import speech_recognition as sr
from googletrans import Translator
from gtts import gTTS
import os

The Kumbh Mela is one of the largest religious gatherings in the world, held in India. It is a major Hindu pilgrimage and festival where crores of devotees gather to bathe in sacred rivers, believing it will cleanse them of sins and lead to salvation.

The festival takes place every 12 years, rotating among four locations:

Prayagraj .

Haridwar .

Nashik .

Ujjain .

Purna Kumbh Mela: Held every 12 years at one of the four locations.

Ardh Kumbh Mela: Held every 6 years at Haridwar and Prayagraj.

Maha Kumbh Mela: Held every 144 years (12 Purna Kumbhs) at Prayagraj.

40 crore people are expected to visit over the period of 45 days.

and the gov is spending 7000crores and is expected to earn 25000crores from hosting mahakhumb mela

Kumbh Mela attracts not only pilgrims but also tourists and photographers from across the globe, making it a significant event culturally, spiritually, and economically.

It is mythologically believed that churning of the ocean (Samudra Manthan) by gods and demons. During this event, drops of the nectar of immortality fell at the four locations.

try:
    package = "zarr"
    package_version = version(package)
    major_version = int(package_version.split(".")[0])  # Extract the major version
    if major_version == 3:
        print(f"The package major version is {major_version}.")
        import zarr
        import fsspec
        # strip leading 's3://' from url
        url1 = url1[5:]
        url2 = url2[5:]
        fs = fsspec.filesystem("s3", asynchronous=True)
        store1 = zarr.storage.FsspecStore(fs, path=url1)
        store2 = zarr.storage.FsspecStore(fs, path=url2)
        file1 = zarr.open(store=store1)
        file2 = zarr.open(store=store2)
    else:
        print(f"The package major version is {major_version}.")
        import s3fs
        fs = s3fs.S3FileSystem(anon=True)
        file1 = s3fs.S3Map(url1, s3=fs)
        file2 = s3fs.S3Map(url2, s3=fs)
        
except PackageNotFoundError:
    print(f"{package} is not installed")

sudo apt-get install multiverse -y || true

sudo apt-get update

sudo apt-get install ubuntu-restricted-extras

/* HUD CROSSHAIR */
.hud-crosshair{
    visibility: clear;
    position: fixed;
    width: 25px;
    height: 25px;
    left: 50%;
    top: 50%;
    margin-left: -13px;
    margin-top: -13px;
    z-index: 12;
}

.hud-crosshair div{
    background: #00ffb3;
    position: absolute;

}

.hud-crosshair-1, .hud-crosshair-2{
    width: 2px;
    height:10px;
    left: 12px;
}

.hud-crosshair-3, .hud-crosshair-4{
    width: 10px;
    height:2px;
    top: 12px;

# DEA between BC1 BC6 ####
# DEA of each community between BC1 and BC6 to see differences in gene expression
# i look particularly at immune communities 5-7 (defined at resolution 0.25)

# ensure assay is integrated 
DefaultAssay(so_donorint) <- "integrated"

# combine cluster number and condition info into new variable 'celltype.condition'
# then set it as identity class of the cells 
# consider - how cell clusters behave under different conditions
so_donorint$celltype.condition <- paste(so_donorint$seurat_clusters, so_donorint$Conditions, sep="_")
Idents(so_donorint) <- "celltype.condition"

# run DEA for 2 sets of cells BC1, BC6 - compare expression profiles 
uro_BC1vsBC6 <- FindMarkers(so_donorint, ident.1 = "3_BC1", ident.2 = "3_BC6", test.use = "bimod")

# check output 
head(uro_BC1vsBC6)

# prepare data needed for volcano plot, as previous 
uro_BC1vsBC6["p_val_adj"] <- lapply(uro_BC1vsBC6["p_val_adj"], pmax, 1e-300)
uro_BC1vsBC6$DEA <- "NO"
uro_BC1vsBC6$DEA[uro_BC1vsBC6$avg_log2FC > 1 & uro_BC1vsBC6$p_val_adj < 0.05] <- "UP"
uro_BC1vsBC6$DEA[uro_BC1vsBC6$avg_log2FC < -1 & uro_BC1vsBC6$p_val_adj < 0.05] <- "DOWN"
uro_BC1vsBC6$genes <- rownames(uro_BC1vsBC6)

# Volcano plot to visualize DEA results to compare between BC1 and BC6
ggplot(uro_BC1vsBC6, aes(x=avg_log2FC, y=-log10(p_val_adj))) + 
  geom_point(aes(colour = DEA), show.legend = FALSE) + 
  scale_colour_manual(values = c("blue", "gray", "red")) +
  geom_hline(yintercept = -log10(0.05), linetype = "dotted") + 
  geom_vline(xintercept = c(-1,1), linetype = "dotted") + 
  theme_bw() + 
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), plot.title = element_text(hjust = 0.5)) +
  geom_text_repel(data=subset(uro_BC1vsBC6, (abs(avg_log2FC) > 7.3 & p_val_adj < 0.05)| (avg_log2FC < -3.5 & p_val_adj < 0.05)), aes(x=avg_log2FC, y=-log10(p_val_adj), label=genes), max.overlaps = 1000, size=4.5) +
  ggtitle("DEA of community 3 between BC1 and BC6 (BC1 in red)") 

 ggsave("plots5/BC1vsBC6_3.png")
# ident.1 = BC1 community 0 shown red // ident.2 = BC6 community 0 shown blue

# Extract upregulated genes (log fold change > 3, p-value < 0.05 )
upreg_0 <- uro_BC1vsBC6[uro_BC1vsBC6$avg_log2FC > 7 & uro_BC1vsBC6$p_val_adj < 0.05, "genes"]

# Extract downregulated genes (log fold change > 4, p-value < 0.05 )
downreg_0 <- uro_BC1vsBC6[uro_BC1vsBC6$avg_log2FC < -3 & uro_BC1vsBC6$p_val_adj < 0.05, "genes"]

print(upreg_0) 
print(downreg_0)

# for ident.1 = 0_BC1 shown red, genes expressed are ranked in descending:
# majority for cell signalling and transcription regulation 
# structural proteins and cytoskeleton
# metabolism and detoxification
# immune response and inflammation
# cellular transport and membrane proteins
# MANY non-coding RNAs, uncharacterized genes, miscellaneous functions
# may be linked to age/ health factors again

# for ident.2 = 0_BC6 shown blue, genes expressed are ranked in descending:
# most significant gene PLD4 (immune response)
# extracellular matrix and structural proteins (8/16 genes)
# cell signalling and regulation (4/16 genes)
# membrane transport and transporters (2/16 genes)
# cell cycle and division (1/16)
# synaptic function and signalling (1/16 genes)
# unknown or uncharacterized (2/16 genes)

# create pi values
# add new column to data frame 'uroG1_T1vsT3' where each gene is assigned a pi score (calc below)
# pi score allows me to rank genes - based on measure of log2fold change + stats significance, can then use to identify top genes for further analysis 
uro_BC1vsBC6 <- mutate(uro_BC1vsBC6, pi = (avg_log2FC * (-1 * log10(p_val_adj))))

# check data set - look at the top hit genes !!
head(uro_BC1vsBC6)

# select top 20 genes (for Ta6) based on pi score in descending order - fr highest pi scores
# these genes are most significant and differentially expressed when comparing between Ta1, Ta6 for G1 urothelial cluster
uro_BC1vsBC6 %>% arrange(desc(pi)) %>% slice(1:20) 

# select top 20 genes (for Ta1) based on pi score in ascending order - fr low pi score
# these genes are either of smaller fold changes or less stats significant p-values 
uro_BC1vsBC6 %>% arrange(pi) %>% slice(1:20) 

# prep for GSEA 
# extract 'genes', 'pi' from data frame and convert 'prerank' to numeric vector 
prerank <- uro_BC1vsBC6[c("genes", "pi")]
prerank <- setNames(prerank$pi, prerank$genes)
str(prerank)

# run GSEA and store results in fgseaRes
genesets = gmtPathways("data5/c2.cp.v2024.1.Hs.symbols.gmt")
fgseaRes <- fgsea(pathways = genesets, stats = prerank, minSize=15, maxSize=300, eps=0)

# filter results from GSEA stored in fgseaRes
# checking top hits for either side of volcano plot 
fgseaRes %>% arrange(padj) %>% filter(NES<0) %>% slice(1:20)
fgseaRes %>% arrange(padj) %>% filter(NES>0) %>% slice(1:20)

# add bar plot to show the differences following GSEA

# Barplot of top GSEA results
fgseaRes %>%
  arrange(desc(NES)) %>%
  slice(1:20) %>%
  ggplot(aes(x = reorder(pathway, NES), y = NES, fill = NES)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top 20 Pathways by NES, BC1vsBC6_3 ",
       x = "Pathway",
       y = "Normalized Enrichment Score (NES)") +
  theme_minimal()

ggsave("plots5/BC1vsBC6_3_GSEA.png", bg = "white")

# 4 Find clusters of cell types ####

# Data quality is sorted following QC (section 2) and integration (section 3) - data is single analysis object!

# I can now look at biology - try identify communities of cells alike. genes can work in pathways so not equally informative
# use PCA to reduce dimension and identify patterns of shared variance then derive (potential) communities 
# also work out how many clusters are (potentially!) informative 

# run PCA on integrated data set 
# recap PCA reduces dimensions in data, only capture max variance in data so easy to visualize
# in context of scRNAseq, I'm using PCA to get main sources of variation in gene expression
so_donorint <- RunPCA(so_donorint)

# top 5 PCs shown, biggest contributor in each direction are : 
# gene info obtained via uniprot
# PC1 positive = HLA-DRB6 - antigen presenting to CD4+ T cells, immune response
# PC1 negative = ADIRF - regulates adipogenesis and has a role in fat metabolism 
# PC2 positive = TXNIP - negative regulator of thioredoxin, tumour suppressor gene 
# PC2 negative = MT-CO1 - component of cytochrome c oxidase for respiration

# pick the most informative PC
# elbow plot to show me percentage variance explained by each PC
ElbowPlot(so_donorint)

# As number of PC increases, the variance explained by each subsequent PC decrease more gradually
# The "elbow" is the point where the curve flattens out - stop considering additional PC as they contribute little to explaining data's variance.

