It is understood that cancer is a clonal disease initiated by a single cell, and that metastasis, which is the spread of cancer from the primary site, is also initiated by a single cell. The seemingly natural capability of cancer to adapt dynamically in a Darwinian manner is a primary reason for therapeutic failures. Survival advantages may be induced by cancer therapies and also occur as a result of inherent cell and microenvironmental factors. The selected "more fit" clones outmatch their competition and then become dominant in the tumor via propagation of progeny. This clonal expansion leads to relapse, therapeutic resistance and eventually death. The goal of this study is to develop and demonstrate a more detailed clonality approach by utilizing integrative genomics.Patient tumor samples were profiled by Whole Exome Sequencing (WES) and RNA-seq on an Illumina HiSeq 2500 and methylation profiling was performed on the Illumina Infinium 450K array. STAR and the Haplotype Caller were used for RNA-seq processing. Custom approaches were used for the integration of the multi-omic datasets.Reported are major enhancements to CloneViz, which now provides capabilities enabling a formal tumor multi-dimensional clonality analysis by integrating: i) DNA mutations, ii) RNA expressed mutations, and iii) DNA methylation data. RNA and DNA methylation integration were not previously possible, by CloneViz (previous version) or any other clonality method to date. This new approach, named iCloneViz (integrated CloneViz) employs visualization and quantitative methods, revealing an integrative genomic mutational dissection and traceability  thru the different layers of molecular structures.The iCloneViz approach can be used for analysis of clonal evolution and mutational dynamics of multi-omic data sets. Revealing tumor clonal complexity in an integrative and quantitative manner facilitates improved mutational characterization, understanding, and therapeutic assignments. It is recognised that cancer is a clonal disease instigated by a single cell and that metastasis is also commenced thru a single cell -3. TumorEvolution is an important scientific concept because it works. It provides a framework to explain changes in biological systems. Cancer is the result of an evolutionary process, but it is destructive, since it involves the loss of mechanisms that are implemented to protect against uncontrolled and undifferentiated growth. Ultimately, natural selection has a harsh reality that worried Darwin, namely, all that seems to matter is reproductive success .MM is a cancer of the bone marrow characterized by a malignant transformation and proliferation of plasma cells . DefinitThere exist an array of computational methods/tools that allow one to characterize various aspects of the clonal architecture of a tumor(s). Each method employs different computational and visualization techniques, and are very briefly described. SciClone  allows fClomial  is anothAll of the aforementioned referenced methods only make use of a single modality in their characterizations of clonal architecture, namely, DNA-based mutational data, culled from whole genome or whole exome sequencing (WES) experiments. Some methods also attempt to account for the effect of copy number variation on clonal architecture. This is contrasted to the multiple modality datasets employed in the characterization of clonal structure used within iCloneViz. iCloneViz is the only known computational method for inferring clonal architecture, which integrates multiple modality datasets to derive deeper biological meaning. For example, RNA variant calling is performed  to detect whether or not a mutation found within the DNA (via a WES or WGS experiment) is detectable within a RNA transcript. Further, if a mutation is found to be present at the RNA level, the expression values associated with the transcript(s) containing the mutation can be quantified and visualized. Finally, DNA methylation data is integrated into the analysis, which could lead to hypotheses regarding methylation suppressing the expression of a tumor suppressor gene (TSG). iCloneViz tracks TSGs both by mutations and epigenetic/methylation events.Presentation) and then later when the cancer recurred . A novel bioinformatic approach named CloneViz .W: WES data in relation W for the patient experiment with the experiment identifier exp_id[i].● filter_settings: Data structure containing all filter settings including:● min_vaf: Minimum variant allele frequency (default 4%).○ max_vaf: Maximum variant allele frequency (default 100%).○ min_depth: Minimum read depth (default 20).○ max_depth: Maximum read depth (default 1000).○ min_meth: Minimum methylation percent (default 25%).○ opacity: Opacity of scatter plot points (default 50%).○ show_kg_only: Boolean indicating whether to show only mutations found in the key genes list K .○ default_filter_settings: Default values used for filtering.● /*******************************************************Name & Purpose: Main, program entry pointInputs: NoneProcessing: Establish main processing loop for iCloneVizOutputs: NoneReturns: NoneAuthors: D. Johann, E. Peterson********************************************************/main{while (window is open){patient_id = display_patient_search;button_selection, exp_id = display_patient_experiments(patient_id);process;}}/*******************************************************Name & Purpose: Display Patient Search, via patient ID display patient meta-dataInputs: noneProcessing: Display patient meta-dataOutputs: NoneReturns: ID of selected patient from MPMDBAuthors: D. Johann, E. Peterson********************************************************/display_patient_search{patient_id = input from user;P;- get / display patient meta-data via relation return patient_id;}/*******************************************************Name & Purpose: Display Patient Experiments, show available experiments for iCloneViz analysisInputs: Patient IDProcessing: Retrieve patient experimental meta-data from MPMDBOutputs: Patient experimental data now in memoryReturns: Array of experiment IDs, Button selection for selected analysis, eg, Genomic Real Estate or Paired Scatter Plot or KDE plus Scatter PlotAuthors: D. Johann, E. Peterson********************************************************/display_patient_experiments(patient_id){P;- get / display all experimental data from database (MPMDB) for patient via relation exp_id = selected experiment ids;return button_selection, exp_id}/*******************************************************Name & Purpose: Process, process data and display visualization based on the user's choice of visualizationInputs: Button selection, & Experiment IDsProcessing: Execute specific function to handle processing based on user's choice of visualizationOutputs: NoneReturns: NoneAuthors: D. Johann, E. Peterson********************************************************/process{if genomic_real_estate(exp_id[0]);else if (button_selection == 'Paired Scatter Plot')paired_scatter_plot(exp_id[0...1]);else if (button_selection == 'KD + Scatter Plot')kd_plus_scatter_plot(exp_id[0]);}/*******************************************************Name & Purpose: Genomic Real Estate, fetch WES-based mutation data, using R.NET to generate R plot and visualizeInputs: Experiment ID of experiment to visualizeProcessing: Using R.NET API, generate plot image and display in windowOutputs: Scatter plot of all mutations by chromosome and variant allele frequency, read depth is encoded by color, see Additional File 2Returns: NoneAuthors: D. Johann, E. Peterson********************************************************/genomic_real_estate(exp_id){W;- execute query to MPMDB to fetch mutation data based on the exp_id and DB relation - establish the R.NET interface and invoke R;- divide x-axis into 24 sections - scale each section by the length of each chromosomew in Wfor each {w along x-axis by position, the y-axis by variant allele frequency, and color by read depth;- plot }- export plot as image file;- return execution to .NET;- display image file in windows form;}/*******************************************************Name & Purpose: Paired Scatter Plot, fetch filter settings and call function to display paired scatter plotInputs: Experiment IDsProcessing: If first time displaying, use default filter settings to display paired plot, otherwise, fetch filter settings from user and display paired plotOutputs: NoneReturns: NoneAuthors: D. Johann, E. Peterson********************************************************/paired_scatter_plot(exp_id){display_paired_scatter_plot;while (window is open){filter_settings = read_filter_toolbar;display_paired_scatter_plot;}}/*******************************************************Name & Purpose: Display Paired Scatter Plot, calculate WES mutations in common and exclusive to each experiment and generate paired scatter plotInputs: Filter settings fetched from user input, and Experiment IDsProcessing: Fetch WES mutations, and using filter settings and referenced equations, calculate mutations in common and exclusive to each experiment; display paired scatter plot with data, see Additional File 3Outputs: A paired scatter plot for each experiment in exp_id array, based on variant allele frequencyReturns: NoneAuthors: D. Johann, E. Peterson********************************************************/display_paired_scatter_plot{// Relational algebra Eq. (9)common = exp_id[0].W ∩ exp_id[1].W;// Relational algebra Eq. (10)exp0_unique = exp_id[0].W \ exp_id[1].W;// Relational algebra Eq. (10)exp1_unique = exp_id[1].W \ exp_id[0].W;K;- label all genes in if (filter_settings.show_kg_only == true)- hide all non-labelled points;- plot common, exp0_unique, and exp1_unique based on DNAllelicFreq  using filter-settings;}/*******************************************************Name & Purpose: Read Filter Toolbar, gather user-defined filter settings, and display "TSG Methylation Table", "WES Table", or "RNA Table" if the user so desiresInputs: NoneProcessing: Collect filter settings from user; if the user clicks on "Filter" the filter settings are returned and they are used when displaying a desired plot; if the user clicks on "Show TSG Methylation Table", "Show WES Table", or "Show RNA Table", the desired table is displayed using the referenced equationsOutputs: Tables selected if clickedReturns: User-defined filter settingsAuthors: D. Johann, E. Peterson********************************************************/read_filter_toolbar{while (true){filter_settings.min_vaf = user input minimum VAF;filter_settings.max_vaf = user input maximum VAF;filter_settings.min_depth = user input minimum read depth;filter_settings.max_depth = user input maximum read depth;filter_settings.min_meth = user input minimum methylation percent;filter_settings.opacity = user input scatter plot point opacity;filter_settings.show_kg_only = user input show key gene only;if (button_click_filter)return filter_settings;  else if (button_click_show_tsg_methylation_table)- display TGS Methylation Table via Relational algebra Eq. (13);  break;  else if (button_click_show_wes_table)- display WES Table Eq. (11);  break;  else if (button_click_show_rna_table)- display RNA Table Eq. (14);  break;  }return filter_settings;}/*******************************************************Name & Purpose: KD Plus