Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261013T115045Z-figure-strengthening-154/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/strengthen_rare_literature_20261013.py

@@ -0,0 +1,327 @@
+"""Original-paper Figure 5 targets and bounded observed Ye marker diagnostics.
+
+CPU-only: reads compact metadata/caches and streams five columns from the saved
+CSR ZIP. Does not fit embeddings, infer labels, load a 6k matrix, or generate cells.
+"""
+from __future__ import annotations
+import hashlib
+import json
+import os
+from pathlib import Path
+import textwrap
+import zipfile
+
+os.environ['CUDA_VISIBLE_DEVICES'] = ''
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+from matplotlib.cm import ScalarMappable
+
+D = Path(__file__).resolve().parents[1]
+M = D.parents[1]
+FT = M/'plans/paired_finetune_intradataset_20260927'
+CACHE = M/'plans/rare_states_paired_20261008/work'
+DATA = D/'data/strengthening_20261013/rare_literature'
+FIG = D/'figures/strengthening_20261013/rare_literature'
+PAPERS = Path('/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/paired-cohorts')
+CANON = Path('/home/sj_server_1/gblee/datalake/paired-pbmc-bulk-sc/processing/strict_qc_v1_20260913/reference/canonical_genes.tsv')
+BLUE, MINT, INK = '#168FE5', '#83C6A8', '#4D4D4D'
+GENES = ['CD14','MKI67','STMN1','MPO','ELANE']
+TARGETS = [
+    dict(cohort='ye2026_sepsis',citation='Ye et al., Nature Immunology 2026',doi='10.1038/s41590-025-02345-x',
+         url='https://www.nature.com/articles/s41590-025-02345-x',figure='Fig. 5a–f; validation Fig. 7f / Extended Data Fig. 9e',
+         population='Mono_c06-CD14-MKI67',kind='Reported discrete proliferating monocyte subset; rarity must be measured locally',
+         setting='Pediatric pulmonary sepsis (paper: within 48 h of onset); pediatric HC/ICU, abdominal sepsis and adults remain controls.',
+         markers=GENES,negative_markers=[],
+         interpretation='MKI67/STMN1/MPO/ELANE jointly support the published proliferative program in a CD14 monocyte context. PCLAF is not required by this original Fig. 5 definition.',
+         gate='No fine-cluster labels found in 133 audited canonical obs schemas or the paired table. Marker co-detection is not a Mono_c06 cluster assignment.',
+         pdf=str(PAPERS/'Single-cell multi-omic landscape reveals anatomical-specific immune features in adult and pediatric sepsis.pdf')),
+    dict(cohort='imperial_eskd',citation='Stephenson et al., Cell Genomics 2025',doi='10.1016/j.xgen.2025.100918',
+         url='https://pmc.ncbi.nlm.nih.gov/articles/PMC12366655/',figure='Fig. 5A–G (especially 5C–E)',
+         population='Dexamethasone-related monocytes',kind='Reported treatment-associated discrete subset; not automatically rare',
+         setting='Wave 2 COVID-positive patients receiving glucocorticoids; align each blood visit to treatment start.',
+         markers=['CD163','ADAMTS2','SLC1A3','SESN1','FPR1','MTSS1','MFGE8','MRC1'],
+         negative_markers=['JUN','CXCL8','HLA-DRA','HLA-DRB5'],
+         interpretation='The paper contrasts an anti-inflammatory/phagocytic program with lower inflammatory and antigen-presentation expression. Fig. 3 HLA-II suppression is a program, not a rare-cell identity.',
+         gate='Paired metadata include wave 2, but the available author labels collapse these cells into broad monocytes. Treatment dates and fine labels must be joined before frequency recovery.',
+         pdf=str(PAPERS/'Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf')),
+    dict(cohort='giroux2022',citation='Giroux et al., Scientific Reports 2022',doi='10.1038/s41598-022-15668-8',
+         url='https://pmc.ncbi.nlm.nih.gov/articles/PMC9271053/',figure='Fig. 5A–F; Supplementary Tables S8/S9',
+         population='IgG-negative CD14 monocyte clusters 2/4 (mild) and 7 (moderate)',kind='Transcriptomic/regulon states with epigenetic corroboration; rarity not established',
