dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, a conference room or hallway can have a box rated for 2000 CFM, while a small office can have a box rated for 150 CFM. Therefore, a damper in a higher capacity rated VAV box has a higher influence on the downstream pressure sensed by the static pressure controller. The resulting variable, x j , captures this and contains information about the operational range of the installed VAV system. As discussed in Section 3. 1, most VAV systems are designed to operate close to their minimum loading condition. In this case, the minimum thermal loading condition for VAV system j can be obtained by calculating the sample minimum using historical data of each damper position or recording it from the control logic parameters. That means that using the values h i;j and h i;j there is a corresponding x j and x j.
Proposed Experiments We validate the results of our proposed model using both simulation (Section 4. 3) and real-world (Section 4. 4) demonstrations. The setup, duration, and actuation of the events are common across all experiment instances. First, all zones are actuated in unison to gather a collective response from all independent HVAC systems in a building. For the simulation experiments, we assume perfect knowledge of the counterfactual load by simulating two instances of the building for every event, one with and one without the events under identical environmental conditions. Therefore, there is no distortion created by the errors of the baseline model. For the real-world experiment, we use sub-metered HVAC data to fit a baseline model and measure the observed events. Using sub-metered data, we are able to reduce the errors that can be introduced by other non-HVAC loads in the observed events.
Simulation Experiments Using two building energy modeling (BEM) software packages, EnergyPlus  [14]  and the Modelica Buildings Library  [49] , we generated simulated DF events. Although both are BEM software, the Modelica models are focused on the dynamic modeling of complex systems composed of multi-domain components. By using both models for validation, we are able to confirm the results under both model architectures commonly used in BEM research. While EnergyPlus uses a quasi-steady state energy balance of thermal loads and simplified HVAC and controls, Modelica uses a set of equation-based dynamic simulation models with explicit HVAC components and controls. Observing the results in both models confirms that the results are not a particular behavior of one of the two modeling engines.
For all experiments, a GTA event was implemented between 8:00 and 9:00 every day during the simulation period. The data was recorded for the event. The simulation period is repeated with an event happening between 9:00 and 10:00 every day. This process is iterated for all 1-h periods between 8:00 and 19:00. Therefore, no sequential inputs were applied within each day. The events were restricted to only cooling operation to engage the power consumption of both the chiller and the fans. This instantiation was repeated for weather conditions aligned with five distinct ASHRAE-defined climate zones. These weather instances range from very hot, hot, warm, mild, and cold  [5]. Generally the transition from a ''very hot" to a ''cold" city is both representative of the maximum temperatures and of the interday variability in average temperature.
Modelica We simulated events using the VAV with Reheat model with the implementation of the control sequence VAV 2A2-21232  [49]. It consists of a five-zone building layout modeled after one floor of the Medium Office standard prototype building. We simulated this model and processed some of the partial results using the software Dymola 2022  [15]. Similar to the EnergyPlus models, the event is measured relative to an undisturbed simulation instance under the same loading conditions.
Second, the intentional model design allows for transparent interpretation by both building managers and grid operations, thereby increasing the trust in its use. Third, the intentional model simplicity allows for the easy adoption and targeting of existing buildings with the collection of a few BAS data points and parameters. Future work includes the validation of the proposed model using multiple systems in a real building. dditionally, it is important to study the application of this model to longer events (e. g. , 2-4 h) and higher GTA inputs (e. g. þ4 F). Furthermore, we would like to pilot larger studies with multiple buildings of varied VAV architectures and uses will help understand the challenges related to model acquisition. Another important future line of work  involves the process of re-commissioning building controls that are inflexible to improve energy efficiency and allow for more flexibility.
An integrated tumor, immune and microbiome atlas of colon cancer The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further m ic ro bi ome c ha ra ct er iz ation. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications.
Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.
1b ). As a first objective, we conducted a validation of the ICR signature on the AC-ICAM cohort. This objective was predefined before data were generated (prospective validation of retrospectively collected samples; Methods provides detail). A consensus-clustering approach based on the ICR genes (Extended Data Fig. 2a, b ) segregated the cohort in three clusters/immune subtypes: ICR high (hot tumors), ICR medium and ICR low (cold tumors) (Fig. 1b ). Systematic transcriptomic analysis using 103 previously defined immune traits (Methods) revealed co-clustering of these traits into seven different modules (M1-M7) (Extended Data Fig. 3 ), with ICR belonging to M2 (lymphocyte infiltration signature), together with other immune signatures, including the tumor inflammation signature  9. We then characterized the immune disposition in relation to Consensus Molecular Subtypes (CMS)  16  , a well-defined transcriptomic-based classification of colon cancers.
