Planning SCI paper draft on tobacco resistance

To help with the paper, I'll need the full content of the PPT, since part of it is truncated in the search. I can gather a rough draft incorporating methods like transcriptome QC, PCA clustering, differential expression, and metabolomics, wrapping up with integrative analysis. I'll use detailed citations from the PPT once I access the full text.

Refining file search for line numbers

I noticed the snippet from file_search doesn’t include line numbers, but the file_search result shows a reference. It looks like I might need to search within the uploaded file directly. I’ll use a refined search to get those line numbers and work with the results in analysis.

{"queries":["+(烟草) +(黑胫病) 转录组 代谢组 关联分析 WGCNA 品种 湘烟7号 红花大金元","黑胫病 转录组 代谢组"],"source_filter":["files_uploaded_in_conversation"]}

Getting full ppt content, including images

I need the full content of the PPT, especially the figures, to accurately describe results like DEG counts and pathways. The extracted text is too minimal. My options include trying to access more content using the regular search method or parsing the PPT with Python to extract both text and images, potentially leveraging built-in vision tools for analysis.

Extracting images from PPTX slides

I plan to use Python to open the PPTX, then extract images from the slides. I could either convert slides to PNG via LibreOffice or employ another strategy with 'soffice'. I'll check if LibreOffice is available in the container before proceeding, ensuring it's the most efficient method.

bash -lc ls -l /mnt/data && file /mnt/data/1.14烟草.pptx

Checking if LibreOffice is available

I need to check if LibreOffice is available in the environment. If it's there, I can use it to extract images from the PPT slides. I'll just quickly look to see whether it's installed before moving ahead with the extraction process.

bash -lc soffice --version || libreoffice --version || echo no_office

Converting PPTX and working with images

I’ll first convert the PPTX to a PDF or images. Since screenshots are not supported inside the container, I’ll try converting them to images and then use the container to open those images. We can interpret manually using the built-in vision to examine charts and text without needing OCR.

bash -lc mkdir -p /mnt/data/ppt_conv && libreoffice --headless --convert-to pdf --outdir /mnt/data/ppt_conv '/mnt/data/1.14烟草.pptx' && ls -l /mnt/data/ppt_conv

Checking for possible PDF creation

I’ve encountered an unknown I/O error, but there’s a chance that the PDF might have been generated despite that. I’ll list the files available to check if the PDF is there, which can help me decide if I can proceed with the next steps.

bash -lc ls -l /mnt/data/ppt_conv || true

Fixing LibreOffice conversion issue

The PDF wasn’t created, likely due to issues with Chinese characters in the path or permissions during the LibreOffice conversion. I’ll move the PPTX file to a path with only ASCII characters and try the conversion again to see if that resolves the problem!

bash -lc cp '/mnt/data/1.14烟草.pptx' /mnt/data/tobacco.pptx && libreoffice --headless --nologo --nofirststartwizard --convert-to pdf --outdir /mnt/data/ppt_conv /mnt/data/tobacco.pptx && ls -l /mnt/data/ppt_conv

Exploring font or permissions issue

The conversion still isn’t working. It might be a font issue, or perhaps write permissions aren’t fully in place. I could try converting to PNG per slide, which might avoid this problem. A better approach might be using --convert-to png and specifying --infilter. Let’s give that a go!

bash -lc rm -rf /mnt/data/ppt_png && mkdir -p /mnt/data/ppt_png && libreoffice --headless --nologo --nofirststartwizard --convert-to 'png' --outdir /mnt/data/ppt_png /mnt/data/tobacco.pptx && ls -l /mnt/data/ppt_png | head

Exploring PNG output issue

It looks like LibreOffice might only be generating a single PNG (likely from the first slide), instead of one per slide. The --convert-to png option might create multiple files like tobacco.png, tobacco_1.png, but that’s not happening. I wonder if adding "--convert-to png" would fix it, so I’ll check for _1 instead.

bash -lc ls -l /mnt/data/ppt_png

Investigating single PNG output issue

The export might be failing — it’s possible that LibreOffice headless can't open the presentation properly and is only producing a blank PNG, or maybe the presentation only has a single slide. Let’s check the tobacco.png file and see what’s happening. I’ll verify if that’s the case.### Investigating multipage export issue

The first slide was exported, but multiple slides aren't showing up; maybe the export only captures the first one due to some issue with multiple pages. I’ll try specifying different output options or possibly use Python libraries to render the slides. If LibreOffice can't handle multipage in headless mode, I’ll check the user profile settings and try that workaround with a writable directory. Let's give it a shot!

bash -lc rm -rf /tmp/lo_profile /mnt/data/ppt_conv && mkdir -p /mnt/data/ppt_conv && libreoffice --headless --nologo --nofirststartwizard -env:UserInstallation=file:///tmp/lo_profile --convert-to pdf --outdir /mnt/data/ppt_conv /mnt/data/tobacco.pptx && ls -l /mnt/data/ppt_conv | head

bash -lc ls -l /mnt/data/ppt_conv && file /mnt/data/ppt_conv/tobacco.pdf || true

Considering writing a draft for the user

I wonder if the issue with exporting the presentation is due to its large size or many images. Maybe we should convert it to an ODP file first. The user asked for a manuscript draft, so I can structure it with placeholders for numeric data and general statements about the study's methods and results.

Drafting the manuscript structure

For the Results section, I’ll note observations like healthier plants at 4 days post-inoculation and lower disease incidence at 8 days. The transcriptome QC results are good with high R² values, and I’ll mention PCA clustering for both transcriptome and metabolome. However, for missing data like specific values, I’ll use placeholders like "xxx" or "[待补充]." I’ll also outline analysis methods like DEGs identification and KEGG enrichment but clarify that thresholds and parameters are assumptions for now.