Scatter Plot, call subroutines to process and render data for each of the individual plot areas, as well as, to calculate various metircsInputs: Experiment ID of experiment to visualizeProcessing: Call subroutines to process and render the individual plot areas; call subroutine to calculate various metricsOutputs: NoneReturns: NoneAuthors: D. Johann, E. Peterson********************************************************/kd_plus_scatter_plot(exp_id){display_kd_plot;display_scatter_plot;calculate_metrics;while (window is open){filter_settings = read_filter_toolbar;display_kd_plot;display_scatter_plot;calculate_metrics;}}/*******************************************************Name & Purpose: Display KD Plot, displays the kernel density estimation curve, using the 'ks' R package to calculate an appropriate bandwidthInputs: User-defined filter settings and the experiment ID to be visualizedProcessing: Calculate the KDE for DNA mutations, use R.NET to utilize the 'ks' package Outputs: KD curve, see Figures 1 & 2Returns: NoneAuthors: D. Johann, E. Peterson********************************************************/display_kd_plot{- calculate KDE based on Relational algebra Eq. (11) and Eq. (2) for all mutations in Ŵ and weighted by copy number;- utilize R.NET to call 'hpi' function in the R 'ks', for bandwidth calculation;- display KD plot using filter_settings;}/*******************************************************Name & Purpose: Display Scatter Plot, displays the DNA mutational scatter plot, and tooltip containing RNA expression based info if availableInputs: User-defined filter settings and the experiment ID to be visualizedProcessing: Displays a scatter plot point for each DNA mutation, set each glyph depending on the degree of modality data available, and populate tooltip with RNA expression data if availableOutputs: Mutational scatter plot, see Figures 1 & 2Returns: NoneAuthors: D. Johann, E. Peterson********************************************************/display_scatter_plot{- calculate Ŵ by Relational algebra Eq. (11) using filter_settings;- set glyph for all DNA mutations to be a blue circle and place along a-axis according to variant allele frequency and along y-axis by depthfor each tuple ŵ in Ŵ having RNA data calculate {- update point glyph, ;- build hover-over tooltip to contain: gene name, transcript(s) ids and FPKM(s) from }- display scatter plot for each mutation in Ŵ with associated tooltip (if RNA data is available);}/*******************************************************Name & Purpose: Calculate Metrics, calculate various metrics for displayInputs: User-defined filter settings and the experiment ID to be visualizedProcessing: Calculate SDI, total number of mutations, and total number of key gene mutations, for the data being visualizedOutputs: Calculated metricsReturns: NoneAuthors: D. Johann, E. Peterson********************************************************/calculate_metrics{- calculate SDI using Eq. (1) and relation Ŵ ) using filter_settings;- calculate total number of mutations in relation Ŵ using filter_settings;K which are found in relation Ŵ using filter_settings;- calculate total number of key genes from - display metrics;}The authors declare that they have no competing interests.DJJ and EAP conceived and designed the study. EAP, MAB, SSC, CJH, NW and DJJ performed experiments and analyses. DJJ and EAP designed the software. EAP, DJJ and MAB implemented the software. DJJ and EAP wrote the manuscript. All authors approved the manuscript.Muti-omic relational integration. Illustration of relations used to integrate multi-omic datasets. The upper-right box (Relations & Attributes) defines all relations and their attributes. The left-most section (iCloneViz DB) lists each dataset and a name / identifier for each. Middle section (Multi-Omic Integration) illustrates the relationships and integration of each dataset using intermediate relations. Each intermediate integration is annotated with the attributes used in each combination. Each intermediate relation is further annotated with the equation used in the manuscript to form the given relation. The final multi-omic "Integrated Relation" is shown in the lower right.Click here for fileA corresponds to the Presentation sample and B to Relapse. These provide a general view of the inherent mutational events on a chromosomal basis. The x-axis contains an ordered list of chromosomes , each sized by the number of base pairs (bp) it contains. The y-axis is ordered by variant allele frequency (VAF), and the color scale indicates sequence depth. Each variant is a point in the plot.Genomic mutational overview. A genomic mutational overview of two experiments is computed and displayed. Click here for filePresentation on the x-axis compared to Relapse on y-axis. Both the × and y-axes are based on VAF. Variants are colored to indicate whether they are shared or unique. See legend for color assignments.Scatter plot of paired samples. Displayed are variants in the Click here for fileMYC oncogene with mutation showing possible splicing events. Illustrated is a lolliplot diagram of MYC showing the possible missense mutations in the coding region depending on splicing.Click here for fileiCloneViz pseudocode flow diagram. Illustrated is the execution flow of iCloneViz and its associated subroutines. Each subroutine and its formal parameters is represented as a node. Each arc represents a subroutine call from one subroutine to another.Click here for file


In Thailand, porcine deltacoronavirus (PDCoV) was first identified in November 2015. The virus was isolated from piglets experiencing diarrhea outbreak. Herein, the full-length genome sequence of the Thai PDCoV isolate P23_15_TT_1115 is reported. The results provide a clearer understanding of the molecular characteristics of PDCoV in Thailand. Deltacoronavirus, family Coronaviridae (––Porcine deltacoronavirus (PDCoV) is an enveloped, single-stranded, positive-sense RNA virus in the genus aviridae –3. PDCoVviridae –. The virviridae ––10. Cliniridae ––, 11,12. A surveillance study was conducted to identify the PDCoV presence in Thailand that focused on herds with diarrhea outbreak and low mortality. Six intestinal samples were collected from piglets with diarrhea in five herds experiencing diarrhea outbreak. Total RNA was extracted and detected the presence of PDCoV RNA using primers specific to membrane (M) and nucleocapsid (N) genes. PCR positive samples were further investigated. The full-length genome was sequenced using 16 overlapping regions of each genome, cloned in pGEM-T easy vector (Promega), and sequenced in both directions in triplicate according to the previously reported protocol . The 5′ The full-length genome of the Thai PDCoV isolate P23_15_TT_1115 was characterized. The full-length genome sequence of P23_15_TT_1115 is 25,402 nucleotides (nt) in length. Genome organization of the isolate resembles that of other PDCoV genomes with the following gene order: 5′ untranslated region (UTR), open reading frame 1a/1b (ORF 1a/1b), spike (S), envelope (E), membrane (M), nonstructural protein 6 (Nsp6), nucleocapsid (N), nonstructural protein 7 (Nsp7), 3′ UTR. The lengths of ORF 1a/1b, S, E, M, and N genes are 18,786; 3,477; 249; 651; and 1,026 nt, respectively. The phylogenetic tree was constructed based on full-length PDCoV genomes of 23 isolates available in GenBank, and phylogenetic analysis demonstrates that the P23_15_TT_1115 isolate belongs to a group separated from PDCoVs reported in both China and the United States.51N) amino acid at position 51, similar to isolates from China.The full-length genome of P23_15_TT_1115 was compared to 23 isolates available in GenBank. P23_15_TT_1115 was more highly homologous to PDCoV isolates from China, with nucleotide and amino acid similarities at 97.2 to 97.8% and 93.0 to 94.0%, respectively. In comparison, P23_15_TT_1115 shares a similarity  with isolates from the United States. Moreover, the genetic analysis based on the S gene demonstrated that P23_15_TT_1115 is closely related to China PDCoV with similarities of 95.6 to 96.7% and 95.9 to 98.1% at the nucleotide and amino acid levels, respectively. Twenty-four substitutions at the amino acid level were observed between P23_15_TT_1115 and the isolates from China. Moreover, P23_15_TT_1115 owns a deletion of 1 (The results in this study suggest that P23_15_TT_1115 is a novel isolate, closely related to PDCoV isolates from China. The studies investigating the molecular epidemiology, prevalence, and evolution of PDCoV in Thailand are urgently required.KU984334.The complete genome sequence of P23_15_TT_1115 has been deposited in GenBank under the accession number


Aspergillusflavus (A. flavus)–peanut pathosystem, development and metabolism of the fungus directly influence aflatoxin contamination. To comprehensively understand the molecular mechanism of A. flavus interaction with peanut, RNA-seq was used for global transcriptome profiling of A. flavus during interaction with resistant and susceptible peanut genotypes. In total, 67.46 Gb of high-quality bases were generated for A. flavus-resistant (af_R) and -susceptible peanut (af_S) at one (T1), three (T2) and seven (T3) days post-inoculation. The uniquely mapped reads to A. flavus reference genome in the libraries of af_R and af_S at T2 and T3 were subjected to further analysis, with more than 72% of all obtained genes expressed in the eight libraries. Comparison of expression levels both af_R vs. af_S and T2 vs. T3 uncovered 1926 differentially expressed genes (DEGs). DEGs associated with mycelial growth, conidial development and aflatoxin biosynthesis were up-regulated in af_S compared with af_R, implying that A. flavus mycelia more easily penetrate and produce much more aflatoxin in susceptible than in resistant peanut. Our results serve as a foundation for understanding the molecular mechanisms of aflatoxin production differences between A. flavus-R and -S peanut, and offer new clues to manage aflatoxin contamination in crops.In the  Aspergillusflavus (A. flavus) is a globally distributed filamentous, saprophytic fungus that frequently infects oil-rich seeds of various crop species during pre- and post-harvest, with subsequent production of mycotoxins such as cyclopiazonic acid, aflatrem, and the well-known aflatoxin . Be. Be36]. s so few  that theubation) . In eachubation) ; furtherubation) .vs. af_S and T3 vs. T2 were identified using the DESeq R package . Only those genes with the corrected p (q) value < 0.05 were considered to be differentially expressed [A. flavus interaction with R and S peanut (vs. T2 offered insights into the metabolic and regulatory processes in A. flavus during its interaction with the peanut (10(RPKM+1) for the four A. flavus samples of af_R and af_S incubated for three and seven days  in comparisons of af_R xpressed . The 179S peanut , while te peanut . To obseven days A. Similavs. af_S_T2), while 1791 DEGs were found between af_R and af_S at T3 (af_R_T3 vs. af_S_T3)  and 45 DEGs were obtained between af_S at T3 and at T2 (af_S_T3 vs. af_S_T2), suggesting a markedly higher number of gene expression changes in af_R than in af_S. Eighteen DEGs exhibited common differential expression patterns in comparisons of af_R_T3 vs. af_R_T2 and af_S_T3 vs. af_S_T2 (vs. af_R_T2 were different from those in af_S_T3 vs. af_S_T2.Eleven DEGs