The strength of the in situ adaptive immune reaction, as captured for instance by the evaluation of the density and spatial distribution of T cells (Immunoscore), is associated with a reduced risk of relapse and death independently of other clinicopathological variables, including MSI status  4, 5. However, despite the overwhelming evidence of the prognostic effect of the Immunoscore and other immune-related parameters in colon cancer  6, 7  , a lack of association between gene-expression-based estimates of immune response and patient survival in The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) cohort has been noted by the research community  [8] [9] [10]. TCGA, for its genomic data richness and curation, represents the preeminent dataset for omics analyses; however, the collecting of comprehensive clinical data, including survival outcomes was neither a primary objective of TCGA nor a practical possibility in view of its worldwide scope and time constraints  11.
As such, the limited patient follow-up data associated with TCGA-COAD and other TCGA datasets has hindered statistically rigorous survival analyses  11. In addition, TCGA did not include dedicated assays for T cell receptor (TCR) repertoire analysis or microbiome characterization, which was later performed using bulk DNA and RNA sequencing (RNA-seq) data and includes only few healthy solid tissue (for example healthy colon) samples  12, 13. Furthermore, as TCGA focused initially on cataloging genomic and molecular changes that occur in cancer cells, sample inclusion criteria based on stringent tumor purity cutoffs were imposed  14  , potentially biasing the population toward less-immune-or stroma-rich tumor specimens. In recent years, while quantitative features of primary colon cancer, including those that are cancer cell intrinsic, immunological, stromal or microbial in nature, have been reported to be significantly associated with clinical outcomes, individually  [15] [16] [17]  , knowledge of how their interactions impact patient outcome is fragmentary.
AC-ICAM overview Fresh-frozen tumor samples and matched neighboring healthy colon tissues (tumor-normal pairs) from systemic treatment-naive, patients with histological diagnosis of colon carcinoma were profiled with orthogonal genomic platforms. After cross-platform quality control (based on whole-exome sequencing (WES) and RNA-seq data) and inclusion criteria checking, genomic data from 348 patients were retained and used for downstream analyses (Fig. 1a  and Extended Data Fig. 1a, b ; Methods provides further details). The median follow-up time was 4. 6 years. We refer to this resource as the Sidra-LUMC AC-ICAM: an Atlas and Compass of Immune-Cancer-Microbiome interactions. Fig. 1  | AC-ICAM study design, immune-related gene signatures, immune and molecular subtypes and survival. a, Samples from a total of 348 patients with colon cancer were included in AC-ICAM.
1e-g ). Conversely, CMS did not stratify the ICR-high cluster (Extended Data Fig. 2g ). ICR remained significantly associated with improved OS in the Cox multivariate analysis (together with pathological stage and age), whereas microsatellite instability (MSI) status and CMS did not (Supplementary Table  2 ). The relationships between ICR and CMS depicted in Fig. 1  were confirmed in the TCGA colon cancer cohort (TCGA-COAD; Supplementary Fig. 2 ). Overall, in TCGA, the survival differences were attenuated (in the PFS analysis) or absent (in the OS analysis) for ICR, immune infiltrates and CMS. Nevertheless, ICR still stratified survival in patients with CMS4 cancers (Supplementary Fig. 2 ; PFS analysis). Overall, we validated the prognostic role of ICR in colon cancer.
Somatic alterations associated with weak immune response We sought to identify potential drivers of immune responsiveness related to cancer cell somatic alterations, such as mutations and copy-number variations by performing WES (Extended Data Fig. 5a, b ) on 281 tumor samples and corresponding healthy tissue. In terms of somatic mutations, the tumor mutational burden (TMB) of the AC-ICAM dataset was highly comparable to the TCGA-COAD cohort (Fig. 3a ), as were the clinicopathological parameters (Supplementary Fig. 3 ). Unlike the TCGA-COAD cohort, however, inclusion of samples in our study did not depend on tumor purity. In fact, stromal and immune content (ESTIMATE score) and the infiltration of individual lymphocyte subpopulations (Fig. 3b  and Supplementary Fig. 4 ) was significantly increased in the AC-ICAM compared to the TCGA-COAD datasets, whereas the opposite was observed for cancer-cell-intrinsic signatures (Supplementary Fig.