+         setting='Before IgG seroconversion, mild versus moderate outpatients; local severe label must be checked against the paper’s moderate definition.',
+         markers=['CD14','IRF7','IRF1','STAT1','CEBPB','KLF3','CCR2'],negative_markers=[],
+         interpretation='IRF7/IRF1/STAT1 versus CEBPB/KLF3 are regulatory-program findings, not an exact RNA-only positivity gate. scRNA clusters connect to scATAC states.',
+         gate='No author fine labels locally; IRF1/KLF3 are outside the saved 6k panel. Align serostatus, longitudinal donors and marked sex imbalance; no exact rare-cluster claim.',
+         xml=str(DATA/'Giroux2022_PMC9271053.xml')),
+    dict(cohort='ji2025_chili',citation='Ji et al., Cancer Immunology, Immunotherapy 2025',doi='10.1007/s00262-025-04033-z',
+         url='https://pmc.ncbi.nlm.nih.gov/articles/PMC12049347/',figure='Fig. 2 (TCR clonality); Fig. 4A/B and Table 2 (cell-type DE/programs)',
+         population='CD8 TEM, CD8 naive, CD4 TCM and CD16 monocyte programs',kind='Common cell-type expression changes; no specific rare subset established in the original paper',
+         setting='Liver-injury timepoint TOL versus pretreatment BC / control AC; FU1 is follow-up and must not be silently pooled with TOL.',
+         markers=[],negative_markers=[],
+         interpretation='Original HuBMAP-type labels survive locally. DEG counts and pathways can be reproduced as Figure 4 evidence; TCR clonality cannot be recovered from generated 6k expression alone.',
+         gate='Do not substitute a generic cycling/plasmablast rule for the paper’s main finding. Select a genuinely paper-defined rare target only if supplementary evidence supports one.',
+         pdf=str(PAPERS/'Ji2025_CancerImmunolImmunother_checkpoint_inhibitor_liver_injury.pdf')),
+]
+
+def sha(path):
+    with Path(path).open('rb') as f:
+        return hashlib.file_digest(f,'sha256').hexdigest()
+
+def binding(path,role):
+    path=Path(path).resolve()
+    return {'path':str(path),'sha256':sha(path),'bytes':path.stat().st_size,'role':role}
+
+def dump(path,obj):
+    Path(path).write_text(json.dumps(obj,indent=2,allow_nan=False)+'\n')
+
+def read_header(stream):
+    version=np.lib.format.read_magic(stream)
+    if version==(1,0):return np.lib.format.read_array_header_1_0(stream)
+    if version==(2,0):return np.lib.format.read_array_header_2_0(stream)
+    raise ValueError('Unsupported NPY version '+repr(version))
+
+def stream_columns(path,cols):
+    """Keep only selected CSR columns while indices/data are decompressed in 1M-entry blocks."""
+    with zipfile.ZipFile(path) as z:
+        with z.open('indptr.npy') as f:indptr=np.load(f,allow_pickle=False)
+        with z.open('shape.npy') as f:shape=np.load(f,allow_pickle=False)
+        out=np.zeros((int(shape[0]),len(cols)),dtype=np.int32)
+        lookup=np.full(int(shape[1]),-1,dtype=np.int16);lookup[cols]=np.arange(len(cols))
+        with z.open('indices.npy') as ix,z.open('data.npy') as values:
+            ish,ifor,idt=read_header(ix);vsh,vfor,vdt=read_header(values)
+            if ifor or vfor or len(ish)!=1 or ish!=vsh or ish[0]!=indptr[-1]:
+                raise ValueError('Unexpected CSR arrays')
+            for offset in range(0,int(ish[0]),1_000_000):
+                n=min(1_000_000,int(ish[0])-offset)
+                rawi=ix.read(n*idt.itemsize);rawv=values.read(n*vdt.itemsize)
+                if len(rawi)!=n*idt.itemsize or len(rawv)!=n*vdt.itemsize:raise ValueError('Truncated sparse stream')
+                indices=np.frombuffer(rawi,dtype=idt);vals=np.frombuffer(rawv,dtype=vdt)
+                match=lookup[indices]>=0
+                if match.any():
+                    chosen=vals[match]