Ensuring accuracy and transparency

We need to follow trust requirements and avoid fabricating data, so I’ll write the draft with placeholders where needed. While I considered extracting slide images or data from charts, they’re likely to be images, not raw data. I'll look into using tools like pptx2pdf or unoconv for conversion to see if they help.

bash -lc unoconv -V || echo no_unoconv

Trying alternative solutions for slide export

I considered using the python-pptx library with spire, but that's not promising. Converting via ImageMagick (convert pptx) might not work either. I’ve already tried using soffice --convert-to 'pdf:impress_pdf_Export', but it failed. Let’s try exporting to odp instead and see if it works for multiple slides.

bash -lc rm -rf /mnt/data/ppt_conv && mkdir -p /mnt/data/ppt_conv && libreoffice --headless --nologo --nofirststartwizard --convert-to odp --outdir /mnt/data/ppt_conv /mnt/data/tobacco.pptx && ls -l /mnt/data/ppt_conv && file /mnt/data/ppt_conv/tobacco.odp || true

Success with ODP conversion

The conversion to ODP worked, even though there was an error. I’m thinking the PDF conversion might have failed due to a filter issue. Since ODP is working, I’ll try converting it to PDF now, hoping for success.

bash -lc libreoffice --headless --nologo --nofirststartwizard --convert-to pdf --outdir /mnt/data/ppt_conv /mnt/data/ppt_conv/tobacco.odp && ls -l /mnt/data/ppt_conv | head

Checking PDF creation

I need to check if the PDF was successfully created from the ODP. This will help confirm the process worked as intended, and I’ll know if I can proceed further or if any additional steps are required. Let’s take a look!

bash -lc ls -l /mnt/data/ppt_conv | grep pdf || true && file /mnt/data/ppt_conv/tobacco.pdf || true

Attempting to convert ODP to images

The conversion to PDF didn’t work, so now I’ll try converting the ODP file to images in PNG format. Hopefully, this method will output multiple images representing the slides, which might be a more effective approach for what I need. Let's see how this goes!

bash -lc rm -rf /mnt/data/ppt_png2 && mkdir -p /mnt/data/ppt_png2 && libreoffice --headless --nologo --nofirststartwizard --convert-to png --outdir /mnt/data/ppt_png2 /mnt/data/ppt_conv/tobacco.odp && ls -l /mnt/data/ppt_png2 | head -n 20

Trying different export formats for multi-page output

The single image output persists even with --convert-to "png". I wonder if it's a bug or tied to the type of document. I’ll test the --convert-to "png:draw_png_Export" and check if specifying the correct options like --infilter=impress8 helps. I expect individual slide images if successful! Let's try it.

Exploring multiple conversion options for slides

I’m considering different methods for converting presentation files to images or PDFs. I could use the --convert-to "png:impress_png_Export" or Python libraries like pptx, but I need to assess issues like page size or path length. I'll inspect LibreOffice logs to gather more information and identify the root cause.

Troubleshooting failed export and seeking workarounds

It seems the export starts but fails, possibly due to missing fonts or unsupported objects causing a crash. We managed to get the first slide, but to avoid the crash, I’m thinking of trying separate slide exports. I’ll explore using python-pptx to extract the slides, text, and images for a more manageable export.

Testing methods to extract and interpret slide content

I’m thinking we could use python-pptx to extract image data from charts and figures. Each image includes a blob that we can save and inspect. To interpret numeric values, vision tools could help read text on the images. I’ll test exporting slides via LibreOffice and possibly converting them to svg.

bash -lc rm -rf /mnt/data/ppt_svg && mkdir -p /mnt/data/ppt_svg && libreoffice --headless --nologo --nofirststartwizard --convert-to svg --outdir /mnt/data/ppt_svg /mnt/data/tobacco.pptx && ls -l /mnt/data/ppt_svg | head

Checking if SVG includes multiple pages

The SVG file is quite large now — it could be that it contains multiple slides. I’ll open it up to see if it’s truly multi-page or if something else contributed to its size. Let's check and inspect the content of the file more closely.

bash -lc head -n 20 /mnt/data/ppt_svg/tobacco.svg

Exploring how to handle the large SVG file

The SVG contains all 29 slides, potentially allowing for easy navigation through SMIL, but I need separate images for each slide. Instead of manually extracting them, we can use Python and tools like cairosvg to split and render each slide. I'll first check if cairosvg is installed before proceeding.

bash -lc python -c "import cairosvg,sys;print('cairosvg',cairosvg.version)" || echo no_cairosvg

Planning the SVG extraction and conversion process

I’ll use Python to read the SVG, locate the slide groups using IDs like "id1," and create separate SVGs for each slide by including only that slide's group. Then I can convert these individual SVGs to PNG using cairosvg. Time to start coding and implement this solution.

from pathlib import Path svg_path = Path("/mnt/data/ppt_svg/tobacco.svg") text = svg_path.read_text(encoding="utf-8") len(text), text[:200]

Considering how to handle large SVG parsing

Parsing the SVG with lxml.etree might be heavy because of the size. It could slow things down. Maybe I should use an incremental approach to process it in chunks instead, but I’ll first try parsing the whole file to see if it works. Let's give it a shot!

from lxml import etree parser = etree.XMLParser(huge_tree=True, recover=True) tree = etree.parse(str(svg_path), parser) root = tree.getroot() root.tag, root.attrib.get('viewBox')

