels each corresponding to a grain of scanning, as shown in Fig. Table Figure Note that computational facilities are crucial for enabling the training of larger models; e.g. GPUs for DNNs. On one hand, some new computational devices, such as Intel KNL of the MIC (many integrated core) architecture, may offer acceleration for gcForest. On the other hand, some components of gcForest, e.g. multi-grained scanning, may be accelerated by GPUs. Moreover, there is plenty of room for improvement with distributed computing implementations.gcForest has been implemented in an industrial distributed-machine-learning platform and applied to real-world illegal cash-out fraud detection by a big unicorn enterprise . On a daThe gcForest is a decision-tree ensemble approach. There are studies showing that by using ensembles such as random forest facilitated with DNN features, the performance can be even better than simply using DNNs . Our purRandom forest , which hMany works have tried to connect random forest with neural networks, such as converting cascaded random forest to CNNs  and explThe multi-grained scanning procedure of gcForest uses different sizes of sliding windows to examine the data; this is somewhat related to wavelet and other multi-resolution examination procedures . For eacThe cascade procedure of gcForest is related to boosting , which iPassing outputs of one level of learners as inputs to another level of learners is related to stacking ,44. HoweAs a tree-based approach, gcForest might be potentially more useful for theoretical analysis than deep neural networks, although this is beyond the scope of this paper. Indeed, some recent theoretical studies about deep learning, e.g. , seem moOne important future issue is to enhance the feature re-representation process. The current implementation of gcForest takes the simplest form of class vectors, i.e. the class distribution at the leaf nodes into which the concerned instance falls. Such a small number of augmented features will be easily drowned out when the original feature vectors are high-dimensional. It is apparent that more features may be involved. Intuitively, more features may enable the incorporation of more information, although this is not always necessarily helpful for generalization. Moreover, a longer class vector may enable a joint multi-grained scanning process, leading to more flexibility of re-representation. Recently we have shown that a decision forest can serve as AutoEncoder . On one Another important future issue is to accelerate and reduce the memory consumption. Building larger models may lead to better performance, where computational facilities are crucial for enabling the training of larger models. The success of DNNs owes much to the acceleration offered by GPUs, but forest structure is unfortunately not suitable for GPUs. One possibility is to consider some new computational devices such as KNL; another is distributed computing implementation. Feature sampling can be executed when transformed feature vectors produced by multi-grained scanning are too long to be accommodated; this not only helps storage reduction, but also offers another channel to enhance the diversity. It is also possible to explore smarter sampling strategies such as BLB  or featuThe adoption of completely random forest not only helps diversity enhancement, but also provides an opportunity to exploit unlabeled data. Note that the growth of completely random trees does not require labels; label information is only needed for annotating leaf nodes. Intuitively, for each leaf node it might be possible to require only one labeled example if the node is to be annotated according to the majority cluster on the node, or one labeled example per cluster if all clusters in the node are non-negligible. This offers gcForest the opportunity of incorporating active learning  and/or sThe gcForest is able to achieve a performance highly competitive with DNNs on a broad range of tasks except some large-scale image tasks. Indeed, DNNs are very successful in image tasks, e.g. ,54. On oThis paper attempts to address the question of whether deep learning can be realized with non-differentiable modules. We conjecture that behind the mystery of deep neural networks there are three crucial characteristics, i.e. layer-by-layer processing, in-model feature transformation, and sufficient model complexity. To verify the conjecture, we try to endow a non-NN-style deep model with these characteristics, and our results show that it really works.The proposed gcForest approach  is ableThere are other possibilities for constructing deep forest. As a seminal study, we have only explored a little in this direction. Indeed, the most important value of this paper lies in the fact that it may open the door for non-NN-style deep learning, or deep models based on non-differentiable modules that do not rely on gradients.


Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital heart disease, characterized by the coronary artery inappropriately originates from the aorta. It is usually classified according to the sinus where the coronary artery arises from, while anomalous origin of the right coronary being the most common type.In this case report, we described a rare case of Tetralogy of Fallot (TOF) in a 1-year-old boy, who also had the anomalous right coronary artery that originated from the left coronary sinus without an intramural segment. Besides TOF repair, lateral pulmonary translocation was undertaken in order to avoid risks of myocardial ischemia.We successfully completed a one-stage operation consisting of TOF repair and pulmonary artery translocation in a 1-year-old boy. We advocated early operation of pulmonic translocation for AAOCA patients without an intramural segment instead of unroofing procedure. Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital heart condition, in which the origin of a coronary artery that arises from the aorta is abnormal, and the coronary artery usually has an intramural segment . The preAll types of AAOCA are associated with increased risks of myocardial ischemia and SAD, especially in young athletes . StudiesA 1-year-old boy presented to our center from a regional hospital for further assessment and management of TOF. The child was found to have structural abnormalities of the heart in his fetal stage. He presented with mild cyanosis, slight shortness of breath after activity, and poor feeding. Physical examination revealed a normal growth level at the height of 78 cm and the weight of 8.5 kg. On further investigation, the transoesophageal echocardiography (TEE) demonstrated the common features of TOF consisting of a 14-mm ventricular septal defect (VSD), pulmonary valve stenosis with a 5-mm valve ring, supravalvular pulmonary stenosis with a 4-mm local inner diameter, subvalvular pulmonary stenosis with a 5-mm infundibular pulmonary artery, 40% overriding of the aorta above VSD, and 6-mm-thickness right ventricular anterior wall. The Color Doppler assessment demonstrated a shunt flow mainly from the left ventricle to the right at the level of VSD, pulmonary valve stenosis and supravalvular pulmonary stenosis with a pressure gradient of 106 mmHg, infundibular pulmonary artery stenosis with a pressure gradient of 78 mmHg, and mild mitral and tricuspid regurgitation. Further assessment with computed tomography (CT) not only confirmed the diagnosis of TOF but also revealed both anomalous RCA and the normal LCA arose from the left coronary sinus, which coursed between the aorta and the pulmonary artery without an intramural segment Fig. .Fig. 1CoGiven the clinical features and radiological findings, a one-stage operation was carried out, which consisted of TOF repair  and lateral pulmonary translocation that aimed to improve coronary flow. A median sternotomy was performed, and cardiopulmonary bypass was initiated through aortic and bi-caval cannulation. Antegrade cardioplegia was administrated to induce cardiac arrest. TOF repair was first performed, followed by pulmonary artery translocation by introducing an autologous blood vessel patch of the right pulmonary artery and inserting this patch in the lateral aspect of the main pulmonary artery. Postoperatively, the TEE showed no residual shunting, mitral or tricuspid regurgitation, with the CT revealed normal blood flow of RCA without inter-arterial compression. The boy was stable during the immediate postoperative period with no arrhythmia or cyanosis. He recovered uneventfully and was discharged from the hospital.Being a rare congenital cardiac disease, the reported prevalence of AAOCA in the general population varies widely, with the true incidence remains unknown . As prevThe clinical manifestations of patients with AAOCA also vary, ranging from complete lack of symptoms to obvious myocardial ischemia such as chest pain and SAD, though the exact mechanisms and the crucial risk factors leading to SAD have not been well understood . An incrAs the most accepted surgical procedure for AAOCA with intramural segments, unroofing could reduce the incidence of SAD. Alternatively, translocation and reimplantation can be performed for those without intramural segments , for thiIn summary, our case has demonstrated the feasibility and success of a one-stage operation consisting of TOF repair and pulmonary artery translocation for a patient presented with both the TOF and anomalous RCA that arose from the left coronary sinus without an intramural segment. Although rare, patients with such complex congenital heart disease should be considered for simultaneous repair of all the anomalies for the best possible long-term outcome.