+                    if not np.isfinite(chosen).all() or (chosen<0).any() or not np.equal(chosen,np.floor(chosen)).all():
+                        raise ValueError('Marker entries are not nonnegative UMI counts')
+                    rows=np.searchsorted(indptr[1:],offset+np.flatnonzero(match),side='right')
+                    np.add.at(out,(rows,lookup[indices[match]]),chosen.astype(np.int32))
+    return out,{'n_cells':int(shape[0]),'input_genes':int(shape[1]),'retained_genes':len(cols),
+                'csr_entries_streamed':int(indptr[-1]),'max_entries_per_block':1_000_000,
+                'no_full_sparse_or_dense_matrix_loaded':True}
+
+def clinical_group(group,site):
+    if group=='ped_sepsis':return 'Pediatric pulmonary' if site=='Respiratory' else 'Pediatric abdominal'
+    if group=='adult_sepsis':return 'Adult pulmonary' if site=='Respiratory' else 'Adult other sepsis'
+    return {'ped_HC':'Pediatric HC','ped_ICU_ctrl':'Pediatric ICU','adult_HC':'Adult HC','adult_ICU_ctrl':'Adult ICU'}[group]
+
+GROUPS=['Pediatric HC','Pediatric ICU','Pediatric abdominal','Pediatric pulmonary','Adult HC','Adult ICU','Adult pulmonary','Adult other sepsis']
+
+def observed_ye(sources):
+    ds='ye2026_sepsis';base=FT/'data'/ds
+    matrix=base/'cells_panel6k.npz';obs_path=base/'cells_obs.parquet';pairs_path=base/'pairs.csv'
+    label_path=FT/'scoring_cache'/ds/'observed_labels/labels/labels.parquet'
+    for p in [matrix,obs_path,pairs_path,label_path,CANON]:sources[str(p)]=binding(p,'Ye observed marker or metadata source')
+    canon=pd.read_csv(CANON,sep='\t',dtype=str);gene_id=dict(zip(canon.gene_symbol,canon.gene_id))
+    with np.load(matrix,allow_pickle=False) as z:
+        panel=list(z['panel_gene_ids'].astype(str));measured=z['panel_measured'];row_sample=z['row_sample']
+    cols=[panel.index(gene_id[g]) for g in GENES]
+    if not measured[:,cols].all():raise ValueError('A target gene is unmeasured in some source samples')
+    counts,stream_proof=stream_columns(matrix,cols)
+    obs=pd.read_parquet(obs_path,columns=['sample_id','donor_id','group','lib_panel','author_cell_type'])
+    lab=pd.read_parquet(label_path,columns=['sample_id','aifi_l1','aifi_l2','npz_row'])
+    if len(obs)!=len(lab) or len(obs)!=len(counts) or not np.array_equal(obs.sample_id.astype(str),lab.sample_id.astype(str)):
+        raise ValueError('Observed labels and marker rows do not align')
+    if not np.array_equal(lab.npz_row.to_numpy(),np.arange(len(lab))):raise ValueError('Observed label NPZ row order differs')
+    pairs=pd.read_csv(pairs_path,keep_default_na=False)
+    eligible=pairs[pairs.identity_pass.astype(str).str.lower().eq('true')].set_index('sample_id')
+    if eligible.index.duplicated().any():raise ValueError('Repeated Ye sample metadata')
+    parent=lab.aifi_l1.eq('Monocyte').to_numpy() & obs.sample_id.isin(eligible.index).to_numpy()
+    if not parent.any():raise ValueError('No frozen-label monocytes')
+    selected=obs.loc[parent].copy();C=counts[parent];del counts
+    selected['clinical_group']=[clinical_group(eligible.loc[s,'group'],eligible.loc[s,'infection_source']) for s in selected.sample_id]
+    selected['npz_row']=np.flatnonzero(parent)
+    if selected.donor_id.astype(str).nunique()!=selected.sample_id.astype(str).nunique():raise ValueError('Ye donor/sample is not one-to-one')
+    np.savez_compressed(DATA/'ye_observed_five_markers.npz',counts=C,genes=np.asarray(GENES),
+                        sample_id=selected.sample_id.astype(str).to_numpy(),donor_id=selected.donor_id.astype(str).to_numpy(),
+                        npz_row=selected.npz_row.to_numpy(),lib_panel=selected.lib_panel.to_numpy())
+    selected.to_parquet(DATA/'ye_observed_marker_cells.parquet',index=False)
+    positive=C>0
+    # These are explicit count patterns, not new cell-type labels or the author's cluster assignment.