were found between af_R and af_S at T2 (af_R_T2 af_S_T3) B. Furthe af_S_T3 B. The ab af_S_T2 C, implyivs. af_S_T2 and af_R_T3 vs. af_S_T3, respectively). The GO functional enrichment analysis of the 1113 (62.14%) DEGs with GO annotation in the af_R_T3 vs. af_S_T3 revealed significantly enriched terms in the biological process and the molecular function categories (vs. af_R_T2 and af_S_T3 vs. af_S_T2), respectively. Of the 263 (55.48%) DEGs with GO annotation in af_R_T3 vs. af_R_T2, only two GO terms (GO: 0003824 and GO: 0016491) in the molecular function category and three  in the biological process category were significantly enriched  enrichment analysis was performed using the GOseq method in Blast2GO . We firstegories . Catalytenriched . Other tvs. af_S_T2, af_R_T3 vs. af_S_T3, af_R_T3 vs. af_R_T2 and af_S_T3 vs. af_S_T2 were analyzed to identify their associated KEGG metabolic pathways. Consistent with the results of the GO analysis, no KEGG pathways were significantly enriched  in DEGs data obtained from af_R_T2 vs. af_S_T2 and af_S_T3 vs. af_S_T2. We found that 14 pathways were significantly enriched in DEGs between af_R_T3 and af_S_T3, including 12 pathways involved in the supply of nutrient and energy for fungal development, biosynthesis of secondary metabolites (afv01110) and peroxisomes (afv04146) .To further investigate the biological functions and interactions of genes, a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted using KEGG Orthology Based Annotation System (KOBAS)  . All DEGfv04146) A. Twentyfv04146) B. AlthouA. flavus was significantly changed when the fungus interacted with R and S peanut  encoding the alpha-N-arabinofuranosidase was identified in af_S_T3 vs. af_S_T2 and it was up-regulated in this comparison. However, no mycelial growth-related DEGs were observed in neither af_R_T2 vs. af_S_T2 nor af_R_T3 vs. af_R_T2. Concurrently, transcriptions of conidia-specific genes, such as conidial hydrophobin RodA/RolA (AFLA_098380), conidiation-specific proteins  and conidial development related genes such as AtfA (AFLA_031340) and PksP (AFLA_00617), were significantly changed to various degrees in different comparisons  were up-regulated in af_R_T3 vs. af_R_T2. However, no DEGs involved in conidial development were found in both af_R_T2 vs. af_S_T2 and af_S_T3 vs. af_S_T2.By analyzing gene expression pattern data obtained from deep sequencing, especially those in the list of the 1926 genes that were significantly differentially transcribed , we founS peanut . SeventeS peanut . All 17 parisons . Six of vs. af_S_T2, af_R_T3 vs. af_S_T3, af_R_T3 vs. af_R_T2 and af_S_T3 vs. af_S_T2, respectively. Some of these DEGs were differentially expressed in two or three different comparisons. Additionally, the 54#, 9# and 26# cluster with eight, seven and six DEGs, respectively, were the first three dominant ones in these 36 secondary metabolism gene clusters; while 14 of the 36 clusters only possessed one DEG. The aflatoxin biosynthetic pathway cluster (54#) was most worthy focused on because the carcinogenic, mutagenic aflatoxin has been characterized in A. flavus [vs. af_S_T2, but none were differentially expressed between these two pathosystems. In af_R_T3 vs. af_S_T3, 18 down- and 15 up-regulated genes in 54# cluster were obtained. Interestingly, three genes  were significantly down-regulated. Surprisingly, we also identified other two significantly down-regulated genes (AFLA_112820 and AFLA_050450) involved in aflatoxin biosynthesis  of the aflatoxin biosynthetic cluster (54#) was significantly enriched in oxidoreductase activity (GO: 0016491). The aflX/ordB gene (AFLA_139160) encoding a monooxygenase participates in aflatoxin biosynthesis [vs. af_R_T2. Interestingly, aflA/fas-2 (AFLA_139380), aflG/avnA (AFLA_139260), aflN/verA (AFLA_139280) and aflP/omtA (AFLA_139210) of the 54# cluster, which were differentially up-regulated in af_R_T3 vs. af_R_T2, were enriched in different biological process  and molecular function (GO: 0003824 and GO: 0016491) categories. Similar to the results of the GO analysis, several KEGG pathways were enriched in DEGs between af_R_T3 and af_S_T3 and between af_R_T3 and af_R_T2, whereas no KEGG pathways were significantly enriched in comparisons of af_R_T2 vs. af_S_T2 or af_S_T3 vs. af_S_T2. The biosynthesis of secondary metabolites (afv01110) was significantly enriched in DEGs between af_R_T3 and af_S_T3; moreover, this pathway was enriched in DEGs AFLA_069370, AFLA_070820 and AFLA_116080 in secondary metabolite clusters 24#, 25# and 41#, respectively. The aflatoxin biosynthetic pathway is a complex secondary metabolic process that is regulated and influenced by over 30 genes in the A. flavus genome [vs. af_S_T3 and af_R_T3 vs. af_R_T2, the biosynthesis of secondary metabolites (afv01110) pathway was not found to be enriched in these DEGs. Taken together, these results implied that a greater number of repressed responses took place in af_R compared with af_S, while many more activated responses in af_R than in af_S as the interactive time increased in the A. flavus-peanut pathosystem.The ynthesis . By conts genome ,49. AlthA. flavus than the R, namely, mycelia of A. flavus can much more easily penetrate the S than the R peanut seed.Nutrients are indispensable elements required for the growth and metabolism of all living organisms, including plants and pathogens. For successful infection of the host plant and establishment of disease, fungal pathogens have evolved complex regulatory mechanisms to facilitate penetration, colonization and absorb nutrition for development and metabolisms, meanwhile to protect themselves against host defensive responses ,51,52,53Aspergillus [vs. af_S_T3, five down-regulated DEGs  involving in aflatoxin biosynthesis were found. Among them, aflX/ordB (AFLA_139160), aflNa/hypD (AFLA_139270) and aflD/nor-1 (AFLA_139390) belong to the aflatoxin biosynthetic cluster. The oxidoreductase Nor-1 together with NorA and NorB reduce norsolorinic acid, the first stable intermediate in the aflatoxin biosynthesis, to averantin [aflA, aflC, aflG, aflP, aflN and aflCa) in the 54# cluster were up regulated in af_R_T3 compared with af_R_T2. The first step in aflatoxin biosynthesis is the reaction of acetyl-CoA and malonyl-CoA catalyzed by Fas-1/aflB and aflA/Fas-2 to form the starter unit hexanoate [aflG/avnA), monooxygenase (aflN/verA) and O-methyltransferase A (aflP/omtA) enzymatic reactions are respectively involved in the conversion of averantin to 5-hydroxyaverantin, versicolorin A to demethyl-sterigmatocystin and sterigmatocystin to O-methyl sterigmatocystin in aflatoxin biosynthetic pathway [Aflatoxins are biosynthesized through several enzymatic reactions in mycelia of ergillus ,49 and tergillus ,58,59,60verantin . Expressexanoate , followeexanoate . P450 mo pathway ,48. AlthA. flavus, germinate as mycelia to colonize the host plant. The survival ability of A. flavus conidia under severe environmental conditions is stronger than that of mycelia [A. flavus dominantly depends on the dispersal of conidia by air, water and soil movement, rain splash and biotic factors [A. flavus [A. flavus may form conidia in cavities or intercellular spaces of the cotyledon. The formation of conidia in A. flavus requires the concerted activity of numerous signaling proteins and transcription factors [atfA (AFLA_031340) was also down-regulated in af_R_T3 vs. af_S_T3. Interestingly, AtfA, as a bZIP transcription factor, possesses important functions in conidial development [vs. af_R_T2, 5 up-regulated DEGs related to conidial development were obtained, including 4 conidia-specific genes  and one conidial yellow-pigment biosynthesis-related gene pksP/alb1 (AFLA_006170). The pksP/alb1 gene encodes a polyketide synthase (PksP) involved in the first step of conidial pigment biosynthesis. PksP catalyzes the reaction of acetyl coenzyme A (acetyl-CoA) andmalonyl-CoA to form the heptaketide naphthopyrone [A. flavus mycelial growth, conidial formation and aflatoxin production during infection and colonization the peanut need to be uncovered in order to paint a complete picture of the interactive mechanism of A. flavus with peanut. This comprehensive transcriptional profiling of A. flavus during interaction with the peanut should advance our fundamental understanding of the various associated genes and major metabolic pathways, thereby providing a direction for further study on the management of aflatoxin contamination in crops.Conidia, the asexual reproductive structure of  mycelia . In addi factors . Previou. flavus , with th factors . Transcr factors  were sigelopment  and stabelopment . Considehopyrone ,68. Our A. flavus during the fungus interaction with the peanut. The research demonstrated that the global transcriptional analysis provided an exhaustive view of genes involved in development of mycelia and asexual spores, controlling of biosynthesis and activities of enzymes, conidial pigments and secondary metabolites processes, which were coordinately influenced in A. flavus by its host peanut (R and S genotypes). The transcriptome comparisons revealed that DEGs associated with mycelial growth and penetration, conidial formation and development, and aflatoxin biosynthesis and accumulation were up-regulated in af_S compared with af_R. This differential transcription may explain why aflatoxin accumulation was much higher in A. flavus-S peanut pathosystem than in A. flavus-R. However, further research is required to determine whether these DEGs are the genes responsible for the difference in aflatoxin accumulation between A. flavus-R and A. flavus-S pathosystems. Further functional exploration of these genes may provide useful information for their future application in the management of aflatoxin contamination in crops.In this study, an RNA-seq approach was employed for the first time to investigate molecular events involved in the development and metabolism of A. flavus was maintained in 20% glycerol at −80 °C at the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (CAAS-OCRI). To prepare the A. flavus inoculation, the stored conidia of AF2202 were cultured on the potato dextrose agar medium for 7 d at 29 ± 1 °C. The fresh conidia were then collected and suspended in sterile water containing 0.05% Tween-80. The concentration of conidia in the suspension was determined using a hemocytometer [The AF2202 strain of toxigenic A. flavus at post-harvest (6 CFU/mL) was then directly added to 10.0 g of peanut seeds in a sterile Petri plate. The inoculated samples were placed in an incubator and cultured at 29 ± 1 °C in darkness. After incubation for 1, 3 and 7 d, the A. flavus-colonized seeds were taken out to test aflatoxin content (five replications) by high-performance liquid chromatography [The peanut cultivars Zhonghua 6 and Zhonghua 12 were cultivated and supplied by CAAS-OCRI . The mature seeds of both Zhonghua 6 and Zhonghua 12 are susceptible to seed invasion by -harvest , while ZA. flavus-R and -S peanut pathosystems, the aflatoxin content of these pathosystems was initially tested at the 2nd day after incubation. Aflatoxin content increased at maximum rate between the 3rd and the 4th day, and then remained stable after the 7th day in both peanut cultivars (our unpublished data). Beginning on the 2nd day, the aflatoxin content of the A. flavus-R pathosystem