Locating the number of slides in the SVG

I’ll use root.xpath with appropriate namespaces to locate the metadata needed for the number of slides. First, I need to define the namespaces to ensure the XPath query works correctly. Once that’s set, I can fetch and count the slides systematically. Let's give it a try!

ns = {k:v for k,v in root.nsmap.items() if k} ns

Finding the slide groups

I’m searching for all <g> elements with the class "Slide." These should represent the different slides in the SVG. Once I locate them, I’ll be able to isolate each slide and work with the relevant data. Let’s focus on finding all the group elements with the class.

slides = root.xpath(".//svg:g[@class='Slide']", namespaces={'svg': 'http://www.w3.org/2000/svg'}) len(slides), slides[0].attrib.get('id'), slides[0].attrib.get('class')

Listing slide IDs

There are 30 slides in total, but one of them is just a dummy. I want to focus on the 29 actual slides, likely with IDs like id1 through id29. I’ll list the slide IDs, ensuring I exclude the dummy one. That should give me what I need.

slide_ids = [g.attrib.get('id') for g in slides] slide_ids[:10], slide_ids[-5:], len(slide_ids)

Planning SVG slide extraction

I plan to create a new SVG for each slide, including the necessary definitions and the slide group. I'll copy the original SVG, remove other slides, and keep the master slide definitions. The slide group might reference other elements, such as bitmaps, so I’ll ensure everything referenced is included. I’ll make sure to inspect the structure carefully for any references to masters or embedded elements.

Examining SVG slide group structure

In the SVG export from LO, each slide group probably contains all necessary elements for the slide, including references to <defs> for clip paths and embedded bitmaps. It may even use something like <g class="Master_Slide"> or <use xlink:href="#id31"> to include the master slide. I'll check the slide group structure.

import re

find whether slide group uses <use> referencing master

slide1 = slides[1] slide1_str = etree.tostring(slide1, encoding="unicode")[:5000] slide1_str[:500]

Considering inclusion of <use> element

I should keep scrolling to see if there's a <use> element present, as it may link to references like master slides or other reusable elements. I'll check how it's used and whether we need to include it in the structure for a complete export.

"use" in slide1_str, slide1_str.find("<use")

Preparing svg export and conversion

I’ve noticed no <use> elements in the first 5000 lines, which suggests the slide group likely contains everything. I'll proceed with including the whole slide group. There are embedded bitmaps as <image> tags inside it, so that's fine. I'll copy all <defs> (for clip paths and fonts) into a new root and include the slide group. Afterward, I'll split the file into 29 SVGs, then convert each to PNG, using the Python tool. Expecting the process to take about 45 seconds per slide; issue should be manageable.

from copy import deepcopy from pathlib import Path out_dir = Path("/mnt/data/ppt_slides_png") out_dir.mkdir(exist_ok=True)

gather defs and style elements at top-level root children excluding slide groups?

top_children = list(root)

find defs elements

defs_elems = [child for child in top_children if child.tag.endswith('defs')] len(defs_elems)

Building SVG function

There are 14 <defs> to include. So, I’ll build a function to create the SVG. It will handle the required definitions and ensure proper structure. This will provide the foundation for the transformation, ensuring all elements like clip paths and fonts are preserved correctly. Let's proceed.

import cairosvg, os, math, time svg_ns = "http://www.w3.org/2000/svg" def make_slide_svg(slide_elem): new_root = etree.Element(root.tag, nsmap=root.nsmap) # copy root attributes for k,v in root.attrib.items(): new_root.set(k,v) # append defs for d in defs_elems: new_root.append(deepcopy(d)) # Some exports include <style> elements outside defs - check for child in top_children: if child.tag.endswith('style'): new_root.append(deepcopy(child)) # append slide itself new_root.append(deepcopy(slide_elem)) return etree.tostring(new_root, encoding="utf-8", xml_declaration=True, doctype='<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">')

test convert slide 1 to png

test_svg = make_slide_svg(slides[1]) tmp_svg_path = out_dir/"slide1.svg" tmp_svg_path.write_bytes(test_svg) png_path = out_dir/"slide1.png" cairosvg.svg2png(bytestring=test_svg, write_to=str(png_path), output_width=1920) png_path, png_path.exists(), png_path.stat().st_size

Opening the task

Alright, now let's move forward and open what we need. It’s time to start working directly with the details and make sure we have everything ready for the next steps. Let's proceed carefully to avoid missing anything important.### Handling missing fonts during rendering

Uh-oh, the Chinese characters are rendering as boxes due to a font mismatch during svg-to-png conversion. Interestingly, the png export from LibreOffice worked fine for Chinese, but now we’re facing issues with cairosvg. Perhaps we can find a way to include fonts properly in the conversion process. I'll need to explore font substitution or directly converting slides in LO.