+    patterns={g:positive[:,j] for j,g in enumerate(GENES)}
+    patterns['MKI67+STMN1']=positive[:,1]&positive[:,2]
+    patterns['MKI67+STMN1 with MPO or ELANE']=positive[:,1]&positive[:,2]&(positive[:,3]|positive[:,4])
+    rows=[]
+    for sample,indices in selected.groupby('sample_id',sort=True).indices.items():
+        for marker,present in patterns.items():
+            hits=int(present[indices].sum());n=len(indices)
+            rows.append({'sample_id':sample,'donor_id':str(eligible.loc[sample,'donor_id']),
+                         'clinical_group':selected.iloc[indices[0]].clinical_group,'marker_or_pattern':marker,
+                         'positive_cells':hits,'parent_monocytes':n,'percent':100*hits/n,
+                         'median_panel_umi':float(selected.iloc[indices].lib_panel.median()),
+                         'parent_definition':'frozen AIFI L1 Monocyte','readout':'observed native UMI counts > 0'})
+    per=pd.DataFrame(rows);per.to_csv(DATA/'ye_observed_marker_per_donor.csv',index=False)
+    summary=per.groupby(['clinical_group','marker_or_pattern'],sort=False).agg(
+        n_donors=('sample_id','size'),positive_cells=('positive_cells','sum'),parent_monocytes=('parent_monocytes','sum'),
+        mean_donor_percent=('percent','mean'),median_donor_percent=('percent','median'),
+        donors_with_signal=('positive_cells',lambda x:int((x>0).sum()))).reset_index()
+    summary.to_csv(DATA/'ye_observed_marker_summary.csv',index=False)
+    m=summary.set_index(['clinical_group','marker_or_pattern']).mean_donor_percent
+    proof={**stream_proof,'identity_passed_samples':len(eligible),'analyzed_samples':int(selected.sample_id.nunique()),
+           'analyzed_monocytes':int(len(selected)),'all_five_genes_measured_in_all_source_samples':True,
+           'labels_npz_rows_exact':True,'author_fine_labels_present':False,'source_sha256':sources[str(matrix)]['sha256'],
+           'observed_pattern_comparison':{'pattern':'MKI67+STMN1','pediatric_pulmonary_mean_percent':float(m['Pediatric pulmonary','MKI67+STMN1']),
+                                          'pediatric_abdominal_mean_percent':float(m['Pediatric abdominal','MKI67+STMN1']),
+                                          'pulmonary_specificity_reproduced':False},
+           'limitations':['Native depth; marker dropout and depth differ between donors.',
+                         'Parent uses the frozen AIFI label; published Mono_c06 fine labels are absent.',
+                         'Co-detection patterns are diagnostics, not author-cluster calls.',
+                         'This is reuse of the original cohort, not independent replication.'],
+           'no_new_inference':True,'no_embedding_fit':True}
+    dump(DATA/'ye_observed_extraction_proof.json',proof)
+    return per,summary,proof
+
+def inventory(sources):
+    canon=pd.read_csv(CANON,sep='\t',dtype=str);gene_id=dict(zip(canon.gene_symbol,canon.gene_id))
+    rows=[];availability=[];panel_bindings=[]
+    for t in TARGETS:
+        ds=t['cohort'];base=FT/'data'/ds;cache=CACHE/ds/'draw0/lora5.npz'
+        for path in [base/'pairs.csv',base/'samples.csv',base/'cells_obs.parquet',cache]:sources[str(path)]=binding(path,'paired cohort readiness')
+        pairs=pd.read_csv(base/'pairs.csv',keep_default_na=False)
+        eligible=pairs[pairs.identity_pass.astype(str).str.lower().eq('true')]
+        ob=pd.read_parquet(base/'cells_obs.parquet',columns=['sample_id','author_cell_type'])
+        labels=ob.author_cell_type.fillna('').value_counts().to_dict()
+        with np.load(cache,allow_pickle=False) as z:cached=set(z['marker_symbols'].astype(str))
+        matrix=base/'cells_panel6k.npz'
+        # Only the tiny gene axis and sample-by-gene measured mask are opened here.