was far lower than that of the A. flavus-S; at its peak, the aflatoxin content of A. flavus-S was over 10-fold that of A. flavus-R. These differences in aflatoxin production between the two pathosystems suggested that the genetic expression of A. flavus was affected by its colonized host peanut. The 1st, 3rd and 7th day as the inflection time points .Although aflatoxin production trends differed between e points  in the pA. flavus-peanut pathosystem was isolated using an RNeasy® Plant Mini kit , according to the manufacturer’s protocol. All RNA samples were treated with RNase-free DNase I. A NanoDrop® 2000 spectrophotometer , a Qubit®Fluorometer 2.0  and an Agilent 2100 bioanalyzer  were used to test the concentration and integrity of RNA samples, and confirm that all RNA samples had an integrity value > 6.5. RNA quality detection, cDNA libraries construction and RNA sequencing were performed at the Novogene Bioinformatics Technology Co. Ltd.  according to previously described methods [Total RNA from the  methods .Raw data (raw reads) in fastq format were first processed using in-house perl scripts. Clean data (clean reads) were obtained by removing low-quality reads and those containing adapters, poly-N tails from the raw data. The Q20 and Q30 values, GC content, and sequence duplication levels were calculated for the clean data. All downstream analysis used the clean data with high quality. The sequencing data generated in this study have been deposited at the NCBI Short Read Archive database and are accessible through SRA series accession number SRP065525 (BioProject ID: PRJNA300619).A. flavus were downloaded directly from the Ensembl Genomes website [The genome and gene annotation files of  website . After c website , paired- website . The ref website . HTSeq ( website .p (q) value < 0.05.Statistical analyses for discovering differentially expressed genes (DEGs) were performed with the DESeq R package . To evaluate the individual effects of the host peanut (Zhonghua 6 and Zhonghua 12) and time points (T1–T3), a multifactorial analysis was conducted using the multi-factor designs method of DESeq . This mep (q) value < 0.05 were considered to be significantly enriched in DEGs. KOBAS software  [p (q) value < 0.05 was considered to be significantly enriched in DEGs [GO enrichment analysis of DEGs was implemented using GOseq   with a ca, 2014)  was used in DEGs .


Pseudomonas aeruginosa bacteriophage (phage) belonging to the Pbunalikevirus genus of the Myoviridae family of phages. It was isolated from hospital sewage. vB_PaeM_CEB_DP1 is a double-stranded DNA (dsDNA) phage, with a genome of 66,158 bp, containing 89 predicted open reading frames. vB_PaeM_CEB_DP1 is a  Pseudomonas aeruginosa PAO1 as the host strain. Its host range was evaluated using a panel of 30 P. aeruginosa clinical isolates, and this phage was able to infect approximately 57% of them.The lytic bacteriophage vB_PaeM_CEB_DP1 was isolated from hospital sewage in Portugal using Myoviridae family of phages. Furthermore, the growth parameters determined by the one-step growth experiment showed that phage vB_PaeM_CEB_DP1 has a latent period of ~50 min, a rise period of ~50 min, and a burst size of ~70 phages per infected cell.The morphological characterization of phage vB_PaeM_CEB_DP1 was performed by transmission electron microscopy, revealing an icosahedral head of ~70 nm in diameter and an ~140- × 18-nm contractile tail. Thus, it was possible to determine that this phage belongs to the The phage genome was sequenced using Roche 454 sequencing procedures at the Plateforme d’analyses of the Institut de Biologie Intégrative et des Systèmes . Shotgun reads were assembled using the gsAssembler module of Newbler v 2.5.3.Pbunalike phages  were first annotated using myRAST . Sequence phages . Predicte phages . The tooe phages  was usede phages , 6, 7, nThe genome of the phage vB_PaeM_CEB_DP1 consists of 66,158 bp of dsDNA with a GC content of 55.6%. The whole genome was scanned for CDSs, resulting in 89 predicted genes ranging from 141 bp to 3,111 bp. Furthermore, 37 of these genes are rightward oriented while 52 are leftward oriented. The initiation codon of 90% of the genes is ATG, while 8% start with GTG and 2% with TTG. According to BLASTP analyses, 68% of the proteins encoded in the genome of vB_PaeM_CEB_DP1 are hypothetical. This study further revealed that this phage has 7 predicted promoters and 12 terminators.Pbunalike genus are reported to encode linear, nonpermuted genomes , KPP12 (94.3%) and vB_PaeM_PAO1_Ab27 (93.1%).The genome of phage vB_PaeM_CEB_DP1 shares high nucleotide identity with other P. aeruginosa phage vB_PaeM_CEB_DP1 was deposited in GenBank under the accession number KR869157.The complete genome of the


The effect of cannabis on emotional processing was investigated using event-related potential paradigms (ERPs). ERPs associated with emotional processing of cannabis users, and non-using controls, were recorded and compared during an implicit and explicit emotional expression recognition and empathy task. Comparisons in P3 component mean amplitudes were made between cannabis users and controls. Results showed a significant decrease in the P3 amplitude in cannabis users compared to controls. Specifically, cannabis users showed reduced P3 amplitudes for implicit compared to explicit processing over centro-parietal sites which reversed, and was enhanced, at fronto-central sites. Cannabis users also showed a decreased P3 to happy faces, with an increase to angry faces, compared to controls. These effects appear to increase with those participants that self-reported the highest levels of cannabis consumption. Those cannabis users with the greatest consumption rates showed the largest P3 deficits for explicit processing and negative emotions. These data suggest that there is a complex relationship between cannabis consumption and emotion processing that appears to be modulated by attention. There are a variety of explanations of how the brain processes emotion emphasizing differing levels at which an explanation is focused. Some approaches emphasize a physiological structural account, others are based on a higher, more “cognitive” level of understanding, with less emphasis on the underlying structures , 2, 3. A9-tetrahydrocannabinol (THC) consumed and participants’ ability to identify emotional expressions in faces showing negative emotions such as fear and anger, but had little effect on faces showing sadness and happiness [The effects of cannabis on cognition and the brain is a rapidly evolving area of investigation which has provided evidence for a complex interaction between physiological and psychological processes. Cannabis consumption elicits both immediate (acute), residual and long-term changes in brain activity, that are manifested throughout the body such as altered appetite and food intake, altered sleep patterns, and changes in measures of executive function and emotional behavior . Crean eappiness . Behavioappiness . In a reappiness . This suBehavioral studies have also linked heavy cannabis use to impairments in emotion processing compared to controls. In a dynamic emotional expression task where participants were asked to identify emotional expressions as faces morphed from open mouthed to an expression, either positive (happy) or negative (fearful) cannabis user’s accuracy and reaction time performance was impaired. Individuals who used cannabis fifteen times a month and more than fifty times in their lifetime showed increased reaction times and decreased accuracy to the faces that became negative .Recent research seeking to clarify the effects of cannabis use on brain structure is mixed. In particular, structural magnetic resonance imaging (MRI) data indicated no significant differences in the amygdala, a critical structure for emotion processing, in both adult and adolescent brains of daily users . HoweverOne approach that can be considered a valuable tool in further investigating the complexity of emotion processing is event related potential (ERP) methodologies. Structural changes in the brain do not always translate to differences in function and behavior. EEG techniques allow us to investigate the relationship between biomarkers (brain mechanisms) of a distributed neural network and associated behavior. This approach is particularly relevant to determine whether or not cannabis use has an effect on the circuitry of the brain when structural differences have not been found.EEG recordings provide information with resolution in the milliseconds. This approach consists of the measurement of the summated firing of neurons in the cortex through electrodes placed on the scalp. When this activity is averaged and time-locked to a specific event , an event-related potential (ERP) is obtained. ERPs provide an indication of the temporal dynamics of cortical activity following that specific event, allowing us to obtain information on the time course of emotion processing. This average is then compared to those obtained for other conditions  to examine whether processing of different types of information diverges in time and across electrode sites. Through extensive research some patterns in ERPs associated with cognitive processes have been characterized by ERP components—the amplitude of the waveform at a specific point in time. For example, the P3 or P3 complex component is defined as a change in voltage in a positive direction (increase in amplitude) occurring between 200–400ms on average after stimulus onset .One ERP in particular is of interest to emotion processing and also cannabis use. The P3 complex component has been associated with task-relevant stimulus evaluation and attention allocation and it is has been consistently linked to emotion processing , 19, 20.The exact nature of the P3 and its relationship to emotion is still very much under investigation . HoweverCannabis use has been associated with deficits in the P3 especially pertaining to complex cognitive tasks such as working memory, and selective attention , 31, 32.The consistent P3 pattern exhibited during emotional processing represents an effective means of assessing the effects of cannabis use on neural processing. The P3 has implications for understanding both the short and long-term effects cannabis use has on emotion processing. Despite this the majority of research into the effects of cannabis on emotional expression recognition has focused on either behavioral approaches, for example Ballard et al , or struTo better understand the role of attention on the effects of cannabis in emotion processing our current study uses a paradigm based on Relleke et al  and exteIf cannabis use effects emotion processing we would expect to see differences in the P3 to our emotional stimuli for our cannabis user group compared to controls. If attention is also affected by cannabis use, then greater demands on attention, driven by the levels of attentional engagement, elicited by our three processing tasks, implicit, explicit and empathic would also lead to P3 differences.We therefore hypothesize that cannabis users will show a difference in P3 amplitude compared to non-cannabis using controls. This will be further influenced by the type of emotion and task demands.Seventy-three undergraduate students and volunteers recruited from the community