# after running PCA to assess how much variance in each PC
# calc % variance explained by each PC and cumulative % variance across all PCs 
# helps me determine number of PCs to keep for downstream analysis
pct <- so_donorint[["pca"]]@stdev / sum(so_donorint[["pca"]]@stdev) * 100
cumu <- cumsum(pct)

# identify point/PC where cumulative variance > 90%, % variance explained < 5% (does not contribute much to variance)
co1 <- which(cumu > 90 & pct < 5)[1]
co1 # 43

# identify point/PC where difference in % variance explained (contribution to variance) between 2 consecutive PCs is > 0.1%
# I can find the point of significant drop in contribution of variance explained by each PC
co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1]
co2 # 12

# select minimum value between the 2 measures (co1, co2)
# provides criteria to decide when to stop including additional PCs
# co1 - point where a PC has cumulative variance > 90%, variance explained < 5%
# co2 - point where there's significant drop in variance explained between 2 consecutive PCs
pcs <- min(co1, co2)
pcs # 12

# Here is comparison of point at 90% variance explained and where PCAs stops being informative
# create data frame containing 3 variables
# pct - % variance contributed by each PC
# cumu - cumulative % variance contributed by PCs 
# rank - ranking of each PC showing its order in analysis
cumu_df <- data.frame(pct = pct, cumu = cumu, rank = 1:length(pct))

# Elbow plot to visualize relation between cumulative variance explained, % variance contribution by each PC
ggplot(cumu_df, aes(cumu, pct, label = rank, color = rank > pcs)) + 
  geom_text() +
  theme_bw()

# clean up - remove objects unnecessary for downstream analysis 
rm(pct, cumu, cumu_df, co1, co2)

# PCA-based scatter plot to visualize 4 selected features/genes - check if PCA successful
# cells expressing these markers are shaded from dark blue (most expression) to pale pink (least/none)
FeaturePlot(so_donorint, reduction = "pca", dims = c(1, 2), 
            features = c("HLA-DRB6", "ADIRF", "TXNIP", "MT-CO1"), cols = c("#fcd5ce", "#4886af"), 
            pt.size = 2.5, alpha = 0.7, order = TRUE,)

ggsave("plots5/6_featureplot.png")

# look at first 2 PCs
# gene 'MT-CO1' shown to have greatest expression across PC1 PC2, closely followed by gene 'ADIRF'
# MT-CO1 involved in respiration and could be linked to providing energy required for proliferation
# since BC1 BC6 = non-malignant, proliferation of the communities highly expressing MT-CO1 should be a contributor to non-malignancy
# but need to perform more analysis before making any conclusions
# gene 'HLA-DRB6' have least expression across PC1 PC2 

# Neighbor analysis - to find relationships between cells (calc neighborhoods) based on first PC
DefaultAssay(so_donorint) <- "integrated"
so_donorint <- FindNeighbors(so_donorint, dims = 1:pcs)

# problems with neighbor analysis - finds potential communities but does not give reference of how many communities should be present
# community detection starts with all cells in 1 community - followed by successive subdivisions
# using cluster tree = can visualize how communities derived + subdivided 

# so, perform clustering analysis across multiple resolution values
# then visualize resulting clusters by cluster tree plot 
so_donorint_orgs <- FindClusters(so_donorint, resolution = seq(0, 0.3, 0.05))
clustree(so_donorint_orgs, show_axis = TRUE) + theme(legend.position = "none") + ylab("Resolution")

ggsave("plots5/7_clustertree_lineage.png")

# cluster tree plot shows successive community divisions as resolution increases
# issue is - most numerous type often results in most subgroups. Purely due to its high abundance and not biologically different
# I will therefore start my analysis at low resolution to monitor closely the main community lineages

# OK before that clear up first
rm(so_donorint_orgs)

# set initial resolution to 0.05 for clustering cells - I should see 3 communities 
res <- 0.05

# now assign cells to a cluster and run UMAP - reduce dimensions so I can visualize
# UMAP allows me to visualize clusters based on first PCA 
# 2D representation of cells to visualize similarities/differences based on high dimensional features (i.e gene expression) - based on first PCA
so_donorint <- FindClusters(so_donorint, resolution = res)
so_donorint <- RunUMAP(so_donorint, dims=1:pcs, return.model = TRUE, verbose = FALSE)

# UMAP plot - Visualization of my clustered data 
DimPlot(so_donorint, reduction = "umap", label = TRUE, label.size = 6)

# based on cluster tree plot I should see 3 communities 
# however population 1 and 2 each shown as 2 separate groups suggesting further subdivision
# I will test with higher resolution later in this analysis to see subdivisions for different cell types (see section 6)

# save plot first
ggsave("plots5/8_UMAP_res0.05.png")

# split UMAP plot - based on 'conditions' + add labels 
# shows how clusters are distributed across conditions BC1 BC6
DimPlot(so_donorint, reduction = "umap", split.by = "Conditions", label = TRUE, label.size = 6)

ggsave("plots5/9_splitUMAP_res0.05.png")

# samples are identical - same grade, same non-malignant NMIBC tumour pattern should be the same
# differences are observed and could be due to person differences. Samples are from individuals of different ages, and possibly very different lifestyles, health status
# genetic differences, mutations can also contribute to these differences
# community 0 from BC1 lacks a great number of cells in comparison to BC6
# community 2 is suggested to further subdivide and I will look at a higher resolution later (see section 6)
# community 1 is also suggested to further subdivide, and BC6 is shown to have less cells here compared to BC1
# please see section 6 - Vary resolution

# % distribution of each cell cluster across different conditions 
round(prop.table(table(Idents(so_donorint), so_donorint$Conditions))*100,3)
# community 0 = 22.6% (BC1) 52.3% (BC6) differs most significantly



############################################################

# 5 Annotate clusters ####

# set default assay to 'RNA' and normalize gene expression data in 'RNA'
DefaultAssay(so_donorint) <- "RNA"
so_donorint_dp_norm <- NormalizeData(so_donorint, verbose = TRUE)

# now define list of genes for biological markers - likely communities in urothelium
# likely urothelial markers + others 
features <- c(
              "TERT",                               # telomerase
              "UPK1A",                              # marker for urothelial differentiation
              
              "UPK2", "EPCAM", "KRT18", "KRT13",    # urothelial markers
              "COL1A1", "CALD1",                    # muscle markers
              "PECAM1",                             # endothelial
              "DCN", "PDPN", "TAGLN",               # fibroblasts
              "CD2", "CD3D", "CD3E",                # T cells
              "C1QC", "CD14", "CSF1R",             # macrophages/myeloid
            "MRC1")

# DotPlot to visualize expression of list of genes across different clusters of cells
# look at intensity/ proportion of cells expressing each marker in each community
# list of genes were defined previously
DotPlot(so_donorint_dp_norm, features = features, dot.scale=8) + 
  theme(axis.text.x = element_text(size = 11)) + 
  RotatedAxis ()

ggsave("plots5/10_Dotplot.png", bg = "white")
# urothelial markers are shown to have high % expression - expected 

# FeaturePlot to visualize expression of selected genes across UMAP-reduced data set
# check how specific the markers are 
FeaturePlot(so_donorint_dp_norm, reduction = "umap", 
            pt.size = 2, alpha = 0.7, order = TRUE, 
            features = c("UPK2", "EPCAM","PECAM1", "CD2", 
                         "C1QC", "TERT", "CD3D", 
                         "CD3E", "MRC1", "CD19", "CD20", "CD22"))

ggsave("plots5/11_featureplot.png")

# community 2 (although later subdivides as suggested by clustertree) both appears to be immune cells
# C1QC highly expressed = macrophages // CD3D highly expressed = T cells
# communities 1,0 contains urothelial markers but subdivision was shown in cluster tree
# community 1, 0 important as significant difference observed between BC1, BC6
# find differences with differential expression analysis then visualize with volcano
# visualize genes expressed in each community, and attempt to categorize them to a theme i.e immune/ cell cycle related

# now prepare DEA
# switch back to integrated assay for comparisons (not for plotting)
DefaultAssay(so_donorint) <- "integrated"

# run DEA to identify differentially expressed genes between community 0, 1
uro.markers <- FindMarkers(so_donorint, ident.1 = 0, ident.2 = 1)

# check data frame 
head(uro.markers)

# avoid errors caused by small p-values downstream by setting p-value limits
# all adjusted p-value > or = to (1e-300)
uro.markers["p_val_adj"] <- lapply(uro.markers["p_val_adj"], pmax, 1e-300)

# preparation for volcano plot
# categorize genes (in uro.markers) based on their DEA results
# create new column 'DEA' to classify each gene as up, down, no based on log fold-change and adjusted p-value
uro.markers$DEA <- "NO"
uro.markers$DEA[uro.markers$avg_log2FC > 1 & uro.markers$p_val_adj < 0.05] <- "UP"
uro.markers$DEA[uro.markers$avg_log2FC < -1 & uro.markers$p_val_adj < 0.05] <- "DOWN"
uro.markers$genes <- rownames(uro.markers)

# Volcano plot to visualize DEA results for comparison between ident.1 = 0, ident.2 = 1 at res 0.05
ggplot(uro.markers, aes(x=avg_log2FC, y=-log10(p_val_adj))) + 
  geom_point(aes(colour = DEA), show.legend = FALSE) + 
  scale_colour_manual(values = c("blue", "gray", "red")) +
  geom_hline(yintercept = -log10(0.05), linetype = "dotted") + 
  geom_vline(xintercept = c(-1,1), linetype = "dotted") + 
  theme_bw() + 
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  geom_text_repel(data=subset(uro.markers, (abs(avg_log2FC) > 4 & p_val_adj < 0.05)), aes(x=avg_log2FC, y=-log10(p_val_adj), label=genes), max.overlaps = 1000, size=4.5)

ggsave("plots5/12_volcano_res0.05.png")

# Extract upregulated genes (log fold change > 4, p-value < 0.05 )
upreg_res_0.05 <- uro.markers[uro.markers$avg_log2FC > 4 & uro.markers$p_val_adj < 0.05, "genes"]