The role of trimethylamine N-oxide (TMAO) in cardiovascular diseases has been highlighted. Nevertheless, the associations of large-artery atherosclerotic (LAA) stroke with TMAO and blood lipid-related indices are little investigated.  A cross-sectional comparative study was performed on 50 patients with LAA stroke and 50 healthy controls. Basic demographic data, common vascular risk factors, and blood lipid-related indices were collected. Plasma TMAO was detected through liquid chromatography tandem mass spectrometry. Multivariable unconditional logistic regression analyses were run to assess the associations of LAA stroke with plasma TMAO level and blood lipid-related indices. The area under the curve (AUC) of the receiver operating characteristic (ROC) was computed to assess the diagnostic performance of plasma TMAO level and blood lipid-related indices for LAA stroke. p < 0.01) and Apo-B  were observed in LAA stroke patients, while lower Apo-A1 , Apo-A1 to Apo-B ratio , and HDL-C  were found in LAA stroke patients after adjusted for age and gender. Moreover, plasma TMAO , Apo-A1 , Apo-B , Apo-A1 to Apo-B ratio , and HDL-C  showed good diagnostic values for LAA stroke in adjusted models.  Compared with healthy controls, the elevated plasma TMAO level (odds ratio [OR], 7.03; 95% confidence interval [CI], 2.86, 17.25;  The plasma TMAO level, Apo-A1, Apo-B, and HDL-C are important biomarkers for LAA stroke patients. Stroke is a significant cause of death worldwide , and larN-oxide (TMAO) with the pathogenesis of cardiovascular disease has been attracted [Recently, more attention on the association of plasma trimethylamine ttracted –10. Prevttracted , atherotttracted , ischemittracted , and strttracted . Plasma ttracted . It may ttracted . Moreovettracted –18, whicOn the above grounds, we speculate that plasma TMAO level and blood lipid-related indices may be associated with LAA stroke patients. Therefore, in this cross-sectional comparative study, we aimed to investigate whether circulating TMAO level and blood lipid-related indices are associated with LAA stroke.This comparative study was performed from October 2018 to November 2019. All subjects were enrolled from two research centers in China . The local Ethics Committees of Xuanwu Hospital, Capital Medical University (LYS2018008), and Dongyang People's Hospital, Wenzhou Medical University (2017-KY-036), approved the study protocols. All subjects and/or their legal representatives provided written informed consent.The 50 LAA stroke patients who suffered acute cerebral ischemia, meeting the stroke diagnostic criteria formulated by the Chinese cerebrovascular disease classification and were validated by magnetic resonance imaging (MRI) and/or computed tomography (CT) scan , were se50 healthy controls were recruited from relatives of outpatients or inpatients (including their immediate family members). Their inclusion criteria were as follows: (1) aged 18 years or older, (2) Han Chinese, (3) no limitation on gender, (4) no history of cerebrovascular diseases, (5) no history of atherosclerotic diseases in the carotid arteries, (6) no the atrial fibrillation, and (7) regular physical examinations. Exclusion criteria were not different from the exclusion criteria of the stroke patient group.μL, 10 μmol/L d9-TMAO, and 20 μL plasma was vortexed for 1 minute. The supernatant was centrifuged at 15,000 × g at 4°C for 25 min and then transferred to a new sample bottle for the check. The 10 mL of supernatant was injected directly into a silica column at a flow rate of 0.8 mL/min with 80% A (0.1% formic acid in water) and 20% B (methanol). TMAO and d9-TMAO were detected by the positive multiple reaction monitoring mass spectrometry mode by characteristic precursor–production transitions including m/z 76/58 and m/z 85/66. For detection of TMAO concentration, a standard curve using multiknown concentrations of TMAO was used. The median (interquartile range) of healthy participants' reference values was 2.8 (1.9-4.8) μmol/L. The intra-assay and interassay coefficients of variation were 1.9%-5.6% and 2.9%-8.4%, respectively. Testers were blind to all subjects' clinical data.The overnight fasting blood samples were collected for routine blood lipid-related indices at 6: 00 to 7: 00 AM before breakfast. Plasma was prepared from the blood of EDTA tube and kept at -80°C until examination. Plasma TMAO level was qualified via stable isotope dilution liquid chromatography tandem mass spectrometry, consistent with the previous reported , 13. In b = 1,000 s/mm2, 2 mm isotropic resolution, and 30 diffusion directions), and 3D time-of-flight angiography. The slice layer of MRI was 5 mm, and the slice interval was 1.5 mm. Acute cerebral infarction was defined as high signal intensity on the DWI sequence. Infarct areas were manually qualified on DWI sequences. The images were collected and analyzed by experienced stroke neurologists who were blind to the participants' other characteristics. Infarct volumes were computed by slice layer multiplied by infarct area in each slice.Imaging acquisition details were standardized in both sites. The area of carotid artery plaque was measured via the common color Doppler ultrasound system by measuring the areas of unstable and stable carotid artery plaques. All patients received MRI within 24 hours of hospitalization with a 1.5 T or 3.0 T scanner. The sequence includes axial spin-echo T1-weighted sequence, T2-weighted