+        with np.load(matrix,allow_pickle=False) as z:
+            panel=list(z['panel_gene_ids'].astype(str));mask=z['panel_measured']
+        with zipfile.ZipFile(matrix) as z:
+            for member in ['panel_gene_ids.npy','panel_measured.npy']:
+                payload=z.read(member)
+                panel_bindings.append({'path':str(matrix),'member':member,'bytes':len(payload),
+                                       'sha256':hashlib.sha256(payload).hexdigest(),'scope':'Only this small ZIP member was used; no matrix payload read for inventory'})
+        info={**t,'all_paired_samples':len(pairs),'identity_passed_samples':len(eligible),
+              'identity_passed_donors':int(eligible.donor_id.nunique()),'legacy_cache_genes':len(cached),
+              'legacy_cache_path':str(cache),'observed_path':str(base/'cells_obs.parquet'),
+              'observed_author_labels':labels,'observed_full_panel_path':str(matrix),
+              'wave_counts':pairs.bulk_wave.value_counts().to_dict() if 'bulk_wave' in pairs else {},
+              'sex_by_group':pd.crosstab(pairs.group,pairs.sex_geo).to_dict() if ds=='giroux2022' else {}}
+        info['available_cache_markers']=[g for g in t['markers']+t['negative_markers'] if g in cached]
+        info['missing_cache_markers']=[g for g in t['markers']+t['negative_markers'] if g not in cached]
+        for g in t['markers']+t['negative_markers']:
+            col=panel.index(gene_id[g]) if gene_id.get(g) in panel else None
+            availability.append({'cohort':ds,'gene':g,'role':'lower in target' if g in t['negative_markers'] else 'marker/program',
+                                 'in_legacy_marker_cache':g in cached,'in_model_6k_axis':col is not None,
+                                 'measured_in_all_source_samples':bool(mask[:,col].all()) if col is not None else False})
+        rows.append(info)
+    dump(DATA/'target_matrix.json',rows)
+    pd.DataFrame([{k:('; '.join(v) if isinstance(v,list) else v) for k,v in t.items()
+                   if not isinstance(v,dict)} for t in rows]).to_csv(DATA/'target_matrix.csv',index=False)
+    pd.DataFrame(availability).to_csv(DATA/'marker_readiness.csv',index=False)
+    dump(DATA/'panel_metadata_bindings.json',panel_bindings)
+    sources[str(DATA/'panel_metadata_bindings.json')]=binding(DATA/'panel_metadata_bindings.json','exact small panel metadata member hashes')
+    return rows
+
+def save(fig,key):
+    paths=[]
+    for ext in ['png','pdf','svg']:
+        p=FIG/(key+'.'+ext);fig.savefig(p,dpi=200,bbox_inches='tight',pad_inches=.08);paths.append(str(p))
+    plt.close(fig);return paths
+
+def target_cards(targets,key):
+    fig=plt.figure(figsize=(12.3,5.6));ax=fig.add_axes([0,0,1,1]);ax.axis('off')
+    ax.set_xlim(0,1);ax.set_ylim(0,1)
+    for i,t in enumerate(targets):
+        y=.96-i*.49
+        ax.text(.01,y,t['citation'],fontsize=16,weight='bold',color=INK,va='top')
+        ax.text(.01,y-.058,t['figure'],fontsize=12,color='#777777',va='top')
+        lines=[('Target',t['population']),('Scope',t['setting']),('Readiness',t['gate'])]
+        for j,(label,body) in enumerate(lines):
+            yy=y-.13-j*.103
+            ax.text(.01,yy,label,fontsize=13,weight='bold',va='top',color=INK)
+            ax.text(.13,yy,textwrap.fill(body,117),fontsize=13,va='top',color=INK,linespacing=1.15)
+        if i==0:ax.plot([.01,.99],[.493,.493],color='#DDDDDD',lw=.8)
+    return save(fig,key)
+
+def plot_observed(per,summary):
+    fig=plt.figure(figsize=(12.3,5.6))
+    ax=fig.add_axes([.19,.18,.41,.65]);bx=fig.add_axes([.72,.18,.26,.65])
+    table=summary[summary.marker_or_pattern.isin(GENES)].pivot(index='clinical_group',columns='marker_or_pattern',values='mean_donor_percent').reindex(index=GROUPS,columns=GENES)
+    n=per[per.marker_or_pattern.eq('CD14')].groupby('clinical_group').size()
+    xx,yy=np.meshgrid(np.arange(5),np.arange(8));vals=table.to_numpy()
+    ax.imshow(vals,cmap='Greys',vmin=0,vmax=100,aspect='auto',interpolation='nearest')
+    for x,y,value in zip(xx.ravel(),yy.ravel(),vals.ravel()):
+        ax.text(x,y,f'{value:.1f}',ha='center',va='center',fontsize=11,color='white' if value>55 else INK)
+    ax.set_xticks(range(5),GENES,rotation=35,ha='right');ax.set_yticks(range(8),[f'{g} (n={n[g]})' for g in GROUPS]);ax.invert_yaxis();ax.set_ylim(7.6,-.6)
+    fig.text(.19,.98,'Observed gene detection',fontsize=14,va='top');ax.set_xlim(-.6,4.6)
+    fig.text(.19,.925,'Numbers = mean donor detection (%)',fontsize=11,color=INK,va='top')
+    for spine in ax.spines.values():spine.set_visible(False)
+    ax.tick_params(length=0)
+    cb=fig.colorbar(ScalarMappable(norm=Normalize(0,100),cmap='Greys'),ax=ax,fraction=.035,pad=.04);cb.set_label('Mean donor %',fontsize=11);cb.ax.tick_params(labelsize=10)
+    patterns=['MKI67+STMN1','MKI67+STMN1 with MPO or ELANE'];markers=['o','s']