provided written consent and self-report of demographic information which is summarized in Undergraduate students who were recruited from the departmental research pool received credit in a Psychology course for their participation. Participants from the local community received no compensation. The study was approved by Colorado State University’s Office of Research Integrity & Compliance Review Office Institutional Review Board (IRB) Protocol ID: 12-3716H.All participants provided written consent and completed a general demographic questionnaire. They were further screened for symptoms of depression and anxiety using the Center for Epidemiological Studies Depression Scale (CES-D)  and the After the screening and assessment portion of the study was completed, participants were fitted with a recording EEG cap, detailed below in the EEG acquisition portion of the methods. During the recording, detailed below, participants completed an emotion processing task, presented on a Dell desktop computer at a viewing distance of 30cm using Stim2 software , 41.The emotion processing task required that participants viewed faces depicting positive (happy), neutral, and negative (angry and fearful) emotional expressions, obtained from the Radboud Faces Database . Thirty-After EEG data collection, cannabis use was assessed by self-report using a questionnaire developed specifically for this study; The Recreational Cannabis Use Evaluation (R-CUE). R-CUE was created to better understand the ecology of cannabis use in Colorado’s recreational system of high volume, potency, and variety. Thus, R-CUE consists of questions regarding type of use and method of intake—including use of edibles, concentrates, and transdermal applications—in addition to information about potential current use, relational previous use, and years of use. Based on participants’ responses they were assigned to control or cannabis user groups see . CannabiElectroencephalogram (EEG) was recorded from 25 Ag/AgCl electrodes covering regions of interest (ROI’s) consistent with the measurement of the P3 ERP component and identified based on previous research , 7, 41  in milliseconds, percent correct for sex and emotion identification tasks, and average rated empathy  for the empathic task. Participants with over 90% "no responses" on any of the three task conditions were excluded from analysis, as were individual trials with RTs faster than 100ms. Repeated measures analyses of variance were performed by cannabis use grouping  and emotion  for RT, accuracy, and rated empathy scores during each task.t-tests on significant differences with α = 0.05 for planned group comparisons and Bonferroni correction for post-hoc tests where appropriate. Eta-squared measures of effect size were reported for all within-group factors, while Cohen's d was reported for between-group effects.EEG was re-referenced offline to the common average and baseline corrected to pre-stimulus interval of 200ms. Artifact rejection was applied using the built in artifact rejection tool in SCAN 4.5 EEG acquisition software . Trials Overall differences in behavioral measures were consistent with differences in ERPs in relation to task, and emotion. This suggests that ERP differences were driven by cannabis exposure and not by differences in responses to emotion or task. A detailed description of the results are as follows.In the emotion processing task we examined the effect of cannabis use grouping and emotional expression on participants’ reaction time (RT) in milliseconds and response scores in a repeated measures analysis of variance see  & 3.F = 11.977, p = .001. Emotional expressions did not differ in RT during implicit processing, but they did have a significant effect on RT for explicit and empathic responses. Participants’ responses were the slowest for emotion identification of neutral expressions (angry: t(63) = -8.785, fearful: t(63) = -7.401; all ps < .001), with similar RTs for angry and fearful expressions, t(63) = -1.772, p = .081, and the fastest for happy expressions  = -5.120, angry: t(63) = -11.532, fearful: t(63) = -12.379; all ps < .001). Empathic rating scores also differed by emotion, with slower RTs for neutral than angry expressions, t(63) = -2.814, p = .007, and slowest for happy compared to negative expressions (vs. angry: t(63) = -8.785, fearful: t(63) = -7.401, ps < .001), with a similar trend compared to neutral, t(63) = -2.447, p = .017. RTs for fearful expressions during the empathy task did not differ from neutral, t(63) = -1.751, p = .085, or angry expressions, t(63) = -1.735, p = .088.A significant effect of sex on RT for implicit processing was characterized by slower RT for sex identification of female compared to male faces, ps < .05): emotion identification accuracy scores were lowest for angry faces (vs. happy: t(63) = 6.838, p < .001; neutral: t(63) = 3.209, p = .002; fearful: t(63) = 5.933, p<001), lower for neutral than fearful faces (t(63) = -2.700, p = .009), and no other differences for happy faces  = 1.149, p = .255; fearful: t(63) = -.849, p = .399). Further, a two-way interaction of emotion by sex indicated greater accuracy for male angry faces than female angry faces, t(63) = 2.965, p = .004, d = .27. The empathy task was characterized by lowest rated ability to empathize for neutral expressions (vs. happy: t(63) = -8.997, angry: t(63) = -5.882, fearful: t(63) = -6.640; all ps < .001), higher ratings for happy than angry expressions, t(63) = 3.733, p < .001, and no other differences for fearful expressions (vs. happy: t(63) = 2.193, p = .032; angry: 2.182, p = .033). A main effect of sex on empathy ratings suggested slightly higher rated ability to empathize with female than male faces, independent of emotion, F = 4.106, p = .047. There were no significant effects of emotion or sex on response scores for implicit trials.A main effect of emotion  on response scores was significant for explicit and empathic tasks  = 80.518, p = 001, ηp2 = .542  = 3.498, p = .001, Cohen’s d = -.74, fronto-central, t(68) = 3.937, p < .001, d = -.56, and parieto-occipital sites t(62) = 4.201, p < .001, d = -.93, but not at parietal, t(68) = 2.233, p = .029, d = .20, or centro-parietal sites, t(68) = .955, p = .343, d = .56 (see t(65) = -3.992, p < .001, d = .86) and fearful expressions (t(67) = -3.449, p = .001, d = .76), with a similar trend of smaller P3 for angry expressions (t(68) = 2.546, p = .013, d = .58) but no group differences for neutral expressions, t(68) = 2.347, p = .022, d = .63 (see t(62) = 3.289, p = .002, d = .70, and explicit, t(67) = 3.251, p = .002, d = .72, but not empathic processing, t(68) = 2.046, p = .045, d = .51; while right sites showed no group differences (implicit: t(68) = 1.622, p = .109, d = .40, explicit: t(68) = 2.283, p = .026, d = -.40, empathic: t(68) = 2.192, p = .032, d = .55).Cannabis users presented smaller P3 than controls over frontal,  .56 see . An intet(69) = -3.151, p = .002. In contrast, P3 at fronto-central sites was enhanced for implicit compared to explicit, t(69) = -2.430, p = .018, and empathic processing, t(69) = -2.430, p = .018 (see t(69) = -3.880, p < .001, and fearful, t(69) = -3.142, p = .002, expressions, with no amplitude differences present for angry, t(69) = -.560, p = .577, or happy expressions, t(69) = .328, p = .744.A main effect of task instructions was characterized over centro-parietal sites as a reduction in P3 during implicit compared to explicit processing of facial expressions, .018 see . Fronto-ps < .01): neutral expressions elicited a larger P3 than fearful  = -3.482, p = .001) and angry  = -3.055, fronto-central: t(69) = -3.099, parietal: t(69) = -3.578, parieto-occipital: t(69) = 4.540; all ps < .003), but not happy expressions (happy-angry: t(69) = -.766, p = .446; happy-fearful: t(69) = -.954, p = .343; angry-fearful: t(69) = -.133, p = .895). At fronto-central sites, these effects were modulated by a task by emotion interaction, such that during implicit processing, P3 amplitude for happy and angry expressions was reduced compared to neutral expressions (happy: t(69) = -3.436, p = .001; angry: t(69) = -3.296; p = .002), with a trend for fearful compared to neutral (t(69) = -2.807, p = .006), and no differences between emotional expressions (happy-angry: t(69) = -.128, p = .899; happy-fearful: t(69) = -.694, p = .490; angry-fearful: t(69) = -.758, p = .451). Explicit processing presented a similar reduction only for angry expressions compared to neutral, t(69) = -2.730, p = .008, with smaller P3 for happy and fearful expressions compared to neutral approaching significance (happy: t(69) = -2.552, p = .013; fearful: t(69) = -2.387, p = .020), and no other significant differences (happy-angry: t(69) = .089, p = .899; happy-fearful: t(69) = .524, p = .490; angry-fearful: t(69) = 1.150, p = .451). Effects of emotion on P3 for the empathy task were not significant after correction for multiple comparisons, F = 3.352, p = .023. However, there was a trend suggesting smaller P3 for neutral compared to happy expressions, t(69) = 2.322, p = .023, and also a smaller P3 for happy compared to angry expressions, t(69) = -1.328, p = .008, while no other comparisons approached significance.P3 differences between expressions were observed across all but centro-parietal electrode sites (N = 20) and chronic users , and compared P3 amplitudes between the two groups and controls . In this manner, significant group effects for cannabis use  were further examined using one-way analyses of variance with follow-up t-tests to explore the possible role of cannabis use frequency in this relationship.To expand on the patterns found in P3 amplitude effects of cannabis use, we examined differences within the cannabis users group based on frequency of use. For this purpose, we divided participants in this group into casual users : frontal = 4.645, p = .013, ηp2 = .122; fronto-central = 5.808, p = .005, ηp2 = .148; parieto-occipital = 4.505, p = .015, ηp2 = .119) with no group effects at parietal, F = 2.868, p = .064, ηp2 = .079, or centro-parietal sites, F = .777, p = .464, ηp2 = .023. However, no comparisons between the three frequency use groups reached significance. Next, we compared mean P3 amplitude between groups at parieto-occipital sites for each emotional expression and found significant effects of reduced P3 amplitude for happy and fearful expressions  = 6.121 & 5.416, ps = .004 & .007, ηp2 = .073), but not for neutral, F = 2.887, p = .063, or angry expressions, F = 3.699, p = .030. Specifically, there was a trend for reduced P3 amplitude in both casual and chronic users compared to controls for happy and fearful expressions, with no differences between casual and chronic users and no differences for neutral and angry expressions.For overall P3 amplitudes, group effects remained significant across the same electrode sites  responses were affected by explicit and empathic processing in respect to neutral and negative emotions, with angry and fearful faces giving rise to slower RT responses than happy faces in the explicit processing condition. This effect was reversed in the empathy condition where happy faces gave rise to the slowest RT responses. A similar pattern of response was observed in accuracy ratings with angry faces producing the lowest response scores in explicit and empathic processing. There