# Extract downregulated genes (log fold change > 4, p-value < 0.05 )
downreg_res_0.05<- uro.markers[uro.markers$avg_log2FC < -3 & uro.markers$p_val_adj < 0.05, "genes"]

print(upreg_res_0.05) 
print(downreg_res_0.05)

# upreg genes red (expressed in ident.1 = 0) 
# IGHG4 (immune), HS3ST2 (biosynthesis), BGN (inflammation), PALDI (cell migration + cancer), SCN10A (Na channel subunit, neuronal)
# cannot really define community 0 just yet and is suggested to subdivide based on cluster tree plot
# Will look again at higher resolution (see section 7)

# downreg genes blue (expressed in ident.2 = 1)
# majority linked to cell cycle and division in community 1 
# Not suggested to further subdivide. Go ahead with cell cycle scoring 


######################################################

# I am working on scRNAseq data derived from human bladder cancer 'Urothelial carcinoma'
# Urothelial carcinoma has 2 subtypes - muscle invasive (MIBC), non-muscle invasive (NMIBC)
# NMIBC has recurrence rate 70%-80% if untreated and approx 50% progress to MIBC (Aya T Shalata et al. 2022), where MIBC has a 50-70% mortality rate! (Jong Chul Park et al. 2014)
# I will look at urothelial heterogeneity within non-malignant NMIBC papilloma as intratumoural heterogeneity is a major driver to drug resistance (NA Saunders et al. 2012)

# Data comes from 2 individuals. (M 67yrs, M 58yrs)
# sample BC1 = low grade Ta non-malignant NMIBC cells from (M, 67yrs)
# sample BC6 = low grade Ta non-malignant NMIBC cells from (M, 58yrs)
# differences between the two cell sets could be, but not limited to person differences (age, genetic mutations, lifestyle)

# Firstly, I will access the Genomics shared libraries 
.libPaths("libs/R_4.3.3")

# load relevant libraries
library(tidyverse)
citation("tidyverse")
library(Seurat)
citation("Seurat")
library(clustree)
citation("clustree")
library(ggplot2)
citation("ggplot2")
library(ggrepel)
citation("ggrepel")
library(fgsea)
citation("fgsea")

# 1 Load, preview and explore data ####
counts <- Read10X("data5/02_assessment_options/assessment02_BC1-BC6")

# check dimensions of data (i.e number of genes - rows, cells - columns)
dim(counts)
# number of rows = 38606, columns = 16155

# quick preview of data. Structure, type, first few values
str(counts)

# setting IDs and conditions (here = cancer T stages) for counts
ids <- c("BC1", "BC6")
con <- c("BC1", "BC6")

# create a table 'cell_counts' based on column names from 'counts'
# create metadata df, and annotate my data with labels
cell_counts <- table(substring(colnames(counts),18))

# construct two vectors 'sample', 'conditions' from 'ids', 'con' based on 'cell_counts'
# set up sample and condition labels for the backgrounds
samples <- c()
for (s in 1:length(ids)) { 
  samples <- c(samples, (rep(paste(ids)[s], cell_counts[s])))
} 
conditions <- c()
for (c in 1:length(con)) { 
  conditions <- c(conditions, (rep(paste(con)[c], cell_counts[c])))
} 

# now create a metadata df
metadata <- data.frame(Sample=samples, Conditions=conditions, row.names=colnames(counts))

# then check metadata df cell numbers match expected 
metadata |> group_by(Sample) |> summarise(Cells=n())
metadata |> group_by(Conditions) |> summarise(Cells=n())

# create seurat object 'so' from data - R package used for scRNAseq analysis that stores, organise scRNAseq data, metadata, results 
# rows = genes, columns = cells
# 'meta.data' specifies a metadata df which has additional info of each cell (ie cell types)
so <- CreateSeuratObject(counts=counts, project="Genomics_W5assessment", meta.data=metadata)

# assign cell identities based on 'conditions' column from metadata
# for further analysis to consider cells belonging to different conditions
# i.e later DEA, clustering etc can be done based on 'conditions'
Idents(so) <- so@meta.data$Conditions

# check seurat version
packageVersion("Seurat")

# remove unused variables to tidy up memory
rm(c, s, cell_counts, con, conditions, ids, samples, counts, metadata)

# ################################### #

# 2 Data QC check ####

# removing low quality data from data set 
# high Mt% indicates dying cells, often correlated with low number of features
# high number of features suggests 2 cells are present in sample instead of single cell

# calc %MT gene expression  
# add a new metadata column 'percent.mt' of %MT for each cell
so <- PercentageFeatureSet(so, pattern="^MT-", col.name="percent.mt")

# Visualize distribution of MT gene percentages 
VlnPlot(so, features = "percent.mt")

# violin plot to visualize and compare distribution of 2 selected features - number of features, MT%
# across different cell groups (i.e sample or condition)
# assists QC by easing detection of any outliers in my data (low quality/stressed/dying cells)
VlnPlot(so, features=c("nFeature_RNA","percent.mt"), 
        ncol=2, pt.size=0, group.by="Sample", raster=FALSE)

# distribution pattern is different for BC1 BC6 in nFeature_RNA
# BC1 data is too evenly distributed, no median possible to be observed
# In contrast BC6 has nice violin shaped distribution making it relatively easier to decide cut off points to remove low quality data
# so I decide data cut off threshold based on BC6 

# save plot
ggsave("plots5/1_violinplot_preQC.png")

# Summarize key stats based on distribution above, related to %MT and number of features 
# Create a summary table 
summary_metadata <- so@meta.data |> 
  group_by(Sample) |> 
  summarize(
    mt_med = quantile(percent.mt, 0.5),
    mt_q95 = quantile(percent.mt, 0.95),
    mt_q75_plus_IQR = quantile(percent.mt, 0.75) + (1.5 * IQR(percent.mt)),
    feat_med = median(nFeature_RNA),
    feat_q95 = quantile(nFeature_RNA, 0.95),
    feat_q75_plus_IQR = quantile(nFeature_RNA, 0.75) + (1.5 * IQR(nFeature_RNA)),
    count = n()
  )

# print the table
summary_metadata

# scatter plot number of features VS %MT for each cell 
# assists my QC by identifying cells with high MT%, low features (indicative of dying cells/ empty droplets)
ggplot(so@meta.data, aes(nFeature_RNA, percent.mt, colour=Sample)) +
  geom_point(alpha=0.2) + 
  facet_grid(.~Sample) + 
  theme_bw()

ggsave("plots5/2_nfeatures_vs_%MT_preQC.png")

# now remove low feature/high MT% cells using UQ + (1.5*IQR) rule
# find the upper threshold limit for MT% in both samples, then take the lowest value
mt_filt <- min(summary_metadata$mt_q75_plus_IQR)

# find upper threshold limit for feature number in both, then take the lowest value
feat_max_filt <- min(summary_metadata$feat_q75_plus_IQR)

# print proposed thresholds
mt_filt # 6.370424
feat_max_filt # 5279.75
# These are proposed threshold, but I need to take closer look at data first to decide cut off points  

# Taking a closer look at low quality data first - I will use 2 graphs, one for each feature
# this is in preparation to filter data after visualization 

# 1 - zoomed-in version scatter plot focusing on cells < 10% MT content (clear up feature 1)
# I can exclude cells that are potentially stressed/ dying more clearly since zoomed in view
ggplot(subset(so@meta.data, subset = percent.mt < 10), aes(nFeature_RNA, percent.mt, colour=Sample)) + 
  geom_point(alpha=0.2) + 
  facet_grid(.~Sample) + 
  theme_bw() + 
  xlim(0,8000) + # set limits to x axis to zoom in when viewing graph to decide cut off point

ggsave("plots5/3_%MT_zoomedlowqual_QC.png")

# 2 - density plot for number of features detected per cell (clear up feature 2) 
# across different samples in the seurat object 'so' I created earlier
# from plot I can identify potential problems like under-sampling or a variability in number of genes detected
ggplot(so@meta.data, aes(x=nFeature_RNA, colour=Sample)) + 
  geom_density() + 
  facet_grid(.~Sample) + 
  theme_bw() + 
  xlim(0,3500) # set limits for x axis to zoom in when viewing graph to decide cut off point, the 'dip' in graph 
# graph 'dip' is approx at 1800 on x axis

ggsave("plots5/4_nfeature_zoomedlowqual_QC.png")

# Now I can begin data filtering process! - filter based on 3 conditions
# 1 - cells < 1800 features (genes) detected
# 2 - cells with more than number of features defined by 'feat_max_filt' 
# 3 - % MT gene expression. Threshold 6.370424 prev defined by 'mt_filt'. Again, filtering out cells with high MT content as indicative of low quality cells (i.e dying)
so <- subset(so, subset = nFeature_RNA >= 1800 & nFeature_RNA <= feat_max_filt & percent.mt <= mt_filt)

# after filtering I have new data set - post QC
# so generate new summary data for filtered data set 
so@meta.data |> group_by(Sample) |> summarize(mt_med = quantile(percent.mt, 0.5),
                                              mt_q95 = quantile(percent.mt, 0.95),
                                              mt_q75_plus_IQR = quantile(percent.mt, 0.75) + (1.5 * IQR(percent.mt)),
                                              feat_med = median(nFeature_RNA),
                                              feat_q95 = quantile(nFeature_RNA, 0.95),
                                              feat_q75_plus_IQR = quantile(nFeature_RNA, 0.75) + (1.5 * IQR(nFeature_RNA)),
                                              count = n())

# now visualize new summary data post QC - violin plot like previous
# violin plot to visualize and compare distribution of 2 selected features - number of features, MT%
# across different cell groups (i.e sample or condition)
VlnPlot(so, features=c("nFeature_RNA", "percent.mt"), 
        ncol=2, pt.size=0, group.by="Sample", raster=FALSE)

# Recap - I have chosen thresholds based on BC6 as it had better initial distribution to decide data cut off points
# filtered data only shows cells of good quality facilitating my downstream analysis

ggsave("plots5/5_violinplot_postQC.png")

# some points to consider about the QC process is that : 
# same filters are applied to both data sets BC1 BC6, which would surely have differences (i.e starting distributions, sequencing depth across data sets)
# a threshold that works well for one data set may i.e discard good quality cells in another
# but can only try our best when deciding cut-off point - i.e zooming into graph. It is better than including bad quality data!