Exploring export options with fonts

I’m considering using LibreOffice to export each slide individually as PNG, possibly with the --show and --norestore options. Alternatively, I could try using Python UNO to automate this. Another option is using Inkscape, but I still might face font issues. I'll need to check the installed fonts list.

bash -lc fc-list | grep -i 'noto.*cjk' | head || true; fc-list | grep -i 'simsun' | head || true; fc-list | grep -i 'wenquanyi' | head || true

Inspecting fonts in SVG

I suspect the issue with the boxes comes from fonts in the SVG that aren't available and croppers like cairosvg might lack fallback options. The missing CJK characters could be fixed by replacing the font family in the SVG to a standard one, like Noto Sans CJK SC. I’ll check the SVG’s font families to confirm.

slide1_svg_path = out_dir/"slide1.svg" s = slide1_svg_path.read_text(encoding="utf-8")

find font-family occurrences

import re families = set(re.findall(r"font-family:'([^']+)'", s)) len(families), list(sorted(families))[:20]

Checking font-family in SVG

I think the font-family might not be correctly set in the SVG, possibly lacking quotes. I need to search the SVG for how the fonts are defined and verify if this is causing the issue. Let's search to see how the font-family is implemented.

families = set(re.findall(r"font-family="([^"]+)"", s)) len(families), list(sorted(families))[:20]

Adjusting font-family in the SVG

It looks like the SVG uses font-family values like "宋体 embedded" and "微软雅黑", which cairosvg may not recognize, even though Noto exists. I can try replacing these specific font-family values with "Noto Sans CJK SC" to see if it resolves the issue. Let's update the SVG and regenerate it.

def replace_fonts(svg_bytes): s = svg_bytes.decode('utf-8') for f in ["宋体 embedded","微软雅黑 embedded","微软雅黑"]: s = s.replace(f, "Noto Sans CJK SC") # also maybe replace 'Arial embedded' etc? keep return s.encode('utf-8')

svg_bytes = make_slide_svg(slides[1]) svg_bytes2 = replace_fonts(svg_bytes) png_path2 = out_dir/"slide1_fontfix.png" cairosvg.svg2png(bytestring=svg_bytes2, write_to=str(png_path2), output_width=1920) png_path2.stat().st_size

Rendering slides with font replacement

Now that the Chinese characters are showing correctly, the next step is to process all the slides (id1..id29) with the font replacements. I'll export them as PNG images, making sure the output has a width of around 2000. Once done, I'll inspect the slides for details like counts and pathways. Let's get started!

out_all_dir = Path("/mnt/data/ppt_slides_png_all") out_all_dir.mkdir(exist_ok=True) def export_all_slides(output_width=1920): slide_elems = [g for g in slides if g.attrib.get('id') not in ('dummy-slide', 'dummy-master-page')] paths=[] for idx, se in enumerate(slide_elems, start=1): svg_bytes = make_slide_svg(se) svg_bytes2 = replace_fonts(svg_bytes) png_path = out_all_dir / f"slide{idx:02d}.png" cairosvg.svg2png(bytestring=svg_bytes2, write_to=str(png_path), output_width=output_width) paths.append(png_path) return paths

paths = export_all_slides(1920) len(paths), paths[0], paths[-1]

Inspecting key slides for results

Great, now we'll go ahead and inspect the key slides to check the results. I'll start by opening a list of important ones—phenotype slide, transcriptome QC, PCA, volcano plots, enrichment, metabolomics, WGCNA, and so on. I'll open the slides one by one and iterate through quickly to gather the data.### Inspecting slide content

Slide 2 seems to have the correct background text, and it matches the file. As for slide 3, it might be related to materials, but I’ll need to check further. I’ll continue looking through the slides to verify the content and make sure everything aligns with expectations.### Inspecting slide details

Slide 3 appears to contain important details about the experimental materials and step 1 of the method. Now, moving on to slide 4—I’ll check if it continues with further steps or if it introduces something new. Let’s keep going to make sure everything looks right.### Checking slides for relevance

Slide 4 has method steps 2-5, including sampling times, which is useful. Slide 5 seems like it could be about glandular hair progress, but I’m not sure if it's directly related to what we need. Let’s review it to confirm if it’s relevant or not and proceed from there.### Reviewing slide details

Slide 5 discusses experimental design and includes important physiological/biochemical measures like SA, IAA, and JA, or their Chinese equivalents, which is beneficial for methods. Slide 6 seems to be about trichomes, but I’ll need to open it to confirm whether it’s directly relevant for the current focus.### Examining phenotype data and next slide

This slide displays the phenotype 4 days after inoculation and notes a decrease in 8D incidence. As for slide 7, it appears to be a transcriptome QC correlation heatmap, but I’ll need to open it to confirm whether it matches what’s expected for the analysis.### Reviewing slide 7 and moving to slide 8

Slide 7 includes a correlation matrix along with accompanying text. For slide 8, it seems like it might display a PCA plot, but I’ll need to verify this by opening it to see if it aligns with expectations for the analysis.### Reviewing PCA results and considering slide 9

This PCA plot shows that PC1 explains 15.48% of the variance, and PC2 explains 12.73%. That looks solid! Now, I'll move on to slide 9 to check what’s presented there and see if it’s relevant to the analysis or if it requires further focus.### Analyzing DEG count data from slide 9

Slide 9 includes a heatmap and a bar plot showing DEG counts for various comparisons like HD1 vs HDCK, HD2 vs HDCK, and others. I need to carefully extract the data from the bar chart. I'll zoom in by cropping the image and analyze it with Python to read the values more accurately.

from PIL import Image img = Image.open("/mnt/data/ppt_slides_png_all/slide09.png") img.size

Cropping chart to focus on relevant data

I think I’ll crop the right side of the chart to focus better on the important data. This will help me zoom in on the key areas and extract clearer insights from the visualization. It's a better approach to get the exact numbers I'm looking for.

import numpy as np, matplotlib.pyplot as plt, pandas as pd, textwrap, os, math img_arr = np.array(img)

approximate crop coordinates for chart right side

crop = img.crop((720, 110, 1850, 1000)) crop.size

Clarifying chart labels and zooming in

I’m reviewing the x-axis labels for the chart to make sure they reflect the comparisons correctly. There's a label "HD1 vs X7CK", which could refer to day 1 comparisons, but I’m getting a bit mixed up. I’ll zoom in by cropping the image to verify, saving the crop for further clarity.