+    for j,pat in enumerate(patterns):
+        for i,g in enumerate(GROUPS):
+            values=per[(per.clinical_group==g)&(per.marker_or_pattern==pat)].percent.to_numpy()
+            bx.scatter(values,np.full(len(values),i)+(-.12 if j==0 else .12),marker=markers[j],s=22,
+                       facecolor=INK if j==0 else 'white',edgecolor=INK,lw=.7,alpha=.65)
+    bx.set_yticks(range(8),[]);bx.set_ylim(7.6,-.6);bx.set_xlabel('Observed monocytes (%)')
+    bx.spines[['top','right','left']].set_visible(False);bx.tick_params(axis='y',length=0);bx.grid(axis='x',color='#EAEAEA',lw=.5);bx.set_axisbelow(True)
+    fig.text(.19,.02,'Native UMI > 0; fixed AIFI monocyte labels. Dots = donors; n = donors, not cells.',fontsize=11,color=INK)
+    fig.text(.72,.98,'Per-donor co-detection',fontsize=14,va='top')
+    fig.text(.72,.925,'● MKI67 + STMN1\n□ + MPO or ELANE',fontsize=10.5,color=INK,va='top')
+    return save(fig,'ye_observed_paper_markers')
+
+def build():
+    DATA.mkdir(parents=True,exist_ok=True);FIG.mkdir(parents=True,exist_ok=True)
+    plt.rcParams.update({'font.family':'sans-serif','font.sans-serif':['Arial','Liberation Sans','DejaVu Sans'],
+                         'font.size':13,'axes.labelsize':13,'xtick.labelsize':12,'ytick.labelsize':12,
+                         'pdf.fonttype':42,'ps.fonttype':42,'svg.fonttype':'none','figure.facecolor':'white'})
+    sources={}
+    for t in TARGETS:
+        p=Path(t.get('pdf',t.get('xml')));sources[str(p)]=binding(p,'original publication')
+    for p in [Path(__file__),D/'PBISC-Diffusion_color_palette_20261013.md',CANON]:sources[str(p)]=binding(p,'analysis definition')
+    inventory_path=DATA/'ye_author_label_inventory.json'
+    if inventory_path.exists():sources[str(inventory_path)]=binding(inventory_path,'read-only original-annotation inventory')
+    targets=inventory(sources)
+    per,summary,proof=observed_ye(sources)
+    assets=[]
+    assets+=target_cards(targets[:2],'published_population_targets')
+    assets+=target_cards(targets[2:],'published_program_boundaries')
+    assets+=plot_observed(per,summary)
+    common='Original-cohort retrospective analysis. No new generation, label inference or embedding fit. Literature targets are not automatically rare; rarity and identity require observed evidence.'
+    slides=[]
+    for key,title,footnote in [
+        ('published_population_targets','Figure 5 targets begin with the source paper','Ye: Fig. 5 proliferating pediatric monocytes. Imperial: Fig. 5 wave-2 steroid-associated monocytes; fine labels/timing still needed.'),
+        ('ye_observed_paper_markers','Ye sepsis: the published markers in observed paired cells','Native-depth diagnostic, not Mono_c06 identity. MKI67/STMN1 mean: pulmonary 2.33%, abdominal 4.10%; pulmonary specificity is not reproduced.'),
+        ('published_program_boundaries','A transcriptional program is not automatically a rare population','Giroux: pre-seroconversion RNA/regulon states and sex imbalance. Ji: common-cell-type DE/TCR changes; no paper-defined rare identity established.')]:
+        notes=common+' '+footnote
+        if key=='ye_observed_paper_markers':notes+=' '+json.dumps(proof,ensure_ascii=False)
+        else:notes+=' '+json.dumps(targets[:2] if key=='published_population_targets' else targets[2:],ensure_ascii=False)
+        slides.append({'kind':'content','key':'rare_literature_'+key,'role':'f5_original_paper_observed_gate','title':title,
+                       'section_label':'Fig. 5 · Source-defined populations','layout':'full','keywords':[],
+                       'figures':[str((FIG/(key+'.png')).relative_to(D))],'palette':'biological','footnote':footnote,
+                       'footnote_full':notes,'notes':notes,'sources':'; '.join(t['url'] for t in targets)})
+    report=['# Source-paper targets and observed readiness','',common,'']
+    for t in targets:
+        report.extend([f"## {t['citation']}",f"[{t['population']}]({t['url']}) — {t['figure']}",
+                       t['kind'],t['setting'],t['interpretation'],t['gate'],
+                       'Cache markers present: '+(', '.join(t['available_cache_markers']) or 'not applicable'),
+                       'Cache markers missing: '+(', '.join(t['missing_cache_markers']) or 'not applicable'),''])
+    report.extend(['## Ye observed diagnostics',
+                   f"Analyzed {proof['analyzed_monocytes']:,} frozen-label monocytes from {proof['analyzed_samples']} identity-passed donors. Five original-paper markers were streamed from the saved sparse matrix.",
+                   'MKI67/STMN1 co-detection and co-detection with MPO or ELANE are explicitly defined diagnostic patterns. They are not the author cluster. Native sequencing depth is not matched in this first observed check.',