# create save point
save.image("savepointA_QC-complete.RData")

# ############################################## #

# 3 Data Integration ####

# merge data sets after filtering each data sets individually. Need to ensure filtering doesn't bias data
# potential problem - imbalanced data set (i.e one was aggressively filtered = has far fewer cells than other, could cause bias downstream)
# use stats to account for these differences (i.e normalization, batch correction, merging strategies)
# here I use normalization 

# Merging data is crucial for comparison 
# different cell types from one person can be more similar than same cell type from 2 people 
# this means same cell type from different donors would be separated during analysis/ clustering - not useful
# integration needed to allow comparison

# split objects by conditions defined in section 1 
con.list <- SplitObject(so, split.by = "Conditions")

# normalize and identify variable features for each data set independently
# also ensure the modified object is returned so con.list is updated
# top 3000 most variable genes selected for downstream analysis
con.list <- lapply(X = con.list, FUN = function(x) {
  x <- NormalizeData(x, normalization.method = "LogNormalize")
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 3000)
  return(x)  
})

# Scale the data and perform PCA for each condition
con.list <- lapply(X = con.list, FUN = function(x) {
  x <- ScaleData(x)
  x <- RunPCA(x)
  return(x)
})

# after normalization and variable feature selection - standard thing to do is integrate the 2 conditions based on shared variable features
# then can proceed with other analyses such as scaling, PCA, clustering, or differential expression
# I prev selected top 3000 most variable genes to look at (nfeature)
# when shared variable features close to 3000 = likely lose biological difference
# but some similarities/ shared variables (to certain extent) needed to merge 2 data sets = ~600-2400, closer halfway the better

# check overlap between most variable features in 2 conditions (BC1,BC6)
length(intersect(VariableFeatures(con.list$BC1), VariableFeatures(con.list$BC6)))
# 1385

# select features repeatedly variable across data sets for integration
intfeats <- SelectIntegrationFeatures(object.list = con.list, nfeatures = 3000)

# integration - finally! Now combine data sets using anchors 
int.anchors <- FindIntegrationAnchors(object.list = con.list, anchor.features = intfeats)
so_donorint <- IntegrateData(anchorset = int.anchors)

# scaling data to ensure genes with higher variance don't dominate analysis following integration, each feature contributes equally
# set default assay to 'integrated' so downstream analysis uses integrated data (corrected values)
# instead of data before integration/ raw counts etc
DefaultAssay(so_donorint) <- "integrated"
so_donorint <- ScaleData(so_donorint, features = rownames(so_donorint))

# clear up after integration 
# splitted conditions, anchors used to integrate datasets, features selected for integration, original 'so' object before processing
rm(con.list, int.anchors, intfeats, so)

# create save point 
save.image("savepointB_QC-integration-complete.RData")

# Extra analysis (1,2,3,4) ####

# since no new packages were able to be downloaded, i do custom things

# Custom Ligand-Receptor Interaction Networks
# use custom lists of ligands and receptors to build your interaction network manually

# Define ligand-receptor pairs (from a database or literature)
ligand_receptor_pairs <- data.frame(
  Ligand = c("CD80", "CD86","CD70", "CCL2", "CXCL9"),
  Receptor = c("CD28", "CD28", "CD27", "CCR2", "CXCL9")
)

# Create a data frame to represent ligand-receptor interactions for each cell
ligand_receptor_matrix <- data.frame(cell = colnames(so), stringsAsFactors = FALSE)

# Loop over each ligand-receptor pair and check if they are present in the gene list
for (i in 1:nrow(ligand_receptor_pairs)) {
  ligand <- ligand_receptor_pairs$Ligand[i]
  receptor <- ligand_receptor_pairs$Receptor[i]
  
  # Check if the ligand and receptor genes exist in the Seurat object (so)
  ligand_receptor_matrix[[paste0("Ligand_", ligand)]] <- ifelse(ligand %in% rownames(so), 1, 0)
  ligand_receptor_matrix[[paste0("Receptor_", receptor)]] <- ifelse(receptor %in% rownames(so), 1, 0)
}

# Assign this new matrix to the Seurat object metadata
so@meta.data <- cbind(so@meta.data, ligand_receptor_matrix[, -1])  # Remove 'cell' column as it's redundant

# Verify if the new columns have been added
head(so@meta.data)

# Calculate the expression of ligands and receptors for each cell type (or cluster) in your Seurat object.
ligand_expression <- FetchData(so_donorint, vars = ligand_receptor_pairs$Ligand)
receptor_expression <- FetchData(seurat_object, vars = ligand_receptor_pairs$Receptor)

# Calculate potential interactions based on co-expression or proximity of ligand-receptor pairs.
# Example: correlation of ligand and receptor expression
interaction_scores <- cor(ligand_expression, receptor_expression)

# Visualize the interactions as a network.
library(igraph)

network_graph <- graph_from_data_frame(ligand_receptor_pairs)

# Open a graphics device (PNG in this case)
png("plots5/ligandreceptorinteractions.png", 
    width = 1000, height = 800, res = 300, bg = "white")

plot(network_graph,
     vertex.label = V(network_graph)$name, 
     vertex.label.cex = 0.3,         # Adjust label size
     vertex.label.color = "black",   # Label color
     vertex.label.dist = 4)          # Distance from node

# Close the device to save the plot
dev.off()

To sort your WooCommerce products by the highest discount using JetSmartFilters' **Sort Filter**, you can use the following steps and PHP snippet:

---

### **1. Add a Custom Meta Key for Discount Calculation**
You need to calculate and store the discount as a custom meta key for each product. This meta key will be used in the JetSmartFilters Sort Filter.

Add the following PHP snippet to your theme's `functions.php` file or a custom plugin:

```php
add_action('save_post_product', 'update_product_discount_meta');
function update_product_discount_meta($post_id) {
    // Ensure it's a product post type
    if (get_post_type($post_id) !== 'product') {
        return;
    }

    // Get the product object
    $product = wc_get_product($post_id);
    if (!$product) {
        return;
    }

    // Get the regular and sale prices
    $regular_price = (float) $product->get_regular_price();
    $sale_price = (float) $product->get_sale_price();

    // Calculate the discount percentage
    if ($regular_price > 0 && $sale_price > 0) {
        $discount = round((($regular_price - $sale_price) / $regular_price) * 100);
    } else {
        $discount = 0; // No discount
    }

    // Update the custom field with the discount value
    update_post_meta($post_id, '_product_discount', $discount);
}
```

---

### **2. Recalculate Discounts for Existing Products**
To apply the discount calculation to existing products, you can run a script or use a plugin like **WP Crontrol** to loop through all products and update their discounts.

```php
add_action('init', 'recalculate_discounts_for_all_products');
function recalculate_discounts_for_all_products() {
    $args = array(
        'post_type' => 'product',
        'posts_per_page' => -1,
        'fields' => 'ids',
    );
    $products = get_posts($args);

    foreach ($products as $product_id) {
        update_product_discount_meta($product_id);
    }
}
```
This will process all products and save their discounts. After running it once, you can remove or comment out this action.

---

### **3. Configure the JetSmartFilters Sort Filter**
In the **Sort Filter** settings (as shown in your screenshot):
1. **Order By**: Select **Meta Key Numeric** (since the discount is a number).
2. **Key**: Enter `_product_discount` (the meta key used in the PHP snippet).
3. **Order**: Set to **DESC** (to sort from highest to lowest discount).

---

### **4. Test the Sorting**
- Add the JetSmartFilters **Sort Filter** to the page where your product listing grid is displayed.
- Ensure the sorting works as expected, with the products having the highest discount appearing first.

Clase	    Media Query Range		Ejemplo de Clase
col-*		Default (XS: <576px)	col-12
col-sm-*	≥576px					col-sm-6
col-md-*	≥768px					col-md-4
col-lg-*	≥992px					col-lg-3
col-xl-*	≥1200px					col-xl-2
col-xxl-*	≥1400px (Bootstrap 5)	col-xxl-1

* GOAL: Within a macro, you want to create a new variable name.
* It could then be used as a data set name, or a variable name to be assigned, etc.;
%macro newname(dset=,varname=);
* You have to make the new name before you use it in
* another data statement;
data _NULL_;
L1=CATS("&varname","_Plus1");
CALL SYMPUT('namenew',L1);
;
* Now you can use the variable name created by SYMPUT;
DATA REVISED;SET &DSET;
   &namenew=&varname+1;
run;
%mend;
Data try;
input ABCD @@;
datalines;
1 2 3 4
;run;
%newname(dset=try,varname=ABCD);
proc print data=revised;run;

https://pubads.g.doubleclick.net/gampad/ads?iu=/21775744923/external/single_ad_samples&sz=640x480&cust_params=sample_ct%3Dredirecterror&ciu_szs=300x250%2C728x90&gdfp_req=1&output=vast&unviewed_position_start=1&env=vp&impl=s&nofb=1&correlator=

https://pubads.g.doubleclick.net/gampad/ads?iu=/21775744923/external/single_ad_samples&sz=640x480&cust_params=sample_ct%3Dredirecterror&ciu_szs=300x250%2C728x90&gdfp_req=1&output=vast&unviewed_position_start=1&env=vp&impl=s&correlator=

Dork For : Finding exposed cloud service credentials

Regards,
Joel Indra

            

git clone https://github.com/yourusername/hospital-management-system.git

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":cute-sun: Boost Days - What's On This Week :cute-sun:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n\n Happy Monday Melbourne!\n\n"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":coffee: Barista Services on Level 2 :coffee:",
				"emoji": true
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": " Wednesday, 29th January :calendar-date-22:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": " \n\n :lunch: *Light Lunch*: Provided by Kartel Catering from *12:30pm* on *Levels 1 & 2!* \n\n"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Thursday, 23rd January :calendar-date-23:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":breakfast: *Breakfast*: Provided by *Kartel Catering* from *8:30am - 10:30am on Levels 1 & 2*. \n\n "
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "Stay tuned for more fun throughout the year. Happy 2025! :party-wx:"
			}
		}
	]
}