crop_path="/mnt/data/ppt_slides_png_all/slide09_chart.png" crop.save(crop_path) crop_path

Finalizing comparison results and planning slide for volcano plot

I’ve successfully read the labels. I’ve compiled the results for all the comparisons: the "Up" and "Down" counts for different groups. I'll use this in the final results. For the next slide, I’m thinking a volcano plot might be appropriate. I’ll prepare for that next.### Decoding Venn diagram for DEG comparison

For Slide 10, the Venn diagram shows the overlap of DEGs between X7 vs CK and HD vs CK at each timepoint. I’ll interpret the numbers for each region: left-only, overlap, and right-only. The top numbers might represent up-regulated, and the bottom numbers down-regulated. I’ll focus on 1 day first. Let’s zoom in and crop the 1-day circle.

img10=Image.open("/mnt/data/ppt_slides_png_all/slide10.png")

crop around first venn (approx)

crop1=img10.crop((210,220,700,470)) crop1_path="/mnt/data/ppt_slides_png_all/slide10_venn1.png" crop1.save(crop1_path) crop1.size

Interpreting overlap in Venn diagram for up-regulated DEGs

For up-regulated DEGs (↑), the top row numbers are:

• X7-specific up: 1215
• Common up: 1731
• HD-specific up: 2294

It seems the overlap region (highlighted in orange) has two numbers: 1731 for the left side and 1733 for the right—perhaps due to rounding. The central overlap has two further numbers related to both up and down-regulated DEGs. This shows a combined Venn diagram for up and down regulation.

Clarifying the intersection in Venn diagram

In the overlap of the Venn diagram, each region shows two numbers for up and down-regulated genes, like 1731 for up and 2032 for down. The difference in numbers across the regions (1731 vs 1733, 2032 vs 2030) may indicate separate counts for X7 and HD, possibly splitting up "same direction" versus "opposite direction." This shows gene expression counts for both groups within the overlapping areas.

Reevaluating the Venn diagram interpretation

It seems there might be a misinterpretation in how the Venn diagrams are layered. They could have two separate Venn diagrams—one for up-regulated genes, one for down-regulated genes. In each region, there would be a corresponding number for up and down, but the anomaly with the numbers might arise from how they were overlapped or shifted.

Clarifying Venn diagram interpretation

It seems the circles might represent two separate Venn diagrams for up and down-regulated genes, slightly shifted to show overlapping counts. For up-regulated genes, the first number represents unique genes, and the second number represents shared genes between groups. This layout also applies to down-regulated genes.

Interpreting shared and unique DEGs

The slight difference in shared up-regulated counts (1731 vs. 1733) might be due to the directionality of fold change in each group. Shared DEGs can be up in one group and down in another. For a given time-point (like 1D), the total shared DEGs is 3763 for both X7 and HD, with unique DEGs depending on each variety.

2D venn in middle top

crop2=img10.crop((780,220,1270,470)) crop2_path="/mnt/data/ppt_slides_png_all/slide10_venn2.png" crop2.save(crop2_path) crop3=img10.crop((1350,220,1840,470)) crop3_path="/mnt/data/ppt_slides_png_all/slide10_venn3.png" crop3.save(crop3_path)

bottom venns: 8D and 12D maybe.

crop4=img10.crop((550,545,1040,795)) crop4_path="/mnt/data/ppt_slides_png_all/slide10_venn4.png" crop4.save(crop4_path) crop5=img10.crop((1110,545,1600,795)) crop5_path="/mnt/data/ppt_slides_png_all/slide10_venn5.png" crop5.save(crop5_path) crop2.size, crop4.size

Examining 2D numbers

For 2D, X7 unique up is 3374, and shared up is 3872. X7 unique down is 3404, and shared down for X7 is 2457. For HD, the shared up is 3861 and unique up is 1793, while shared down is 2468 and unique down is 2265. The shared total is 6329 for both.

crop3b=img10.crop((1200,200,1900,480)) crop3b_path="/mnt/data/ppt_slides_png_all/slide10_venn3b.png" crop3b.save(crop3b_path)

Reviewing the 4D numbers

For the 4D data, X7 has 2584 unique up and 5513 shared up. X7 unique down is 2612, and shared down for X7 is 3320. For HD, shared up is 5514 and unique up is 2966. HD shared down is 3319, and unique down is 3344, with the shared total being 8833.### Adding labels and adjusting crop

It seems I need to include labels, especially for the comparison between 8 and CK. I'll check if the full bottom-left section includes those labels. If not, I’ll recrop a larger portion of the bottom-left to make sure I capture everything needed.

crop4b=img10.crop((350,490,1150,840)) crop4b_path="/mnt/data/ppt_slides_png_all/slide10_venn4b.png" crop4b.save(crop4b_path)

Adjusting for missing labels

It seems the labels are still missing due to cropping. The bottom labels are actually under the diagrams, outside of the cropped area. To fix this, I’ll recrop the entire bottom section so it includes the labels and provides a complete view.

bottom=img10.crop((250,450,1700,1040)) bottom_path="/mnt/data/ppt_slides_png_all/slide10_bottom.png" bottom.save(bottom_path)

Reviewing 8D and 12D data

Now that I have the 8D and 12D numbers, for 8D: X7 unique up is 3070, shared up is 3514, X7 unique down is 4159, shared down is 2889. For HD in 8D, shared up is 3419, unique up is 5079, shared down is 2984, unique down is 4901. For 12D, totals are similar. Checking Slide11 next.### Adjusting for clearer visuals

Slide 11 contains GO enrichment bubble plots for various conditions, but the pathways are hard to read. I could zoom in on major terms or summarize. However, since key pathways are needed, I’ll extract top-enriched terms like "defense response." To address text clarity, I'll re-export the slide at a higher resolution—4000px width for better readability. I'll verify slide 11 corresponds to the 11th element.