sequence, fluid-attenuated inversion recovery sequence, DWI sequence , apolipoprotein A1 (Apo-A1), apolipoprotein B (Apo-B), Apo-A1/B ratio, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), D-Dime, and thrombin time (TT)). Model 1 was unadjusted. Model 2 was adjusted for sex and age. The area under the curves (AUCs) of the receiver operating characteristic (ROC) were also calculated to assess their diagnostic performance of these abovementioned indices. Correlations between plasma TMAO and Lp-a, Apo-A1, Apo-B, Apo-A1/B ratio, TC, HDL-C, LDL-C, TG, D-Dimer, and TT were calculated using Spearman's correlation.Statistical tests were conducted using SAS, version 9.4 (SAS Institute Inc). Chi-square test or Fisher's exact test was used to compare group differences of categorical variables. Mann-Whitney p < 0.01). Significance was also observed between LAA stroke patients and healthy controls in age, gender, smoking, alcohol, diabetes, and hypertension, the volume of acute cerebral infarction, Lp-a, Apo-A1, Apo-B, the ratio of Apo-A1/B, HDL-C, and D-Dimer (p < 0.05), while no statistical differences were found in obesity, area of carotid artery plaque, TC, LDL-C, TG, and TT (p > 0.05).50 LAA stroke patients and 50 healthy controls were included in the study. p < 0.01), while lower Apo-A1 , lower Apo-A1 to Apo-B ratio , lower TC , and lower HDL-C  were associated with higher risks of LAA stroke patients in an unadjusted model.The multivariate unconditional logistic regression analyses revealed that elevated plasma TMAO was associated with higher risk of LAA stroke  and higher Apo-B  were associated with higher risks of LAA stroke, while lower Apo-A1 , lower Apo-A1 to Apo-B ratio , and lower HDL-C  were observed in LAA stroke patients.After adjusting for sex and age, logistic regression exhibited that higher plasma TMAO level  and lower Apo-A1 to Apo-B ratio  were found to have good diagnostic performance for vulnerable LAA stroke in an unadjusted model.While adjusted for sex and age in multivariate unconditional logistic regression, the elevated plasma TMAO  and the increased Apo-B  were of significant diagnostic values for vulnerable LAA stroke. By contrast, the lower Apo-A1 , the lower Apo-A1 to Apo-B ratio , and lower HDL-C  showed good diagnostic ability for suffering LAA stroke.β = 4.49, p = 0.04) (p > 0.05)  . Further > 0.05) .The main findings of this study include plasma TMAO level in LAA stroke patients was significantly higher than that of the control group. Furthermore, for LAA stroke patients, the increased plasma TMAO level and Apo-B were risk factors, while lower Apo-A1, Apo-A1 to Apo-B ratio, and HDL-C were risk factors. Moreover, the increased plasma TMAO level and Apo-B were good diagnostic values for susceptible LAA stroke, but lower Apo-A1, Apo-A1 to Apo-B ratio, and HDL-C were good diagnostic values for suffering LAA stroke. Interestingly, the elevated plasma TMAO level was positively correlated with the area of carotid artery plaque in healthy controls, but not associated with the volume of acute cerebral infarction and area of carotid artery plaque in LAA stroke patients.Our results also replicated the previous reports on the risk factors included age, smoking, alcohol drinking, diabetes, and hypertension , 22 and In our study, the result that the elevated plasma TMAO and Apo-B were found to be risk factors for LAA stroke may be important manageable biological markers for exploring the potential of having LAA stroke in those with large-artery atherosclerosis. These markers combined with other factors on blood lipid-related indices, such as Apo-A1, Apo-A1 to Apo-B ratio, and HDL-C, may reflect the progress of pathogenesis of LAA stroke and be a panel of assessments for individuals with large-artery atherosclerosis without stroke. It has been reported that plasma TMAO is a potential mediator of the pathogenesis of atherosclerotic diseases, including stoke and myocardial infarction . Our finOur study did not reveal that the elevated plasma TMAO was positively correlated with the area of carotid artery plaque in healthy controls but not significantly correlated with volume of acute cerebral infarction and area of carotid artery plaque in LAA stroke patients. The previous study supported this point of our results . It showThe strengths of the study include a homogeneous LAA stroke population, blind evaluation on TMAO and imaging collection and analysis, and simultaneous detection of multiple blood lipid indicators. The study has limitations. Firstly, the sample was not big in both groups; the results of the current study should be confirmed in a large sample. Secondly, the positive control group, such as different stroke types, was a lack in this study. The positive control group of stroke, except for LAA stroke, should be designed for exploring the role of plasma TMAO and the associations of TMAO and blood lipid-related indices in various types of stroke, including different LAA strokes with various responsible large arteries. Lastly, the potential factors on plasma TMAO level, including diet, gut microbiota, drug management, and liver flavin monooxygenase , 28, 29,To be our best knowledge, we firstly explored the association of LAA stroke with plasma TMAO and blood lipid-related indices. We demonstrated that plasma TMAO level, Apo-A1, Apo-B, and HDL-C could be as the potential biomarkers on LAA stroke and in blood will be beneficial for early managing and preventing stroke , especia