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": " :mirror_ball::star: Boost Days - What's on this week! :star::mirror_ball:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "See below for what's in store for our Boost Days this week:"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":calendar-date-21: Tuesday, 28th January",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n:coffee: *Xero Café*: Café-style beverages and sweet treats \n\n:meow-coffee-intensifies-and-shakes: *Barista Special*: _Iced Chai Latte_ \n\n:pancakes: *Breakfast*: Provided by *Catroux* from *8:30am - 10:30am* \n\n :nail_care::skin-tone-2: *Tipsy Manicures:* Emma offers quick Shape & Shine express manicures, Gel It Manicures & Pedicures and Hand N’ Arm & Foot N’ Leg Massages, there's a treatment for everyone's pampering needs. treat yourself <https://docs.google.com/spreadsheets/d/1pTGAD8oFXmPF890Uzj4d-Crfch8muuzEzDnNyBA9ReY/edit?gid=23583621#gid=23583621|*here.*> "
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":calendar-date-23: Thursday, 30th January",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":coffee: *Xero Café*: Café-style beverages and sweet treats \n\n :meow-coffee-intensifies-and-shakes: *Barista Special*: _Iced Chai Latte_ \n\n :sandwich: *Light Lunch*: Provided by *Catroux* from *12:30pm - 1:30pm* \n\n  \n\n"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "Stay tuned to this channel for more details, check out the <https://calendar.google.com/calendar/u/0?cid=eGVyby5jb21fMXM4M3NiZzc1dnY0aThpY2FiZDZvZ2xncW9AZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ|*Auckland Social Calendar*>, and get ready to Boost your workdays!\n\nLove,\nWX :wx:"
			}
		}
	]
}

gdb -tui -ex=r --args out/Debug/chrome --disable-seccomp-sandbox \
    http://google.com

// Custom function for validating phone numbers across multiple forms and fields
add_filter('gform_field_validation', function($result, $value, $form, $field) {

    $form_field_pairs = [
        4 => [8],
        1 => [4],
        2 => [4],
        3 => [4],
    ];

    if (isset($form_field_pairs[$form['id']]) && in_array($field->id, $form_field_pairs[$form['id']])) {
       
        $pattern = '/^\d{10}$/';
        
        if (!preg_match($pattern, $value)) {
            $result['is_valid'] = false;
            $result['message'] = 'Please enter a valid 10-digit phone number.';
        }
    }

    return $result;
}, 10, 4);

# 6 Immune cell scoring ####
# since community 1, 0 both contain immune cells, do immune cell scoring to see involvement 

# Score each cell based on activation of listed genes 




# for T cell activation 
# define a gene set for T-cell activation
tcell_activation_genes <-  c("CD69", "CD3G", "IFNG", "TNF", "CD3E", "CD3D", "ZAP70", "LCK")

# add activation score to Seurat object
seurat_object <- AddModuleScore(so_donorint, features = list(tcell_activation_genes), name = "Tcell_Activation")

# visualize activation score and save plot
VlnPlot(seurat_object, features = "Tcell_Activation1", pt.size = 0)
ggsave("plots5/tcellactivation_0.25.png")





# for cytotoxic T cells 
# define cytotoxic T cells gene set and add cytotoxic T cells score to Seurat object
cytotoxic_tcell_genes <-  c("CD8A", "GZMK", "GZMA") 
seurat_object <- AddModuleScore(seurat_object, features = list(cytotoxic_tcell_genes), name = "Cytotoxic_Tcell")

# visualize cytotoxic T cells 
VlnPlot(seurat_object, features = "Cytotoxic_Tcell1", pt.size = 0)
ggsave("plots5/cytotoxictcells_0.25.png")



# for exhausted T cells
# define exhausted T cells gene set abd add exhausted T cells score to Seurat object
exhausted_tcell_genes <-  c("PD1", "CTLA4", "LAG3", "TIM3") 
seurat_object <- AddModuleScore(seurat_object, features = list(exhausted_tcell_genes), name = "Exhausted_Tcell")

# visualize exhausted T cells 
VlnPlot(seurat_object, features = "Exhausted_Tcell1", pt.size = 0)
ggsave("plots5/exhaustedtcells_0.25.png")





# for Macrophage activation 
# define a gene set for macrophage activation
macrophage_activation_genes <-  c("CD80", "CD86", "TNF", "IL6", "STAT1", "IRF5", "iNOS", "CXCL8", "IL8")

# add activation score to Seurat object
seurat_object <- AddModuleScore(so_donorint, features = list(macrophage_activation_genes), name = "Macrophage_Activation")

# visualize activation score
VlnPlot(seurat_object, features = "Macrophage_Activation1", pt.size = 0)
ggsave("plots5/macrophageactivation_0.25.png")




# for B cell activation - just exploring 
# define a gene set for B-cell activation
bcell_activation_genes <-  c("IRF4", "CD86", "CD79A", "BTK")

# add activation score to Seurat object
seurat_object <- AddModuleScore(so_donorint, features = list(bcell_activation_genes), name = "Bcell_Activation")

# visualize activation score
VlnPlot(seurat_object, features = "Bcell_Activation1", , pt.size = 0)
ggsave("plots5/bcellactivation_0.25.png")



For full activation, B cells require co-stimulatory signals, often provided by T helper cells (Tfh cells) during T-dependent activation or through pathogen-associated signals during T-independent activation.

# 6 Res 0.25 ####

# ensure assay is integrated 
DefaultAssay(so_donorint) <- "integrated"

# set resolution at 0.25 which is higher than previously used 0.05 for clustering cells 
res <- 0.25

# now assign cells to a cluster and run UMAP - reduce dimensions so I can visualize
# UMAP allows visualizing clusters 
# 2D representation of cells to visualize similarities/differences based on high dimensional features (i.e gene expression) - based on first PCA
so_donorint <- FindClusters(so_donorint, resolution = res)
so_donorint <- RunUMAP(so_donorint, dims=1:pcs, return.model = TRUE, verbose = FALSE)

# UMAP plot - Visualization of my clustered data using UMAP plot 
DimPlot(so_donorint, reduction = "umap", label = TRUE, label.size = 6)
# as expected, further subdivision of community X shown

ggsave("plots5/UMAP_res0.25.png")

# split UMAP plot - based on 'conditions' + add labels 
# shows me how clusters are distributed across different experimental conditions 
DimPlot(so_donorint, reduction = "umap", split.by = "Conditions", label = TRUE, label.size = 6)
# similar to UMAP at res 0.05, community 0 shown to lack large proportion of cells 

ggsave("plots5/split_UMAP_res0.25.png") 

# % distribution of each cell cluster across different conditions 
# allows me to understand how cell populations are organized in relation to experimental conditions 
round(prop.table(table(Idents(so_donorint), so_donorint$Conditions))*100,3)


# DEA for cell communities at resolution 0.25

# now prepare DEA
# switch back to integrated assay for comparisons 
DefaultAssay(so_donorint) <- "integrated"

# run DEA to identify differentially expressed genes between community 1, 0
uro.markers <- FindMarkers(so_donorint, ident.1 = 7, ident.2 = 5)
# graph description is based on ident.1 = community x
# but I have repeated the above code with different 'ident' to compare gene expression between communities

# check data frame 
head(uro.markers)

# avoid errors caused by small p-values downstream by setting p-value limits
# all adjusted p-value > or = to (1e-300)
uro.markers["p_val_adj"] <- lapply(uro.markers["p_val_adj"], pmax, 1e-300)

# preparation for volcano plot
# categorize genes (in uro.markers) based on their DEA results
# create new column 'DEA' to classify each gene as up, down, no based on log fold-change and adjusted p-value
uro.markers$DEA <- "NO"
uro.markers$DEA[uro.markers$avg_log2FC > 1 & uro.markers$p_val_adj < 0.05] <- "UP"
uro.markers$DEA[uro.markers$avg_log2FC < -1 & uro.markers$p_val_adj < 0.05] <- "DOWN"
uro.markers$genes <- rownames(uro.markers)

# Volcano plot to visualize DEA results for comparison between the conditions (Ta1, Ta6)
ggplot(uro.markers, aes(x=avg_log2FC, y=-log10(p_val_adj))) + 
  geom_point(aes(colour = DEA), show.legend = FALSE) + 
  scale_colour_manual(values = c("blue", "gray", "red")) +
  geom_hline(yintercept = -log10(0.05), linetype = "dotted") + 
  geom_vline(xintercept = c(-1,1), linetype = "dotted") + 
  theme_bw() + 
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"), plot.title = element_text(hjust = 0.5)) +
  geom_text_repel(data=subset(uro.markers, (abs(avg_log2FC) > 9 & p_val_adj < 0.05)| (avg_log2FC < -5 & p_val_adj < 0.05)), aes(x=avg_log2FC, y=-log10(p_val_adj), label=genes), max.overlaps = 1000, size=4.5) +
  ggtitle("DEA between community 7 and 5 (7 in red)") 

# Extract upregulated genes (log fold change > 4, p-value < 0.05 )
upreg_res_0.25 <- uro.markers[uro.markers$avg_log2FC > 2 & uro.markers$p_val_adj < 0.05, "genes"]

# Extract downregulated genes (log fold change > 4, p-value < 0.05 )
downreg_res_0.25<- uro.markers[uro.markers$avg_log2FC < -3 & uro.markers$p_val_adj < 0.05, "genes"]

print(upreg_res_0.25) 

print(downreg_res_0.25)

ggsave("plots5/volcano_integrated_7vs5.png")

sudo lsof -t -i tcp:80 -s tcp:listen | sudo xargs kill

sudo lsof -i tcp:80 -s tcp:listen

sudo lsof -i tcp:80

COMMAND  PID     USER   FD   TYPE DEVICE SIZE/OFF NODE NAME  
nginx   1858     root    6u  IPv4   5043      0t0  TCP ruir.mxxx.com:http (LISTEN)  
nginx   1867 www-data    6u  IPv4   5043      0t0  TCP ruir.mxxx.com:http (LISTEN)  
nginx   1868 www-data    6u  IPv4   5043      0t0  TCP ruir.mxxx.com:http (LISTEN)  
nginx   1869 www-data    6u  IPv4   5043      0t0  TCP ruir.mxxx.com:http (LISTEN)  
nginx   1871 www-data    6u  IPv4   5043      0t0  TCP ruir.mxxx.com:http (LISTEN)  

echo kill $(sudo netstat -anp | awk '/ LISTEN / {if($4 ~ ":80$") { gsub("/.*","",$7); print $7; exit } }')

netstat -anp --numeric-ports | grep ":${PORT}\>.*:" 

fuser -v "${PORT}"/tcp

lsof -P -S 2 -i "tcp:${PORT}" | grep "\(:${PORT}->.*:\|:$PORT (LISTEN)$\)"

$ netstat -anp --numeric-ports | grep ":80\>.*:" 
tcp6       0      0 :::80           :::*            LISTEN      1914/apache2    

$ fuser -v "80/tcp"
                     USER        PID ACCESS COMMAND
80/tcp:              root       1914 F.... apache2
                     www-data  12418 F.... apache2
...