Clarifying the slide index and resolution adjustment

Actually, the slides list includes a dummy and 29 real slides, so index slides[1] corresponds to the first slide (id1), and slides[11] corresponds to slide11.png. I'll generate higher resolution for slide11 and potentially others. To streamline, I'll create a function called export_slide_n(n, width).

def export_slide_png(n, width=4000): # n from 1..29 se = slides[n] # because slides[1]=id1 svg_bytes = replace_fonts(make_slide_svg(se)) out_path = out_all_dir / f"slide{n:02d}_{width}.png" cairosvg.svg2png(bytestring=svg_bytes, write_to=str(out_path), output_width=width) return out_path

hi11 = export_slide_png(11, 5000) hi11

Cropping and zooming into one plot

The text is still too small, but maybe cropping out one bubble plot will help with readability. Let's start by cropping the first plot for one day's specific X7 and see if that makes a difference. Maybe zooming in could make the labels more legible.

hiimg = Image.open(str(hi11))

Crop 1 day specific X7 plot (top left)

crop_go = hiimg.crop((1050, 360, 1650, 1250)) crop_go_path="/mnt/data/ppt_slides_png_all/slide11_go_1day_x7.png" crop_go.save(crop_go_path)

Reading and summarizing GO terms

We can now read the enriched terms. For biological processes, the focus is on stress responses, like heat, mycotoxin, chitin, and protein folding. Cellular components include thylakoid and plasma membrane, while molecular functions involve activities like chaperone binding and dehydrogenase. I'll focus on key pathways for the paper, such as defense responses and protein folding. Slide12 might offer a KEGG pathway overview.### Interpreting Venn diagrams and stable genes

Slide 12 shows Venn diagrams comparing gene expression across different timepoints (X7 vs DH). The key numbers are 343 genes consistently upregulated and 227 downregulated. The left Venn shows differentially expressed genes between varieties at each timepoint, and the center highlights genes common in all comparisons. We can use these as stable genotype-differentiating genes. Slide 13 may focus on KEGG pathways for these genes.### Interpreting enriched GO terms in Slide13

Slide 13 likely shows enriched GO terms such as responses to salicylic acid, chitin, jasmonic acid signaling, and processes like cell wall biogenesis and lignin biosynthesis. These terms highlight enriched defense signaling pathways and cell wall/lignin responses. I'll mention these in variety comparisons and cite appropriately. Slide 14 might contain metabolome PCA, so let's check that.### Interpreting Slide14 and Slide15

Slide 14 seems to show a metabolome correlation heatmap—great for visualizing relationships. Slide 15 might feature a metabolome PCA (Principal Component Analysis), which will be useful for understanding the variance in the data. Let's take a closer look at Slide 15 to confirm!### Analyzing Metabolome PCA and Slide16

The metabolome PCA looks solid with PC1 at 39.86% and PC2 at 18.88%. Now, looking at Slide 16, it seems likely to be a volcano plot showing metabolites—probably used to highlight significant changes between samples. Let's check it to confirm!### Analyzing Slide16 and Preparing Presentation

Slide 16 features a bar plot with DAMs counts across various comparisons. I'll need the exact numbers for better clarity. To make it easier to read, I'll crop the chart as we did earlier. This should help the user focus on the essential details without distractions.

img16=Image.open("/mnt/data/ppt_slides_png_all/slide16.png") chart16=img16.crop((720,150,1860,1030)) chart16_path="/mnt/data/ppt_slides_png_all/slide16_chart.png" chart16.save(chart16_path)

Clarifying X-axis labels and comparisons

Looks like there’s some inconsistency with the labels on the X-axis. After five "HD vs HDCK" comparisons, we should see "X71 vs X7CK" and so on, but it seems "HD12 vs X7CK" appears instead. This seems like a mislabeling issue that needs checking—I'll confirm all the X-axis labels to make sure they match the proper comparisons.

Clarifying 6th label confusion

It looks like the 6th label should indeed be "X71 vs X7CK," but it's showing up as "HD12 vs X7CK" instead, possibly due to overlapping bars or a missing space. After checking the order of comparisons, I confirmed the 6th bar corresponds to "X71 vs X7CK." I’ll include this in the final explanation.

Reviewing the comparison data

The comparison data for X72 to X712 versus X7CK and DHCK is in a good range – a mix of ups and downs with relevant values that could efficiently guide the analysis. Slide 17 might represent the DAM categories (e.g., HD-up), possibly as a bubble chart. I'll check on that.### Reviewing Venn diagram data for DAMs

The Venn diagram illustrates the overlap of DAMs across times for different conditions: HD-up, HD-down, X7-up, and X7-down. The central numbers seem to suggest: HD-up (161), HD-down (35), X7-up (92), and X7-down (12). This should be noted. Slide 18 might show pathways for metabolites, so I’ll check that next.### Reviewing KEGG pathway enrichment for metabolites

This slide showcases KEGG pathway enrichment in metabolite categories: HD-UP, HD-DOWN, X7-UP, and X7-DOWN. The pathways include Arachidonic acid metabolism, Phenylalanine metabolism, Plant secondary metabolites biosynthesis, Flavonoid biosynthesis, and Fatty acid biosynthesis. These are great for discussion. Slide 19 might explore WGCNA with hormones.### Clarifying content of slide 19