Microbiome composition in healthy and colon cancer tissue We sequenced the 16S rRNA gene using DNA extracted from matched tumor and healthy colon tissues from 246 patients (Fig. 5a ; AC-ICAM246 cohort). This dataset was used for the microbiome landmark analysis. Whole-genome sequencing (WGS, median coverage 76×) was performed in a subgroup of these samples (n = 167; Fig. 5b ) for technical validation. For validation purposes, once the landmark analysis was completed, we analyzed 16S rRNA gene-sequencing data from 42 additional tumor samples for which no matched normal DNA was available for this assay (referred here as ICAM42 cohort, see also Fig. 1a ). After applying the same abundance filter to AC-ICAM246 and TCGA-COAD datasets, AC-ICAM captured all the genera detected in TCGA-COAD  13  , which displayed almost identical co-correlation patterns in the two cohorts, in additional to several other genera (Supplementary Fig.
bromii through the administration of castalagin (an ellagitannin found in certain aliments  including the berry Myrciaria dubia), resulting in enhanced antitumor immunity, possibly mediated by boosting antigen presentation and T cell response  59. Administration of Myrciaria dubia powder concomitantly with immune checkpoint inhibitors is currently being explored in patients with melanoma and non-small-cell lung cancer (NCT05303493). Our study has some notable limitations. While the cohort was relatively large and compares favorably with the TCGA-COAD colon cohort (for example, ~50% OS events more in AC-ICAM versus TCGA-COAD  60  ), it remains underpowered for stage-specific survival analysis. For the mICRoScore, we were unable to assess and quantify potential data overfitting as we did not reserve internal samples for this purpose; however, we observed a good performance of the mICRoScore in the external validation cohort (TCGA-COAD), which may be due to the combination of two biologically relevant variables (ICR and MBR) into the model.
This combination likely contributed to the model's impact and suggests that the mICRoScore might be generally applicable. We did not perform in situ spatial profiling, which could reveal more complex spatial immune-microbiome interactions  61. Additional research is needed to confirm the validity of the mICRoScore and investigate its potential applications in clinical treatment decision-making. Both the mICRoScore and IES could be tested in the context of cancer immunotherapy as predictive biomarkers. Data from the NIBIT-M4 trial and publicly available datasets suggest that the combination of the genetic immunoediting and ICR (IES) has predictive value in melanoma patients treated with immune checkpoint inhibitors  62. The quantification of the immunoediting using WES data is an emerging subject of research  63, 64.
Survival analysis Kaplan-Meier curves were generated using ggsurvplot from R package survminer (v. 0. 4. 9). HRs between any two groups of interest and corresponding P values based on a Cox proportional hazard regression analysis and 95% confidence intervals (95% CI), were calculated using R package survival (v. 2. 41-3). Cox proportional hazard analysis was only computed when both groups of comparison consisted of at least ten patients. Overall P value for comparison of survival between two or more groups was also calculated by log-rank test. Multivariate Cox regression was performed using conventional clinical and biological variables, as explained in the specific section. Separate multivariate Cox regression analyses were run including age (continuous), pathological stage (ordinal), MSI status (binary) and CMS (categorical).
Transcriptomic data processing Data conversion and demultiplexing was performed using bcl2fastq2 conversion software (v. 2. 20). FastQC was run to perform QC checks on the raw sequence data (Python v. 2. 7. 1, FastQC v. 0. 11. 2). Trimming of adaptor sequences was performed using flexbar (v. 3. 0. 3) using Illumina primers FASTA file. Subsequently, reads were aligned to reference genome GRCh38. 93 by Hisat2 (v. 2. 1. 0) using SAMtools (v. 1. 3). After alignment, QC was performed to verify quality of the alignment and paired-end mapping overlap (Bowtie2, v. 2. 3. 4. 2). Finally, the feature-Counts function of subreads (v. 1. 5. 1) was used to count paired reads per Resource https://doi. org/10. 1038/s41591-023-02324-5 genes. Gene expression normalization was performed within lanes, to correct for gene-specific effects (including GC content) and between lanes, to correct for sample-related differences (including sequencing depth) using R package EDASeq (Exploratory Data Analysis and Normalization for RNA-seq) (v.