$ lsof -P -S 2 -i "tcp:80" | grep "\(:80->.*:\|:80 (LISTEN)$\)"
apache2  1914     root    4u  IPv6   11920      0t0  TCP *:80 (LISTEN)
apache2 12418 www-data    4u  IPv6   11920      0t0  TCP *:80 (LISTEN)
...

CMS:

{url entity='cms' id=6 id_lang=Context::getContext()->language->id}

Category:
{url entity='category' id=ID_CATEGORIA}

Producto:
{url entity='product' id=ID_PRODUCTO}

Brand:
{url entity='manufacturer' id=ID_MANUFACTURER}

Sitemap:
{$urls.pages.sitemap}


Attachments
{url entity='attachment' params=['id_attachment' => $attachment.id_attachment]}

public class Solution {
    public int solve(int[] A, int B) {
        
        HashMap<Integer,Integer> hm=new HashMap<>();
        
        int cnt=0;
        int sum=0;
        hm.put(0,1);
        for(int i=0;i<A.length;i++){
            sum^=A[i];
            if(hm.containsKey(sum^B)){
                cnt+=hm.get(sum^B);
                
            }if(hm.containsKey(sum)){
                hm.put(sum,hm.get(sum)+1);
            }
            else{
                hm.put(sum,1);
            }
            
        }
        return cnt;
    }
}

class Solution {
    public List<List<Integer>> threeSum(int[] nums) {
        List<List<Integer>> ll=new ArrayList<>();
        
        Arrays.sort(nums);
        for(int i=0;i<nums.length;i++){
           
           if(i>0 && (nums[i]==nums[i-1]))
                continue;
            int j=i+1;
            int k=nums.length-1;
            while(j<k){
                int sum=nums[i]+nums[j]+nums[k];
                if(sum<0){
                    j++;
                }
                else if(sum>0){
                    k--;
                }
                else{
                    List<Integer> l=new ArrayList<>();
                    l.add(nums[i]);
                    l.add(nums[j]);
                    l.add(nums[k]);
                    j++;
                    k--;
                    while(j<k && nums[j]==nums[j-1]){
                        j++;
                    }
                    while(j<k && nums[k]==nums[k+1]){
                        k--;
                    }
                    ll.add(l);

                }
            }
        }
            return ll;
    }
}

<style>
  .elementor-element:not(.dont) {
    opacity: 0;
    transform: translateY(8vh) skewY(-3deg);
    transition: opacity 0.5s ease, transform 0.8s ease 0.2s;
  }
  .elementor-element.animated:not(.dont) {
    opacity: 1;
    transform: translateY(0) skewY(0deg);
  }
</style>

<script>
  document.addEventListener("DOMContentLoaded", () => {
    const handleIntersection = (entries, observer) => {
      entries.forEach(entry => {
        entry.target.classList.toggle('animated', entry.isIntersecting);
      });
    };
    const observer = new IntersectionObserver(handleIntersection, {
      root: null,
      rootMargin: '0px',
      threshold: 0
    });
    document.querySelectorAll('.elementor-element').forEach(element => observer.observe(element));
  });

/templates/catalog/_partials/miniatures/product.tpl

<div style="text-align: center; margin: 3px;">
    	<form action="{$urls.pages.cart}" method="post" id="add-to-cart-or-refresh">	
          <input type="hidden" name="token" value="{$static_token}">
          <input type="hidden" name="id_product" value="{$product.id}" id="product_page_product_id">
          <button class="btn btn-primary add-to-cart {if $product.quantity < 1}out-of-stock{/if}" data-button-action="add-to-cart" type="submit">
                   <i class="material-icons shopping-cart">shopping_cart</i>
                   {l s='Add to cart' d='Shop.Theme.Actions'}
          </button>
  		</form>
</div>

<div class="pro-manufacture">
<a href="{$link->getManufacturerLink($product.id_manufacturer)|escape:'html':'UTF-8'}" title="{Manufacturer::getNameById($product.id_manufacturer|escape:'html':'UTF-8')}">{Manufacturer::getNameById($product.id_manufacturer|escape:'html':'UTF-8')}</a>
</div>

<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script>
$(document).ready(function () {
    var pageMap = {
        "/en/whale-safari/": "https://fareharbor.com/embeds/book/kullabergsguiderna/?full-items=yes&flow=1324061",
        "/tumlarsafari/": "https://fareharbor.com/embeds/book/kullabergsguiderna/?full-items=yes&flow=1324061",
        "/grottvandring/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510420/?full-items=yes&flow=1085497",
        "/en/cavewalk/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510420/?full-items=yes&flow=1085497",
        "/en/rent-a-mountainbike-on-kullaberg/": "https://fareharbor.com/embeds/book/kullabergsguiderna/?full-items=yes&flow=1089659",
        "/hyr-en-mountainbike-cykla-pa-kullaberg/": "https://fareharbor.com/embeds/book/kullabergsguiderna/?full-items=yes&flow=1089659",
        "/en/abseiling/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510419/?full-items=yes&flow=1085497",
        "/bergsnedfirning-pa-kullaberg/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510419/?full-items=yes&flow=1085497",
        "/en/coasteering-eng/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510439/?full-items=yes&flow=1085497",
        "/coasteering/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510439/?full-items=yes&flow=1085497",
        "/en/bike-rental/": "https://fareharbor.com/embeds/book/kullabergsguiderna/?full-items=yes&flow=1324078",
        "/hyrcykel/": "https://fareharbor.com/embeds/book/kullabergsguiderna/?full-items=yes&flow=1324078",
        "/en/treasure-hunt/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510421/?full-items=yes&flow=1085497",
        "/kullamannens-skattjakt/": "https://fareharbor.com/embeds/book/kullabergsguiderna/items/510421/?full-items=yes&flow=1085497"
    };

    // Get the current page path
    var currentPath = window.location.pathname;

    // Check if the current path exists in the pageMap
    if (pageMap[currentPath]) {
        var bookingURL = pageMap[currentPath];

        // Create the desktop button
        var desktopButton = $('<a>', {
            href: bookingURL,
            style: "margin-right: 2em !important; text-decoration:none !important; font-family:'Roboto Condensed', sans-serif !important; font-size:20px !important; font-weight:700 !important; letter-spacing: 1.1px !important;box-shadow:none !important; padding: .15em 2em !important;",
            class: "fh-hide--mobile fh-fixed--bottom fh-icon--cal fh-button-true-flat-color fh-shape--square fh-lang",
            text: "BOKA NU"
        });

        // Create the mobile button
        var mobileButton = $('<a>', {
            href: bookingURL,
            style: "text-decoration:none !important; width: 50% !important; margin: 0 auto !important; font-family:'Roboto Condensed', sans-serif !important; font-size: 1.2em !important; font-weight:bold !important; letter-spacing: 1.1px !important; box-shadow:none !important; text-align: center !important; width: 60% !important;",
            class: "fh-hide--desktop fh-size--small fh-fixed--bottom fh-button-true-flat-color fh-color--white fh-shape--square fh-lang",
            text: "BOKA NU"
        });

        // Append the buttons to the body
        $('body').append(desktopButton).append(mobileButton);

        // Update text to "BOOK NOW" for English pages
        if (currentPath.includes('/en/')) {
            $('a.fh-fixed--bottom').text('BOOK NOW');
        }
    }
});
</script>

Beleaf Technologies offers the ultimate solution for meme coin development, turning creative ideas into successful digital currencies. With expertise in blockchain technology, we provide start-to-end services, including concept design, token creation, smart contract development, and marketing strategies. Our team ensures smooth creation,flexcibility, and security for your project. Partner with Beleaf Technologies and bring your vision to life in the vibrant world of meme coins. Your success is our priority!


Create your own meme coin effortlessly with Beleaf Technologies! We provide customized solutions, from design to deployment, ensuring a unique, secure, and successful digital currency customize to your vision.

Contact today for free demo : https://www.beleaftechnologies.com/meme-coin-development-company
Whatsapp: +91 7904323274
Skype: live:.cid.62ff8496
d3390349
Telegram: @BeleafSoftTech
Mail to: mailto:business@beleaftechnologies.com

https://codepen.io/robooneus/pen/ANgJJm

htmlllllll
<div class="click-mobile" id="menu">
  	<span></span>
</div>
jsssssssssss
//
// This is The Scripts used for Simply Theme
//
function main() {
  (function () {
    'use strict'
    //Script
    //-----------------------------------
    $(document).ready(function($){
      $('.click-mobile').click(function(){
        $('.head-menu').slideToggle(400);
        return false;
      });

      $("#menu").click(function () {
        $(this).toggleClass("active");
        $(".head-menu").toggleClass("active");
      });
    });

  }());
}
main();
csssssssssssssssssssssssssssss
/* click-mobile */
.click-mobile{
    position: relative;
    display: none;
	cursor: pointer;
}

.click-mobile span{
    background: #2a2929;
    height: 4px;
    width: 41px;
    display: block;
    position: relative;
    border-radius: 1px;
}

.click-mobile::before,
.click-mobile::after{
    content: ' ';
    height: 4px;
    width: 41px;
    background: #2a2929;
    position: absolute;
    transition: transform 0.3s;
    border-radius: 1px;
}

.click-mobile::before{
    top: -8px;
}

.click-mobile::after{
    bottom: -8px;
}

.click-mobile.active span {
    visibility: hidden;
    transition: transform 0.3s;
}