Wait, slide 19 seems to be under metabolomics. Actually, it looks like a Venn overlap of DAMs between HD and X7 at each timepoint. It likely compares "HD1 vs HDCK" vs "X71 vs X7CK," showing numbers like 204 unique to HD, 111 shared, and 39 unique to X7. It could represent shared differential metabolites. Slide 20 might follow.### Summarizing slide 20 and previewing slide 21

Slide 20 shows KEGG pathways for metabolites across HD, all, and maybe X7, but the text is small. We might not need to dive into details beyond the major pathways already highlighted. As for Slide 21, it seems to focus on WGCNA for the transcriptome and hormones—let’s keep this open to explore.### Describing Slide 21 and previewing Slide 22

Slide 21 presents a sample dendrogram and trait heatmap for various hormones like ABA, IAA, tZ, GA, JA, and others. We can cite these measurements. As for Slide 22, it appears to focus on the WGCNA module trait correlation heatmap—this could be important for contextualizing the data.### Describing Slide 22 and previewing Slide 23

Slide 22 has cluster dendrogram modules, which could help in understanding the relationships among the data. Moving on, Slide 23 might focus on the module-trait correlation heatmap—this could be useful in further analyzing how these modules relate to specific traits. It's a good follow-up!### Interpreting module-trait relationship heatmap and previewing slide 24

The heatmap shows module-trait relationships with hormones, but it's difficult to distinguish specific modules with strong correlations. We can mention modules significantly correlated with hormones like ABA, SA, JA, and others. Perhaps we'll highlight candidate modules, such as lavenderblush1. Slide 24 might show scatterplots for gene significance vs. module membership.### Analyzing module sizes and hub genes on Slide 24

Slide 24 presents modules like coral3, lightblue3, and lavender, detailing gene counts and hub genes: coral3 has 1009 genes with 279 hubs, lightblue3 has 185 genes and 40 hubs, and lavender has 1173 genes with 274 hubs. We could include GO enrichment for the module genes—zooming on the top terms for coral3 might work.

img24=Image.open("/mnt/data/ppt_slides_png_all/slide24.png")

crop right top plot area for coral3

crop24a=img24.crop((1180,120,1800,380)) path24a="/mnt/data/ppt_slides_png_all/slide24_coral3_go.png" crop24a.save(path24a)

crop middle plot for lightblue3

crop24b=img24.crop((1180,430,1800,700)) path24b="/mnt/data/ppt_slides_png_all/slide24_lightblue3_go.png" crop24b.save(path24b)

crop bottom plot for lavender

crop24c=img24.crop((1180,760,1800,1030)) path24c="/mnt/data/ppt_slides_png_all/slide24_lavender_go.png" crop24c.save(path24c)

Exporting Slide 24 for better resolution

The crop of Slide 24 is too small, and the text isn't readable. I’ll need to export it again at a higher resolution, setting the width to 5000 for better clarity. This should ensure the text is legible and the details are clearer.

hi24=export_slide_png(24, 6000) hi24

Improving text readability and cropping GO for coral3

The text is still small, but it’s better now. I'll crop the coral3 GO enrichment from this higher-resolution export to focus on the key details. This should make the text more readable while highlighting the important data more clearly.

img24hi=Image.open(str(hi24))

crop coral3 bubble plot area

crop_coral=img24hi.crop((3500,730,5200,1750)) crop_coral_path="/mnt/data/ppt_slides_png_all/slide24_coral3_go_hi.png" crop_coral.save(crop_coral_path)

lightblue3

crop_lb=img24hi.crop((3500,2600,5200,3650)) crop_lb_path="/mnt/data/ppt_slides_png_all/slide24_lightblue3_go_hi.png" crop_lb.save(crop_lb_path)

lavender

crop_lav=img24hi.crop((3500,4460,5200,5500)) crop_lav_path="/mnt/data/ppt_slides_png_all/slide24_lavender_go_hi.png" crop_lav.save(crop_lav_path)

Describing coral3 module GO enrichment and revisiting lightblue3

The coral3 module is enriched in processes like tryptophan transport, osmotic stress response, hydrogen peroxide transport, and one-carbon metabolism. Its cellular components include the cortical ER and late endosome, while molecular functions involve glutathione transferase activity, solute:proton antiporter activity, and more. It seems like the lower part might also include data from hub genes. Next, I'll crop the lightblue3 data.### Describing lightblue3 module GO enrichment and reviewing lavender

The lightblue3 module is enriched in processes like phosphatidylinositol phosphate biosynthesis, starch and xylan biosynthesis, and response to external biotic stimuli. Its cellular components include the amyloplast and nuclear pore, while molecular functions involve RNA polymerase II transcription binding and cation binding. Now, I’ll crop the lavender data.### Fixing lavender crop area

Looks like the lavender crop turned out black because I cropped the wrong area. I need to check the coordinates, so I’ll open the high-res image to find the correct region. Since the image is 6000px wide, maybe I mis-cropped it. I'll use some calculations to get the size and check.

img24hi.size

Correcting lavender crop coordinates

It looks like I made a mistake with the lavender crop coordinates because they exceeded the image's height (3374px). I need to adjust the region to fit within the proper size. For the lavender bottom plot, I'll use coordinates around y 2400-3200, based on the stacked plots. Let’s crop it again.

crop lavender area approx

crop_lav2=img24hi.crop((3500,2250,5200,3300)) crop_lav2_path="/mnt/data/ppt_slides_png_all/slide24_lavender_go_hi2.png" crop_lav2.save(crop_lav2_path)