+                   'The mean donor MKI67/STMN1 co-detection is 2.33% in pediatric pulmonary versus 4.10% in pediatric abdominal sepsis. This simple diagnostic does not reproduce pulmonary specificity; no corresponding model-recovery claim is made.',
+                   'Fine-label audit: all 133 canonical GSE279452 H5AD obs schemas, the 665-member raw extraction receipt and paired cells_obs.parquet were checked without reading H5AD X. No barcode-level author subtype annotation was found. The public rnacenter/Human-Sepsis repository contains analysis code; the paper (Data availability, PDF p21) also points to source data and supporting data on reasonable request. Obtain and align original barcode labels before assigning Mono_c06.',
+                   'Next: validate observed subpopulation with the original fine labels or a prespecified marker program; then expand the existing generated marker caches by CD14/STMN1 (and Imperial markers), preserving original Poisson readout, cell row IDs and support/test bindings. Compare PBISC and scSemi only on the shared held-out donor set and equal depth/cell budgets. All negatives and failures remain visible.',
+                   'ImMicro is a healthy negative/reference cohort, not a disease-specific rare-population reproduction target.'])
+    (DATA/'READINESS.md').write_text('\n\n'.join(report)+'\n')
+    assets += [str(p) for p in DATA.iterdir() if p.is_file() and p.name!='result.json']
+    result={'schema':'pbisc.strengthening.rare-literature.v1','slides':slides,'assets':sorted(set(assets)),
+            'sources':list(sources.values()),'targets':targets,'observed_ye':proof,'no_new_inference':True,
+            'generated_population_recovery_claim':False,'completion_scope':'Four primary-paper target definitions plus observed Ye five-marker diagnostics; exact paper-cluster/PBISC/scSemi identity comparison remains gated.'}
+    dump(DATA/'result.json',result)
+    return result
+
+if __name__=='__main__':
+    r=build();print(json.dumps({'slides':len(r['slides']),'assets':len(r['assets']),'observed_ye':r['observed_ye']},indent=2))

paper-releases/pbisc-diffusion/updates/20261013T115045Z-figure-strengthening-154/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/verify_strengthened_master_20261013.py

@@ -0,0 +1,67 @@
+"""Verify source hashes, page preservation, embedded pictures and rendered bounds."""
+from pathlib import Path
+from datetime import datetime,timezone
+import hashlib
+import json
+import sys
+import fitz
+from pptx import Presentation
+D=Path(__file__).resolve().parents[1]
+A=D/'data/strengthening_20261013';Q=A/'qa'
+sys.path.insert(0,str(D/'tools'))
+import verify_expanded_master_20261013 as H
+H.QA=Q
+
+
+def sha(path):
+    with Path(path).open('rb') as f:return hashlib.file_digest(f,'sha256').hexdigest()
+
+
+def main():
+    Q.mkdir(exist_ok=True)
+    spec=json.loads((A/'spec.json').read_text());m=json.loads((A/'manifest.json').read_text())
+    records=spec['slides'];pp=D/spec['deck']['output_pptx'];pf=D/spec['deck']['output_pdf']
+    prs=Presentation(pp);pdf=fitz.open(pf);fail=[]
+    counts={'spec':len(records),'pptx':len(prs.slides),'pdf':len(pdf)}
+    if set(counts.values())!={m['total_slides']}:fail.append(['page_counts',counts])
+    if sorted(s['parent_142_page'] for s in records if 'parent_142_page' in s)!=list(range(1,143)):fail.append(['parent_coverage'])
+    if sorted(s['original_slide'] for s in records if 'original_slide' in s)!=list(range(1,77)):fail.append(['original_coverage'])
+    for p,b in m['sources'].items():
+        if sha(p)!=b['sha256']:fail.append(['source_hash',p])
+    for p,h in m['protected_sources'].items():
+        if sha(p)!=h:fail.append(['protected_hash',p])
+    original=json.loads(Path(m['parent_spec']).read_text())['slides']
+    for s in records:
+        if 'parent_142_page' in s and s.get('figures',[])!=original[s['parent_142_page']-1].get('figures',[]):
+            fail.append(['parent_picture_replaced',s['parent_142_page']])
+    images=[];fonts=set()
+    for n,(slide,s) in enumerate(zip(prs.slides,records),1):
+        images.append(H._image_proofs(slide,s,n,fail))
+        for shape in slide.shapes:
+            if shape.left < -2 or shape.top < -2 or shape.left+shape.width > prs.slide_width+2 or shape.top+shape.height > prs.slide_height+2:
+                fail.append(['shape_bounds',n,shape.name])
+            for tf in H._text_frames(shape):
+                for paragraph in tf.paragraphs:
+                    for run in paragraph.runs:fonts.update(H._font_names(run,paragraph))
+    if fonts!={'Arial'}:fail.append(['font',sorted(fonts)])
+    for n,page in enumerate(pdf,1):
+        for block in page.get_text('dict')['blocks']:
+            for line in block.get('lines',[]):
+                for span in line.get('spans',[]):
+                    x0,y0,x1,y1=span['bbox']