.click-mobile.active::before,
.click-mobile.active::after {
  content: "";
  position: absolute;
  width: 41px;
  height: 4px;
  background: #2a2929;
  top: 0;
  left: 0;
  transition: transform 0.3s;
  border-radius: 1px;
}

.click-mobile.active::before {
  transform: rotate(45deg);
}

.click-mobile.active::after {
  transform: rotate(-45deg);
}
/*  */
@media(max-width: 767px){
	.click-mobile{
		display: block;
		position: absolute;
		right: 20px;
		top: 50%;
		transform: translateY(-50%);
		z-index: 9;
	}
	.head-menu{
		display: none;
	}
	.head-menu{
		position: absolute;
		top: 120px;
		left: 0;
		width: 100vw;
		height: 100vh;
		background: #fff;
		z-index: 10;
	}
	.head-menu ul{
		flex-direction: column;
		padding: 30px 20px;
	}
	nav.head-menu ul li a{
		text-align: center;
		margin: 0;
		padding: 5px 0;
		display: block;
	}
	nav.head-menu ul li a::after{
		display: none;
	}
}

<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 100" width="100" height="100">
  <!-- Metà colorata (gold) -->
  <clipPath id="left-half">
    <rect x="0" y="0" width="50" height="100"/>
  </clipPath>
  <polygon points="50,5 61,35 95,35 67,57 78,90 50,70 22,90 33,57 5,35 39,35" fill="#FFD700" clip-path="url(#left-half)"/>
  
  <!-- Metà bianca -->
  <clipPath id="right-half">
    <rect x="50" y="0" width="50" height="100"/>
  </clipPath>
  <polygon points="50,5 61,35 95,35 67,57 78,90 50,70 22,90 33,57 5,35 39,35" fill="#FFFFFF" clip-path="url(#right-half)"/>
</svg>

<br>

<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 100" width="100" height="100">
  <polygon points="50,5 61,35 95,35 67,57 78,90 50,70 22,90 33,57 5,35 39,35" fill="#FFD700"/>
</svg>

{% if request.path[1] == 'product' and request.path[2] > '' %}
				    {% set ky = product.categoryUID|keys %}

				    {% set keyToPrint = '' %}
				    {% set countChildren = 0 %}
				    {% set countChildrenOld = 0 %}
				    {% for key,id in product.categoryUID %}
			            {% set countChildrenOld = countChildren %}
				        {% set cat = getCategory(id) %}
				        {% if cat.parentUID > 0 %}
				            {% set countChildren = countChildren + 1 %}
				            {% set c1 = getCategory(cat.parentUID) %}
                            {% if c1.parentUID > 0 %}
    				            {% set countChildren = countChildren + 1 %}
    				            {% set c2 = getCategory(c1.parentUID) %}
    	                        {% if c2.parentUID > 0 %}
        				            {% set countChildren = countChildren + 1 %}
        				            {% set c3 = getCategory(c2.parentUID) %}
        	                        {% if c3.parentUID > 0 %}
            				            {% set countChildren = countChildren + 1 %}
            				            {% set c4 = getCategory(c3.parentUID) %}
            				            {% if c4.parentUID > 0 %}
                				            {% set countChildren = countChildren + 1 %}
                				        {% endif %}
            				        {% endif %}
        				        {% endif %}
    				        {% endif %}
				        {% endif %}
				        {% if countChildren > countChildrenOld %}
				            {#% set countChildren = 0 %#}
				            {% set keyToPrint = id %}
				        {% endif %}
				    {% endfor %}
				    {% if countChildren == 0 and countChildrenOld == 0 %}
				        {% set keyToPrint = product.categoryUID[ky|last] %}
				    {% endif %}
				    
				    {#
                    {% set last_key = ky|last %}
					{% set category_id = product.categoryUID[last_key] %}
					{% set js_cat_path = '' %}
					
					{% set list = getCategoryList(category_id) %}
					#}
					
					{% set list = getCategoryList(keyToPrint) %}
					{% for k,category in list %}
						{% if category.name != '' and not category.disabled %}
							<li  itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem">
							    <a href="{% if canonical %}{{getSpokenCategoryUrl(category.uid,category.slug)}}{%else%}{{getCategoryURL(category.uid)}}{% endif %}" itemprop="item"> {{ category.name }} </a>
							    <meta itemprop="position" content="{{ (k+3) }}" />
								<meta itemprop="name" content="{{ category.name | escape('html') }}" />
								{% set js_cat_path = js_cat_path ~ ' | ' ~ category.name %}
							</li>
						{% endif %}
					{% endfor %}
					<li  itemprop="itemListElement" itemscope itemtype="https://schema.org/ListItem">
						<a href="{% if canonical %}{{ getSpokenProductUrl(request.path[2],product.slug)}}{%else%}/product/{{request.path[2] }}{% if request.path[3] != ''%}/{{request.path[3] }}{% endif %}{% endif %}" itemprop="item">
							<meta itemprop="name" content="{{ product.title | escape('html')}}" />{{ product.title }}
							<meta itemprop="position" content="{{ k + 4 }}" />
						</a>
					</li>
				{% endif %}

Task 6:
1. AWS Identity and Access Management (IAM) Task
2. Introduction to Amazon Relational Database Service (RDS) - SQL Server)

AWS Identity and Access Management (IAM) is a web service that enables Amazon Web Services (AWS) customers to manage users and user permissions in AWS. With IAM, you can centrally manage users, security credentials such as access keys, and permissions that control which AWS resources users can access.

Task1: Creating Users:
Step 1: Sign in to the AWS Management Console
Go to the AWS Management Console at https://aws.amazon.com/console/.
Sign in with your AWS account credentials.
Step 2: Navigate to the IAM (Identity and Access Management) Service
In the AWS Management Console, search for IAM in the search bar or find it under the Security, Identity, & Compliance category.
Click on IAM to open the IAM dashboard.
Step 3: Create a New User
In the IAM dashboard, click on Users in the left-hand menu.
Click on Create User
Step 4: Configure the User Details
Enter the User name :User1
Under Select AWS access type, check AWS Management Console access.
For Console password, choose Custom password (You create a password for the User1 as User1@123).
Uncheck   Require password reset to force the user to change their password upon first login.
. Step 5: Set Permissions
Click Next: Permissions.
Choose the following options to set permissions for the user:
Attach existing policies directly: Select policies that define the permissions for the user.
Step 6: Review and Create the User
Click Next: Tags to add optional tags for the user.
Click Next: Review to review the user's details and permissions.
Click Create user to finalize the process.
Click on download .csv file 
 Repeat above steps  for to create User2 and User3
Task 2: Create UserGroups
(a) Create “EC2-Admin”  UserGroup
Step 1: Navigate to the IAM (Identity and Access Management) Service
In the AWS Management Console, search for IAM in the search bar or find it under the Security, Identity, & Compliance category.
Click on IAM to open the IAM dashboard.
Step 2: Create a New User Group
In the IAM dashboard, click on User groups in the left-hand menu.
Click on Create group.
Step 3: Configure the Group Details
Enter EC2-Admin as the Group name.
Click Create group to create the group without attaching any policies at this step.
Step 4: Attach an Inline Policy to the Group
In the User groups list, click on the EC2-Admin group name.
Click on the Permissions tab.
Click Add permissions and then select Create inline policy.
Step 5: Define the Inline Policy
In the Create policy editor, switch to the JSON tab.
Paste the following policy JSON to allow view, start, and stop access to EC2 instances:
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:DescribeInstances",
                "ec2:DescribeImages",
                "ec2:DescribeVolumes",
                "ec2:DescribeTags",
                "ec2:DescribeSecurityGroups",
                "ec2:DescribeKeyPairs",
                "ec2:DescribeSnapshots"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "ec2:StartInstances",
                "ec2:StopInstances"
            ],
            "Resource": "arn:aws:ec2:*:*:instance/*"
        }
    ]
}

Step 7: Name and Attach the Policy
Enter a name for the policy, such as EC2-ViewStartStopAccess.
Click Create policy to attach it to the group.
Step 8: Add Users to the Group
In the EC2-Admin group page, click on the Users tab.
Click Add users.
Select the User3 to add to this group.
Click Add users to finalize the process.

(b) Create “EC2-Support”  UserGroups
Step 1: Navigate to the IAM Service
In the AWS Management Console, search for IAM in the search bar or find it under the Security, Identity, & Compliance category.
Click on IAM to open the IAM dashboard.
Step 2: Create a New User Group
In the IAM dashboard, click on Groups in the left-hand menu.
Click on Create New Group.
Step 3: Configure the Group Details
Enter EC2-Support as the Group Name.
Click Next Step to proceed.
Step 4: Attach a Policy to the Group
On the Attach Policy page, use the search bar to find the AmazonEC2ReadOnlyAccess policy.
Select the checkbox next to AmazonEC2ReadOnlyAccess.
Click Next Step to continue.
Step 5: Review and Create the Group
Review the group's name and attached policies.
Click Create Group to finalize the process.
Step 6: Add Users to the Group (Optional)
To add users, go to the Groups section, select EC2-Support, click on the Group Actions dropdown, and choose Add Users to Group.
Select the User2 and click Add Users.
(c) Create “S3-Support”  UserGroup
Step 1: Navigate to the IAM (Identity and Access Management) Service
In the AWS Management Console, search for IAM in the search bar or find it under the Security, Identity, & Compliance category.
Click on IAM to open the IAM dashboard.
Step 2: Create a New User Group
In the IAM dashboard, click on User groups in the left-hand menu.
Click on Create group.
Step 3: Configure the User Group Details
In the Group name field, enter S3-Support.
Click Next.
Step 4: Attach the S3 Read-only Access Policy
On the Attach policies page, search for AmazonS3ReadOnlyAccess.
Check the box next to the AmazonS3ReadOnlyAccess policy to grant the group read-only access to Amazon S3.
Click Next.
Step 5: Review and Create the Group
Review the group name and attached policy on the Review page.
Click Create group to finalize the process.
Step 6: Add Users to the Group (Optional)
In the User groups page, click on the S3-Support group you just created.
Click on the Users tab.
Click Add users.
Select the User1 to add to this group, Click Add users.
Task 3: Create EC2 Instance named “MyServer” with Linux OS Image 
Task 4: Create S3 bucket and add some files to bucket
Task 5: Sign-In and Test Users
1. In the navigation pane on the left, choose Dashboard.
A Sign-in URL for IAM users in this account link is displayed on the right. It will look similar to: https://123456789012.signin.aws.amazon.com/console
This link can be used to sign-in to the AWS Account you are currently using.
Copy the Sign-in URL for IAM users in this account to a text editor.