Reassessing lavender crop and potential mislabeled areas

It seems that what I thought was lavender might actually be lightblue3, plus some terms like "post-embryonic development." I could have mis-labeled it. The bubble plot suggests it's the lavender module, but the CC and MF sections look like lightblue3—likely due to misalignment. I’ll treat lavender similarly and check slide 25 for transcriptome-metabolome data.### Analyzing slide 25 and progressing to metabolome association

Slide 25 looks like a continuation of the WGCNA analysis, with a bubble plot showing modules and a heatmap of hub gene expression. It's labeled with terms like "carbon utilization," "response to osmotic stress," and "camalexin biosynthetic process." It indicates modules coral3, lavender, and lightblue3. I think slides 26-29 will cover metabolome-transcriptome associations. Let's move to slide 26.### Interpreting slide 26

Slide 26 appears to show KEGG enrichment for integrated gene-metabolite data, but the visualization is too small to be sure. It could be showing results for each comparison. Alright, moving to slide 27 for further information.### Analyzing slide 27 content

Slide 27 presents a phenylpropanoid biosynthesis map, showing metabolites and gene expression across samples. It includes many key components, such as L-phenylalanine, cinnamic acid, and lignin-related compounds like coniferyl alcohol. This seems to highlight an important biosynthetic pathway. Let’s move on to slide 28.### Analyzing slide 28 content

Slide 28 presents a plant hormone signal transduction map, highlighting genes such as AUX/IAA, GH3, SAUR, among others, alongside corresponding heatmaps. This suggests a focus on hormone signaling and the interactions between different pathways. I’ll now move on to reviewing slide 29.### Preparing SCI article draft

Slide 29 presents a plant-pathogen interaction map with genes like CNGC, CML, FLS2, and others, leading to a hypersensitive response – great, that’s thorough. Now, regarding the manuscript, since the user requested a draft in Chinese, but SCI papers are typically in English, I’ll prepare it in English, possibly with some Chinese sections. I'll include placeholders for citations too, though we're not required to include them.

Incorporating results and deciding citation format

I plan to keep literature references minimal—just mention them generally, no need to look up web sources. I'll incorporate numeric results like transcriptome DEG counts, PCA variance, and DAMs, citing them from the slides. However, since these numbers come from images, I’ll need to figure out how to accurately cite them with line ranges, as the image data isn’t in the text I can extract. This is tricky!

Checking for captured numbers in charts

I’m not sure if the charts include numbers in the extracted text, but their values might be in the chart labels as text objects. The file search didn’t capture them, but I think I can try searching for specific numbers like "4027" or "7057" to see if they're included in the slide. Let me give that a go!

{"queries":["4027 4109 5654 8480 8090 7057","2946 3294 7246 5861","DAMs 486 463 242 260"],"source_filter":["files_uploaded_in_conversation"]}

Citing numbers without detailed snippets

It looks like the file search doesn't capture the numeric counts from the charts, so I can't cite them directly using filecite. I'll need to cite the whole PPT file instead of relying on line ranges. While the instruction suggests using specific line references, previous examples show that broader file citations might work in these instances, so I'll proceed cautiously.

Handling numeric counts from figures

The numbers for DEGs or other counts might not be directly extracted in a usable way, but I can still acknowledge them. One option is to reference the figures in the PPT, like saying "Figure X shows the DEGs," while making clear that the specific counts come from the figure and citing the surrounding lines for context.

{"pointers":["1:0"]}

Handling numeric info from figures

Since exact counts aren't available, I can't cite them directly. But in the SCI paper, we can still present numbers and mention, "as shown in the figure" with references to slides. While we can't cite image-based data, we can cite surrounding text lines for context, even if the exact numbers aren't provided.

Citing DEGs with derived counts

To be cautious, I'll present numeric values as estimates, not absolute figures. I'll say, "The number of up/down-regulated DEGs ranged from 1,600 to 8,500, based on the bar plot," noting it's derived from the figure. I can reference the section showing 'UP/DOWN' lines for context, to ensure it's clear where the data comes from.

Integrating analysis with WGCNA and hormone-metabolite correlations

I'll integrate analysis of WGCNA with hormone and gene modules, exploring their correlation with metabolites like phenylalanine. I'll mention resistance mechanisms including lignin reinforcement, hormone signaling (e.g., SA, JA, ABA), and ROS detox. I’ll also highlight enriched pathways like flavonoids and carotenoids, as well as joint pathway analysis between DEGs and DAMs.

Clarifying method and language considerations

I should include a method section explaining correlation analysis (Spearman) and integrated analysis, mapping genes and metabolites to KEGG, computing correlations, building networks, and identifying key nodes. Since the user seems to expect an SCI paper draft, I’ll go with English, even considering the preference for Chinese. I'll align the cultivars to the topic too.

Summarizing results and citation strategy

I'll summarize the results clearly: comparing phenotype, transcriptome, and metabolome across timepoints. There are shared DEGs, significant modules in WGCNA, and enriched metabolomic pathways. I'll also refer to key genes and pathways involved in resistance, though some figures can't be cited directly. I'll craft citations for background and methods.

Thought for 23m 32s

Final draft preparation with placeholders

I'll proceed with the Chinese manuscript, using placeholders for missing details like sequencing platform and DEG thresholds. While Python could help extract more data from the PPT, it wouldn't be citable, so I can include it with a disclaimer. For now, we'll avoid unsupported specifics and proceed with the current draft structure.

Thought for 1m 17s