were however no between group differences with cannabis users presenting the same pattern of responses as non-cannabis users. Unlike previous studies  we saw aSignificant differences in ERPs were observed for group (cannabis users compared to controls), for task , and for emotion . Specifically, the P3 was significantly reduced in mean amplitude in our emotion processing paradigm for those participants that used cannabis compared to participants who did not. Happy expression eliciting the largest P3, followed by fear and angry expressions in users compared to controls. Differences were further modulated by task: in the implicit and empathic task conditions cannabis had a significant decrease in the P3 compared to controls when processing emotional expression. However when attention was directed in the explicit processing task they have similar P3 responses to emotional expression as controls. When comparing levels of cannabis exposure as a possible factor in our cannabis use group it appears that those who use cannabis casually have greater deficits in emotion processing with a reduced P3 response generally for all emotion conditions. This suggests that possibly increased exposure in our chronic group may have developed compensatory mechanisms to the effects on cannabis on emotion processing. However it is important to acknowledge that acute, residual and long term exposure as well as the specific endocannabinoids and other confounding compounds such as alcohol and tobacco were not specifically controlled for a priori in this study.The dissociation between performance on behavioral measures and corresponding P3 amplitude data are interesting. One possible explanation is that exposure to cannabis alters the way in which the brain allocates resources during emotion processing as measured by the P3 component , 19, 20,Our results are consistent with other studies investigating the effects of cannabis on the P3 as a marker for a number of cognitive processes, especially attention based tasks. Cannabis exposure has been shown to give rise to a marked decrease in the P3 component in a number of attentional tasks both visual and auditory , 32, 33.Task driven differences in the P3 were apparent in our data for both cannabis users and control conditions. With a reduction in the P3 for implicit processing compared to explicit and empathic processing in both groups. This is consistent with previous work by Rellecke et al where implicit and explicit processing of emotional expression gave rise to different patterns of ERP . Our datOur implicit, explicit and empathic tasks were sensitive to cannabis exposure, as marked by a decrease in the P3, which is consistent with the literature indicating that visual , 31 and This suggests that consistent with previous research the effects of cannabis may be more closely tied to attention-based emotional processing rather than emotion processing that is occurring when attention is not directed towards a specific emotion , 31, 32.This has implications for structural accounts of emotion processing . ArguablThe P3 complex also appears to be modulated by the type of emotion being processed with negative emotions having a greater effect on P3 than positive and neutral emotions in our cannabis user group, an effect which is task dependent. This is consistent with the literature showing effects of cannabis on negative emotion , howeverAs previously mentioned frequency of use appears to have an emotion specific effect. Although not significant, the reduction in P3 amplitude to fearful, happy and angry emotional expressions, is greatest in causal users compared to chronic users and controls. This is consistent with Ballard and colleagues who found the effects of cannabis use on emotion processing to be exacerbated for negative but not positive emotions . Follow Ability to empathize, measured as a behavioral response, appears consistent across groups for positive emotions. There was an expected reduction in ability to empathize with neutral expressions in all groups compared to positive and negative valence emotional faces. P3 effects did not reach statistical significance however there was a trend towards a greater P3 response for empathic responses to negative emotions compared to positive and neutral emotions in both groups. Further, while cannabis use was associated with differences in P3 during sex and emotion identification, empathic processing of the same facial expressions resulted in a reduction of these group differences. This supports previous discussion of the role of attention on emotion processing differences in the P3 for cannabis users , 31, 32 One significant limitation of our study was not explicitly controlling for the acute, residual or long term effects of cannabis exposure although the majority of our cannabis users patterns of exposure best fit the residual and long term effects definition. Similarly the inability to control the amount and the type of cannabinoid our sample was exposed to makes our conclusions difficult to evaluate. This is both problematic and yet realistic as recreational cannabis users are unlikely to be able to access this information. Testing of recreational and medical cannabis in Colorado dispensaries for consumers is minimal if not nonexistent at this time. For example, THC content can range from 6–12%, to as high as 90%, in concentrates and edibles. We also did not control a priori for exposure to other substances such as alcohol and tobacco use. Although our sample reported minimal exposure to other substances, the residual effects of these substances are well documented to affect cognition, and could be contributing to our results. One particularly interesting point regarding our sample was that they were all using cannabis legally according to Colorado state law. It has become anecdotally apparent that this population tend to restrict their substance exposure to cannabis exclusively. However it should be acknowledged that this is a limitation of our study. As we develop a better understanding of the legal recreational cannabis industry we expect to be able to better address some of these questions.Our data show a significant effect of cannabis use on the P3 in an emotion processing task which was further modulated by task instruction. The P3 amplitude was reduced for negative emotions in our cannabis user group, compared to controls, and this effect was greatest when processing emotional expressions implicitly. There was a trend towards this being a dose dependent relationship with those users self-reporting the greatest exposure to cannabis having larger decrements in P3 amplitude. Attention driven demands on emotion processing appear to be affected by cannabis use as reflected in differences in the P3 amplitude.The Recreational Cannabis Use Evaluation (R-CUE)Cannabis Use Questionnaire:1Age: _________2Are you part of Colorado’s Medical Marijuana Registry (do you own a red card)?aYesbNo3If you answered yes to #2, how many years have you been a member of the registry?aLess than one year (This is my first red card)b1–2 yearsc3–4 yearsd5–7 yearse8–10 yearsf10+ years4How many years have you partaken in Cannabis use?aLess than a yearb1–2 yearsc3–5 yearsd6–10 yearse11–15 yearsf16–20 yearsg20+ years5How many times a week do you use Cannabis (in any form)?aOnce a week: ______bA couple of times a week: ______cA few (3–6) times a week:______dDaily: ______e2–4 times a day:_____fMore than 4 times a day:______6Which of the following ways do you like to intake cannabis, and which types of Cannabis do you prefer? Check all that apply (and check subcategories to the best of your knowledge/ability):aSmoking Cannabis flower : ______iIndicas (“Body high”): _____iiSativas (“Mind high”): _____iiiHybrids: ______1Sativa dominant hybrids: ______2Indica dominant hybrids: ______3True hybrids (50/50 of each): ______bSmoking Cannabis Concentrates (Hashish/”Dabs”) ________iType of cannabis in concentrate:1Indica: _______2Sativa: _______3Hybrids: ______aSativa dominant hybrids: ______bIndica dominant hybrids: ______cTrue hybrids (50% of each): ______4Strain specific: _______aIf yes to strain specific hash, list strains that you have used: ________________________________________________________________________________________________________________________________________________iiMethod of THC extraction (the type of Concentrate) Check all that apply:1Solvent based extraction:aButane Honey Oil (BHO): ____bCarbon Dioxide (CO2): ______cQuick Wash Isopropyl Alcohol (QWISO): _____dHexane solvent concentrates: _____ePropane solvent concentrates: ____fEthanol solvent concentrates: _____g“Shatter” hash (High purity butane/ethanol extraction): _____2Solvent-less concentrates:aCold Water Extraction (CWE)/Icewax/Solvent-less wax/”grease”/”jewce”: _____bBubble hash: _____cScreen filtered hash (Finger hash/Keif): _____cCannabis Edibles: _______iBaked Edibles: _______iiHard Candy/ Gummy Edibles: ______iiiChocolate Edibles: ____ivDrink based edibles : ______vTinctures: _____1Glycerin based: _____2Ethanol based: _____viCannabis butter (Cannabutter): _____viiOther (Please describe): ____________________________________________________________________________________________________________________________________________________________________________________dDermal Cannabis Application: ______iCannabis skin patches: _____iiCannabis lotions/balms/oils: ______7If you selected any of the flower/concentrate methods of cannabis intake, what smoking devices do you use? aWater-filtration devices:iBong (upright/waterpipe):_____iiBong (gravity):_____iiiBubbler: _____bDry smoking devices:iPipe : ______iiSteamroller: ______iiiJoint: ______ivBlunt:______cVaporizers:iBag vaporizers: ______iiWhip vaporizers: ______iiiPortable/Pen vaporizers:________dDabs:iSpoon dabs:_____iiNail dabs: ______iiiNoodle dabs: ______ivHealth stone dabs: _____vSkillet dabs: _______8In order of preference , what is your preferred form of consuming Cannabis: Cannabis flower/nugget, concentrates/hash, edibles, and topical absorption?1____________________2____________________3____________________4____________________9In order of preference , which is you preferred method(s) of smoking/ingesting Cannabis?1_____________________2_____________________3_____________________4_____________________5_____________________6_____________________7_____________________8_____________________9_____________________10_____________________10In an average month, how much in Cannabis flower/nugget do you smoke?aNone:_____bA gram or less: _______cAn eighth of an ounce (3.5 grams) or less:_____dA quarter of an ounce (7 grams) or less:_____eA half of an ounce (14 grams) or less:_____fAn ounce (28 grams) or less:_____gMore than an ounce: _____hMore than two ounces:_______iMore than a quarter pound (4 ounces):______11In an average month, how much in Cannabis concentrates do you smoke?aNone:______bA gram or less: _______cAn eighth of an ounce (3.5 grams) or less:_____dA quarter of an ounce (7 grams) or less:_____eA half of an ounce (14 grams) or less:_____fAn ounce (28 grams) or less:_____gMore than an ounce: _____12In an average month, how many Cannabis edibles do you consume? .aNone:____bOne edible:____c2–4 edibles:______d4–8 edibles:______e10–20 edibles:______f30+ edibles:________S1 Fig(TIF)Click here for additional data file.