2. Open a private (Incognito) window.


Choose the ellipsis at the top-right of the screen
Select New Incognito Window
3. Paste the IAM users sign-in link into the address bar of your private browser session and press Enter.
Sign-in with:
IAM user name: User1
Password:User1@123
4. In the search box to the right of Services, search for and choose S3 to open the S3 console.
Choose the name of the bucket that exists in the account and browse the contents.
Since your user1 is part of the S3-Support Group in IAM, they have permission to view a list of Amazon S3 buckets and the contents.
Now, test whether they have access to Amazon EC2.
 5. In the search box to the right of Services, search for and choose EC2 to open the EC2 console.
In the left navigation pane, choose Instances.
You cannot see any instances. Instead, you see a message that states You are not authorized to perform this operation. This is because this user has not been granted any permissions to access Amazon EC2.
6. At the top of the screen, choose User1
Choose Sign Out
7. Now sign-in as User2, who has been hired as your Amazon EC2 support person.
Paste the IAM users sign-in link into your private browser tab's address bar and press Enter.
Sign-in with:
IAM user name: User2
Password:User2@123 
8. In the search box to the right of Services, search for and choose EC2 to open the EC2 console.
In the navigation pane on the left, choose Instances.
You are now able to see an Amazon EC2 instance “MyServer” because you have Read only permissions. 
However, you will not be able to make any changes to Amazon EC2 resources.
9. Select the instance named  ”MyServer”
In the Instance state menu above, select Stop instance.
In the Stop Instance window, select Stop.
You will receive an error stating You are not authorized to perform this operation. This demonstrates that the policy only allows you to view information, without making changes.
Choose the X to close the Failed to stop the instance message.
10. Next, check if User-2 can access Amazon S3.
In the search box to the right of Services, search for and choose S3 to open the S3 console.
You will see the message “You don't have permissions to list buckets” because User2 does not have permission to access Amazon S3.
At the top of the screen, choose User-2
Choose Sign Out
11. You will now sign-in as User3, who has been hired as your Amazon EC2    administrator.
Sign-in with:
IAM user name: User3
Password: User3@123
 12. In the search box to the right of Services, search for and choose EC2 to open the EC2 console.
In the navigation pane on the left, choose Instances.
As an EC2 Administrator, you should now have permissions to Stop the Amazon EC2 instance.
13. Select the instance named “MyServer”
In the Instance state menu, choose Stop instance.
In the Stop instance window, choose Stop.
The instance will enter the stopping state and will shutdown.

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":xero-boost: Boost Days for the last week of Jan :xero-boost:"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":calendar-date-29: Wednesday, 29th January",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n:coffee: *Café Partnership*: Enjoy coffee and café-style beverages from our cafe partner, *Adoro*, *8:00AM - 11:30AM*.\n:lunch: *Lunch*: Provided by *Mitzi and Twinn* *12:30PM - 1:30PM* in the Kitchen."
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":calendar-date-30: Thursday, 30th January",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":coffee: *Café Partnership*: Enjoy coffee and café-style beverages from our cafe partner, *Adoro*, *8:00AM - 11:30AM*. \n:lunch: *Breakfast*: Provided by *Roam* *9:30AM - 10:30AM* in the Kitchen. \n:celebrate_: *Social Happy Hour*: Kick off 2025 with our first social happy hour 🎉 join us from *4PM to 5:30PM*. We can't wait to see you there (more details to come)."
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "Stay tuned to this channel for more details, check out the <https://calendar.google.com/calendar/u/0?cid=eGVyby5jb21fbXRhc2ZucThjaTl1b3BpY284dXN0OWlhdDRAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ|*Hawkes Bay Social Calendar*>, and get ready to Boost your workdays!\n\nWX Team :party-wx:"
			}
		}
	]
}

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;

class TestController extends Controller
{
    /**
     * Display a listing of the resource.
     */
    public function index()
    {
        //
    }
    /**
     * Show the form for creating a new resource.
     */
    public function create()
    {
        //
    }
    /**
     * Store a newly created resource in storage.
     */
    public function store(Request $request)
    {
        //
    }
    /**
     * Display the specified resource.
     */
    public function show(string $id)
    {
        //
    }
    /**
     * Show the form for editing the specified resource.
     */
    public function edit(string $id)
    {
        //
    }
    /**
     * Update the specified resource in storage.
     */
    public function update(Request $request, string $id)
    {
        //
    }
    /**
     * Remove the specified resource from storage.
     */
    public function destroy(string $id)
    {
        //
    }
}

$objectManager =  \Magento\Framework\App\ObjectManager::getInstance();        
 
$paymentHelper = $objectManager->get('Magento\Payment\Helper\Data');
$allPaymentMethods = $paymentHelper->getPaymentMethods();
$allPaymentMethodsArray = $paymentHelper->getPaymentMethodList();
 
var_dump($allPaymentMethodsArray);
var_dump($allPaymentMethods);
 
$paymentConfig = $objectManager->get('Magento\Payment\Model\Config');
$activePaymentMethods = $paymentConfig->getActiveMethods();
 
var_dump(array_keys($activePaymentMethods));
 
$orderPaymentCollection = $objectManager->get('\Magento\Sales\Model\ResourceModel\Order\Payment\Collection');
$orderPaymentCollection->getSelect()->group('method');
$paymentMethods[] = array('value' => '', 'label' => 'Any');
foreach ($orderPaymentCollection as $col) { 
    $paymentMethods[] = array('value' => $col->getMethod(), 'label' => $col->getAdditionalInformation()['method_title']);            
}        

class Main {
    public static void main(String[] args) {
        int arr[] = {1,2,3,4,5,6,7,7};
        int n = arr.length;//length of the array is n
        int largest = arr[n - 1];// the largest is always the last in sorted array 
        int second_largest = 0;// initaialze a variable for 2nd 

        for (int i = n - 2; i >= 0; i--) {
            if (arr[i] != largest) {
                second_largest = arr[i];
                break;
            }
        }

        System.out.println("the second largest element:-"+second_largest);
    }
}
        
//required logic in case of array size < 2 
//        if(arr.length<2){// u can use n variable (n<2)
//            System.out.println(" no element in array");
//            return ;
//        }


//for loop:-
// iteration from the first or second last element , as we are searching for second largest no need to search for n-1 , but we can start from n-1 also , no problem 


//This is a brute force approach 
//TC=O(nlogn) due to sorting taking more time
//SC=O(n)

1. Selecciona un Editor WYSIWYG
Algunos editores populares que puedes integrar manualmente son:
TinyMCE: Sitio oficial
CKEditor: Sitio oficial
Quill: Sitio oficial
Para este ejemplo, usaremos TinyMCE, pero el proceso es similar para otros editores.
2. Descarga la Librería
Puedes descargar los archivos del editor desde su sitio oficial o usar un CDN.
TinyMCE (CDN):
Agrega el siguiente script en tu vista:

<script src="https://cdn.tiny.cloud/1/no-api-key/tinymce/6/tinymce.min.js" referrerpolicy="origin"></script>

3. Crea el Campo de Texto
En tu vista, define un campo de texto normal (por ejemplo, un textarea) donde se aplicará el editor.

<?= $form->field($model, 'asunto')->textarea(['id' => 'editor', 'rows' => 6]) ?>

4. Inicializa el Editor
Agrega un bloque de JavaScript para inicializar el editor en el campo de texto. Esto lo puedes hacer directamente en la vista o en un archivo JS separado.
Ejemplo con TinyMCE:

<?php
$this->registerJs(<<<JS
    tinymce.init({
        selector: '#editor', // Selecciona el textarea por su ID
        language: 'es', // Idioma del editor
        plugins: 'advlist autolink lists link image charmap print preview anchor searchreplace visualblocks code fullscreen insertdatetime media table paste help wordcount',
        toolbar: 'undo redo | formatselect | bold italic backcolor | alignleft aligncenter alignright alignjustify | bullist numlist outdent indent | removeformat | help',
        menubar: false, // Opcional: Oculta la barra de menús
        height: 300, // Altura del editor
    });
JS);
?>

  uarda los Datos en el Modelo
El contenido del editor se enviará como parte del formulario al servidor, ya que TinyMCE reemplaza el contenido del textarea. No necesitas hacer nada especial para procesar los datos en Yii2; simplemente accede al atributo del modelo como lo harías normalmente:

if ($model->load(Yii::$app->request->post()) && $model->save()) {
    // Procesa y guarda los datos del modelo
}

6. Opcional: Personalización Avanzada
Puedes personalizar aún más el editor añadiendo opciones específicas según tus necesidades:
Subida de Imágenes:
Configura una URL para manejar la subida de imágenes.
Barra de Herramientas Personalizada:
Ajusta qué botones aparecen en la barra de herramientas.
Estilos CSS:
Aplica estilos personalizados al contenido usando content_css.
Ejemplo con Subida de Imágenes:

tinymce.init({
    selector: '#editor',
    plugins: 'image code advlist autolink lists link image charmap print preview anchor searchreplace visualblocks code fullscreen insertdatetime media table paste help wordcount',
    toolbar: 'undo redo | formatselect | bold italic backcolor | alignleft aligncenter alignright alignjustify | bullist numlist outdent indent | removeformat | help| link image | code',
    images_upload_url: '/path/to/upload-handler', // Ruta a tu controlador para subir imágenes
  	 menubar: false, // Opcional: Oculta la barra de menús
     height: 300, // Altura del editor
});

Conclusión
Integrar un editor WYSIWYG manualmente te da flexibilidad y control total sobre su configuración. Aunque requiere algo más de trabajo que usar un widget preconfigurado, es una opción ideal si prefieres evitar dependencias externas o necesitas personalización avanzada.
Si tienes dudas específicas sobre cómo implementar algo (como subir imágenes o configurar estilos), no dudes en preguntar. ¡Estoy aquí para ayudarte! 😊