+                    if x0<-.75 or y0<-.75 or x1>page.rect.width+.75 or y1>page.rect.height+.75:
+                        fail.append(['pdf_text_bounds',n,span['text']])
+    renders=H._render(pdf,records)
+    m['output']['pdf']={'path':str(pf),'sha256':sha(pf)}
+    (A/'manifest.json').write_text(json.dumps(m,indent=2)+'\n')
+    report=dict(schema='pbisc.strengthening.qa.v1',checked_at=datetime.now(timezone.utc).isoformat(),
+                status='automated_pass_visual_pending' if not fail else 'failed',automated_checks_pass=not fail,
+                failures=fail,counts=counts,parent142_preserved=True,original76_preserved=True,
+                newpages=m['newpages'],groups=m['groups'],image_checks=images,renders=renders,fonts=sorted(fonts),
+                artifacts=m['output'],visual_review={'status':'pending'})
+    (Q/'VERIFICATION.json').write_text(json.dumps(report,indent=2)+'\n')
+    print(json.dumps({'pass':not fail,'counts':counts,'failures':fail}))
+    if fail:raise SystemExit(1)
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261013T115045Z-figure-strengthening-154/diffusion/metadata/inference_locations.json

@@ -0,0 +1,5 @@
+{
+  "locations": [],
+  "project_id": "pbisc-diffusion",
+  "update_id": "20261013T115045Z-figure-strengthening-154"
+}

paper-releases/pbisc-diffusion/updates/20261013T115045Z-figure-strengthening-154/diffusion/metadata/request.json

@@ -0,0 +1,1363 @@
+{
+  "assets": [
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/AGENTS.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/PAPER.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/evidence/README.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/logic/claims.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/logic/problem.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/logic/solution/heuristics.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/staging/observations.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/exploration_tree.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/pm_reasoning_log.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/sessions/2026-10-13_001.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/sessions/session_index.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_expanded_master_20261013_strengthened.pdf",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_expanded_master_20261013_strengthened.pptx",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/README.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/figure_assignment_20261013/STRENGTHENING_PROPOSAL.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/README.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/STRENGTHENING_PROPOSAL_before_revision.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/LIMITATIONS.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/celltype_counts.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/condition_by_split.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/condition_counts.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/hashes.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/recipe_counts.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/result.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/split_counts.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/corpus/verified_data.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/cv_cat_complete.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/cv_multimetric.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/result.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/pooled/cat.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/pooled/ceiling.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/pooled/contrast.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/pooled/headline.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/pooled/manifest.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/pooled/rbo.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/scsemi_cv_20261002/pooled/cat.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/scsemi_cv_20261002/pooled/ceiling.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/scsemi_cv_20261002/pooled/contrast.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/scsemi_cv_20261002/pooled/headline.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/scsemi_cv_20261002/pooled/manifest.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/giroux2022/scsemi_cv_20261002/pooled/rbo.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/imperial_eskd/pooled/cat.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/strengthening_20261013/disease_metrics/source_snapshot/plans/paired_finetune_intradataset_20260927/scores_contrast/imperial_eskd/pooled/ceiling.csv",
+      "state": "completed"
+    },
+    {
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