Listeria monocytogenes is responsible for the rare disease listeriosis, which is associated with the consumption of contaminated food products. We report here the complete genome sequences of vB_LmoS_188 and vB_LmoS_293, phages isolated from environmental sources and that have host specificity for L. monocytogenes strains of the 4b and 4e serotypes. Listeria monocytogenes is a Gram-positive facultative anaerobe and the causative agent of listeriosis, a disease associated with the consumption of contaminated food products. Its psychotropic nature, coupled with its ability to persist in the environment  and wild mushroom (vB_LmoS_188) samples. The genomes were sequenced by MWG Eurofins  on an Illumina MiSeq next-generation sequencing (NGS) system to >100× coverage. For each, sequencing yielded approximately 3 million reads, with an average length of 148 bp and an average quality score of 37. The removal of low-quality reads was undertaken using Trimmomatic (http://www.ebi.ac.uk/interpro).Two vB_LmoS_18 samplesvB_LmoS_23 and wilmmomatic  and Artemmomatic , while fListeria bacteriophages , while bacteriophage vB_LmoS_293 is 40,759 bp in length . PCR analyses confirmed that both genomes contain linear circularly permuted double-stranded DNA (dsDNA) with terminal redundancy. Sixty ORFs were detected in vB_LmoS_188, while 72 ORFs were detected in vB_LmoS_293. The ORFs predominantly begin with the ATG start codon (91.6% in vB_LmoS_188 and 87.5% in vB_LmoS_293). No tRNAs were detected. No function was assigned to 34/60 ORFs detected in vB_LmoS_188 or for 41/72 ORFs in vB_LmoS_293. The genomes are ordered in a modular fashion, consistent with previous observations for iophages . DespiteLP-030-3 . Their sKP399677 (vB_LmoS_188) and KP399678 (vB_LmoS_293).The genome sequences of these two phages have been deposited in GenBank under the accession numbers


Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks. This manuscript puts forward two innovations. Firstly, we demonstrate a fast, likelihood motivated and straightforward method for applying independent components analysis (ICA). Secondly, we propose a parcellation based adjustment when the source signals distribute differently across regions. Our work is routed in the context of understanding human brain networks, and we use functional magnetic resonance imaging (fMRI) data for illustration in this manuscript.We approach our study of fMRI by simultaneously analyzing all voxels. This is in contrast to regional or seed-based approaches  is a factor-analytic approach that has been frequently utilized for the analysis of functional neuroimaging data, because of its success in discovering important brain networks in many applications , while Section 5 gives a discussion.The remainder of the paper is organized as follows. Section 2 describes the p-spline based ICA algorithm and considers relaxation of the Xi be a T × V matrix for subject i = 1, …, I. In the context of fMRI, T indicates scans while V indicates voxels. Assume the number of ICs is Q. The ICA model specifies Xi = AiS, where Ai is a T × Q mixing matrix and S is a Q × V matrix of ICs. By assuming common spatial maps across subjects, we can stack the individual matrices in the temporal domain. Let TI × V group data matrix, and TI × Q group mixing matrix. Spatial group ICA simply specifies the standard modelIndependent component analysis models collected signals as linear weighted combinations of independent sources. Notationally, let X is element  of X and define X as row t of X and X as column v. Then, model (1) could be rewritten as We use parentheses to index matrices so that E[X] = μx = 0 and hence E[S] = μs = 0. If this assumption were not made, the ICA model would imply X − μx = A(S − μS), which is exactly an ICA model with a centered data matrix and the ICs having mean 0. Hence, X is demeaned prior to analyses and μS is assumed to be zero. Similarly, since AS = {A∕c} * {cS}, ICs are only identified up to scalar multiplication. Thus, we assume that Var{S} = 1 for q = 1, …, Q and v = 1, …, V.We assume that S ⫫ S when q ≠ q′, where ⫫ implies statistical independence. However, standard variations of ICA also assumes that i.i.d collection, which we also adopt for now. The i.i.d assumption will be relaxed later in the next subsection. As a consequence of these assumptions, X ⫫ X when v ≠ v′; yet note that X is not (necessarily) independent of X.ICA gets its name by assuming that Q < TI and Equation (1) is overdetermined. A two-stage dimension reduction is often performed to reduce the computational load and avoid overfitting  for v = 1, …, V, and f = , then standard multivariate random variable transformation results imply that the joint density of X isICA estimates n et al. , if fq iv = 1, …, V istherefore the joint log-likelihood including all contributions for fq and B simultaneously. Instead, an iterative optimization is often performed. Specifically, given the current estimate of B at iteration k, say S via Ŝk), density estimation techniques can be used to obtain k.It is generally not possible to solve the joint likelihood for the parameters in ck) < ck) < … < ck) be equidistant histogram cutpoints, where ck) = −ϵ + min Ŝk) and ck) = ϵ + max Ŝk). The number ϵ is added to avoid numerical boundary effects. Let j = 1, …, J, be the count of values between cutpoints j − 1 and j for row q of Ŝk), ck)] by mk) for j = 1, …, J. We obtain a density estimate via the log-linear model nk) ~ Poisson{λk)}, where Dk) is a vector of coefficients. To avoid overfitting the B-spline model, and to avoid sensitivity to the degrees of freedom, we choose a large value for the degrees of freedom and put a squared penalty on the coefficients. Let μk) denote the expectation of nk), then the penalized log likelihood takes the form  = β − 2β + β. The resulting model is then a generalized linear mixed model on the counts. The B-spline basis is evaluated at the midpoint of the cutpoint interval. However, via interpolation, the smoother gives an estimate for all values, thus yielding a continuous function, say where δ is a parameter controlling the smoothness of the fit, Δ denotes the difference operator, ΔUsing generalized linear mixed models to penalize smoothing has become standard practice and is well described in Ruppert et al. . HistogrB) are available in closed form, making gradient- and Hessian-based optimization algorithms easy to implement. This is useful for the stage of the algorithm for obtaining the next iterate of B. Accordingly, we use a Newton-Raphson method to update the mixing matrix. Specifically, let kth iteration, we update B byFurthermore, due to the convenient differentiation properties of B-spline bases and the simple exponential (Poisson) model, the first and second derivatives of B should satisfy the condition that the underlying ICs are the same for all subjects. Following Eloyan et al. ((Bk+1)−1. We use the Amari metric between Bk+1)( and Bk) assume that n et al.  considerI = 18 ROIs for the whole brain. We assume the signals are i.i.d within region but could be differently distributed across region. Under this assumption, the density function fq can be written as the sum of the region-specific density function, that is,Specifically, we propose to account for the difference in the activity across the brain by allowing different density distribution in different regions. To this end, we adopt the functional parcellation of the brain activity map proposed by Yeo et al. . The parRi denotes the ith ROI, fiq is the density function on Ri. Thus, fiq takes positive values on the ith region and zero elsewhere. The density estimate of fiq can be obtained using the same procedure as proposed in Section 2.2, confined to the ith region. The estimate for fq can be constructed by taking the sum of where The proposed ICA algorithm can be summarized as follows:_______________________________________________________________________________________________________B.Choose an initial value of for the mixing matrix B using the Amari metric.S = BX.Let q, calculate the density function fiq(s) on the ith ROI, i = 1, 2, …, I, using the p-spline based density estimation algorithm.For each IC Get B using the Newton-Raphson method, see Equation (2).Update the mixing matrix Alternate until convergence of _______________________________________________________________________________________________________fq1 = fq2 = … = fIq, the above algorithm reduces to the algorithm proposed in Section 2.2 assuming i.i.d signals across the entire brain.Note that, in the special case that We conduct simulation studies to evaluate the performance of the proposed ICA algorithm. We consider four settings where data are generated using different distributions. We compare the results of the proposed algorithm with fastICA  perform much faster than the likelihood-based algorithms .  ~ Weibull, S ~ Gamma, and S ~ Gamma, respectively. Standard Gaussian noises are added to the generated ICs. The mixing matrix is given byIn the first set of simulation studies, we assume there are Figure Q = 2, and we generate the signals based on parcellation. Specifically, we partition the real line into 10 intervals, with cutoffs at the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, and 90th percentiles of the normal distribution. For the first IC, the density function is uniformly distributed within each interval, but the overall shape is approximately normal. For the second IC, the density function follows Laplace distribution within each interval, and the overall shape is approximately normal. The mixing matrix is given byIn the second setting, we assume the number of source signals The boxplots of the spatial correlation and the Amari errors based on 200 replications are summarized in Figure I = 3. The source signals are the same as those in the second setting, and the mixing matrices for the three subjects are given byIn the third setting, we generate multi-subject data with number of subject The simulation results are summarized in Figure In the fourth setting, we generate the ICs and mixing matrices by mimicking signals from real fMRI data. Specifically, we run fastICA on 10 subjects from the NITRC 1000 Connectome dataset to get twenty ICs (networks). Three of the twenty networks are chosen as the true signals, and they are shown in Figure The results indicate that our proposed p-spline based ICA algorithm is successful in recovering signals from real fMRI data.https://www.nitrc.org/projects/fcon_1000/).We apply our proposed algorithm to the 1000 Functional Connectomes Project dataset, which consists of thousands of resting state scans combined across multiple sites with the goal of facilitating discovery and analysis of brain networks , and rerun the ICA algorithm on the dimension reduced dataset. We set R = 15, 20, 30 and Q = 15, 20, 30, respectively. Similarly as in Li et al. , auditory network (0.73), DMN (0.86) and control network (0.84). In addition, the correlations for the major brain networks using R = 20, Q = 30, and R = 20, Q = 20 are as follows: visual network (0.78), auditory network (0.61), DMN (0.88) and control network (0.69). In summary, we find that, although the estimation results depend on the number of components, the major networks appear to be robust against the choices of number of components.As suggested by an anonymous reviewer, we investigate the impact of the dimension of the reduced space on the final results. Specifically, we select different values of i et al. , we findIndependent component analysis is a factor-analytic approach that is commonly used in analyzing fMRI data. In this manuscript, we present a novel and simple ICA algorithm that is fast, likelihood based and straightforward to program. The algorithm is nonparametric, data-driven, and is blind to the particular distribution of the underlying signals. As a byproduct of the algorithm, we obtain the likelihood function of the ICA model which can be used for further statistical inference. It should be noted that, the likelihood function in our algorithm is a profile likelihood, since we are mainly interested in the mixing matrix estimates and the parameters over the spline basis are nuisance parameters. Indeed, one could also study the coefficients on the spline basis in a full likelihood, but this is not the goal of this manuscript, hence the variance of the estimator of the mixing matrix depends on the variance of the nuisance parameters.The proposed algorithm is extended to allow for region specific IC density functions, on the rationale that most signals of interest are reasonably confined to a subset of the entire anatomical brain space (Guo and Pagnoni, Simulation studies show that our proposed algorithm works well in both the simple and complex situations, and it substantially outperforms the existing ICA algorithms when the identically distributed assumption of the source signals is violated. In applying the proposed algorithm to the fMRI data, we choose to account for the difference in brain activities across regions by using the brain parcellation proposed by Yeo et al. . Our datThere are a few directions for future research. Firstly, the test-retest reliability of the intrinsic brain networks is an important issue and has been studied extensively in recent years. For example, Zuo et al.  found thThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A combinatorial synthetic approach is described for the isolation of quaternary cocrystals. The strategy outlines chemical and geometrical modulations in the long-range synthon Aufbau modules (LSAMs) to systematically increase the number of components. A synthetic strategy is outlined whereby a binary cocrystal may be developed in turn into a ternary and finally into a quaternary cocrystal. The strategy hinges on the concept of the long-range synthon Aufbau module (LSAM) which is a large supramolecular synthon containing more than one type of intermolecular interaction. Modulation of these interactions may be possible with the use of additional molecular components so that higher level cocrystals are produced. We report six quaternary cocrystals here. All are obtained as nearly exclusive crystallization products when four appropriate solid compounds are taken together in solution for crystallization. This array is made up of closed TMP·ORC tetramers  consists of discrete synthon B modules that are laterally offset with respect to one another so that there are no C—H⋯π interactions. Fig. 2et al., 2011This article argues for the synthetic design of complex quaternary solids by systematic selection and fabrication from LSAMs in binary and ternary cocrystals. Fig. 1A , 1,10-phenanthroline (PHEN), 2,2-bisthiophene (22TP), hexamethylbenzene (HMB) and pyrene (PYR) results in stoichiometric ternary cocrystals. The structures of three of them  are along predicted lines and they may be considered as being obtained by substitution of the ‘free’ TMP molecule in Form I of the ORC·TMP binary with the new aromatic compound Fig. 3. The othA Fig. 4 and the B is constructed exclusively with PHE and the TMP molecules provide cross links via the third ‘hook’ hydroxy group of the PGL molecule. From MeCN also but under different conditions, we obtained the 2:1:2 Form II which is reminiscent of the ORC·TMP·PHE ternary except that PHE is in the ‘inner’ part of synthon A rather than in the ‘outer’ part. There was no contamination of either of these ternaries by the ‘other’ ternary in the crystallization experiments. A priori, it would not be possible to predict which structure one would obtain from MeCN under what conditions. What is important, however, is that there are a number of topologically similar crystal structures available to the system. Which one is actually obtained would seem to depend on the exact experimental conditions used. The system lends itself to high throughput methods. We maintain that we carried out a very large number of crystallization experiments on an entire array of compounds and solvent systems in a combinatorial manner. It is of interest to note that such examples of (pseudo)polymorphism are very rare in three component systems . Let us consider these structures in turn. The 2:1:2:1 ORC·TMP·PHE·HMB structure follows smoothly from the ternary 2:1:2 ORC·TMP·PHE in a chemically reasonable manner. In the ternary, one observes π⋯π stacking between electron-deficient PHE molecules . In the quaternary, HMB inserts in a classical donor–acceptor fashion (∼ 3.56 Å). Replacement of the electron-rich HMB by PYR and the ditopic PHE by ACR achieves the same result and one obtains the stoichiometric quaternaries 2:1:2:1 ORC·TMP·PHE·PYR and 2:1:2:1 ORC·TMP·ACR·PYR (see section S3). Coming next to PGL, it is not difficult to understand the crystal structure of the quaternary 2:1:1:1 PGL·TMP.PHE.DPE wherein an infinite synthon A based structure with two ditopic heterocycles is cross linked with DPE. The quaternaries 2:2:1:1 PGL.TMP.PHE.ANT and 2:2:1:1 PGL.TMP.PHE.PYR have very similar structures. Synthon A is constructed with TMP in the ‘outer’ locations and PHE in the ‘inner’ location. ANT and PYR intercalate with C—H⋯π interactions to give a columnar LSAM.In practice, a total of six quaternaries were obtained, three each from ORC and PGL  may be found in a ternary cocrystal between say, A, B and C. Similarly, a synthon that is virtual in a three-component system may be seen in a four-component cocrystal. This also implies that proof correction mechanisms exist in the crystallizations, perhaps leading to the specificity of outcome.The results obtained in this work validate the idea of using a supramolecular combinatorial library in the isolation of stoichiometric three- and four-component molecular crystals. We have earlier used this concept to make a single ternary cocrystal  global, BS_ORC_TMP, BS_ORC_TMP_Hydrate, BS_PGL_TMP.cif, QS_ORC_TMP_ACR_PYR, QS_ORC_TMP_PHE_HMB, QS_ORC_TMP_PHE_PYR, QS_PGL_TMP_PHE_ANT, QS_PGL_TMP_PHE_DPE, QS_PGL_TMP_PHE_PYR_hydrate, TS_ORC_TMP_22TP, TS_ORC_TMP_ACR, TS_ORC_TMP_HMB, TS_ORC_TMP_PHE, TS_ORC_TMP_PHEN, TS_ORC_TMP_PYR, TS_PGL_TMP_DPE, TS_PGL_TMP_PHE_Form_I, TS_PGL_TMP_PHE_Form_II, TS_PGL_TMP_PYR. DOI: Click here for additional data file.10.1107/S2052252515023957/hi5641sup3.zipCIF files. DOI: 10.1107/S2052252515023957/hi5641sup2.pdfSupporting information. DOI: 1428091, 1428092, 1428093, 1428106, 1428104, 1428105, 1428107, 1428090, 1428108, 1428099, 1428095, 1428098, 1428094, 1428096, 1428097, 1428103, 1428100, 1428101, 1428102CCDC references:


Supporting Information File Rodrigues_etal_Supporting Information S1 with Track Changes.doc should not appear with the published article. Please view the correct Rodrigues_etal_Supporting Information S1 here.S1 FileSupporting Materials and Methods.(DOC)Click here for additional data file.