TCGA data RNA sequencing. RNA-seq data (raw counts) from TCGA were downloaded and processed using R package TCGAbiolinks (v. 2. 18. 0). Gene symbols were converted to official HGNC gene symbols and genes without symbol or gene information were excluded. Normalization was performed within lanes, to correct for gene-specific effects (including GC content) and between lanes, to correct for sample-related differences (including sequencing depth) using R package EDASeq (v. 2. 12. 0) and quantile normalized using preprocessCore (v. 1. 36. 0). After normalization, samples were extracted to obtain a single primary tumor tissue (TP) sample per patient. Clinical data were sourced from the TCGA Pan-Cancer Clinical Data Resource  11  and survival events OS and progression-free interval (relabeled here as PFS) were used.
ICR clustering and calculation of ICR score was performed exactly as described for the AC-ICAM cohort. For the TCGA-COAD cohort, the optimal number of clusters for best segregation based on the Calinski-Harabasz criterion was three. CMS classification of TCGA-COAD samples was performed as described for the AC-ICAM cohort. The Single Sample Predictor by 'CMSclassifier' (v. 1. 0) was used for comparison of CMS classification between AC-ICAM and TCGA-COAD. A renormalized matrix of both TCGA-COAD and AC-ICAM datasets was generated by merging the raw counts matrices and performing the EDASeq normalization, as described above, on this combined matrix. These data were used to calculate ssGSEA scores for deconvoluted immune cell subpopulations, immune signatures and oncogenic pathways, to compare between cohorts.
Transcriptome analysis ICR score and clustering. Consensus clustering based on 20 a priori selected ICR genes (IFNG, IRF1, STAT1, IL12B, TBX21, CD8A, CD8B, CXCL9, CXCL10, CCL5, GZMB, GNLY, PRF1, GZMH, GZMA, CD274/PDL1, PDCD1, CTLA4, FOXP3 and IDO1)  21  , was applied to the normalized log 2 -transformed expression matrix using R package ConsensusClus-terPlus (v. 1. 42. 0) 69 using 5,000 repeats, agglomerative hierarchical clustering with Ward criterion inner and complete outer linkage. The optimal number of clusters allowing for the best segregation of samples was based on the Calinski-Harabasz criterion. Optimal number of clusters used for segregation was three. Colon cancer samples in the cluster with the highest expression of ICR genes were designated as 'ICR high', the intermediate cluster as 'ICR medium' and the cluster with the lowest expression was designated 'ICR low'.
0. 15) and visualized using ggplot2 (v. 3. 3. 2). The t-SNE plot was annotated with distinct colors to visualize the distribution of samples of different CMS (using random forest method) in high-dimensional space. The same t-SNE plot was annotated by ICR cluster in a separate panel. A circos plot to visualize the relation between CMS and ICR classifications was generated using the chord-Diagram function from R package 'circlize' (v. 0. 4. 8).
Immune cell deconvolution and ESTIMATE. Consensus tumor microenvironment cell estimation (ConsensusTME)  70  was performed to estimate relative abundancies of specific immune cell subsets from bulk transcriptome data. This method relies on integrated gene sets from multiple sources that have been curated and validated on a per-cancer-type basis, using benchmark datasets and seems to outperform previously published methods  70. We applied ConsensusTME using R package ConsensusTME (v. 0. 0. 1. 9) using parameters 'COAD' to specify cancer type and 'ssgsea' as statistical method. The median of each ConsensusTME score was calculated per CMS stratified by ICR cluster and was displayed in a dotted heat map using R package ComplexHeatmap (v. 2. 1. 2). The association of each Con-sensusTME score with OS and PFS was calculated by Cox proportional hazard regression.
HR and corresponding 95% CIs as are displayed as forest plots (forestplot v. 2. 0. 1). To infer estimated levels of overall stromal and immune cell infiltration to the tumor, the ESTIMATE algorithm (v. 1. 0. 13) was applied to the expression data in R. ESTIMATE was run for both TCGA-COAD dataset and the AC-ICAM cohort. The combined ESTIMATE score for both the stromal and immune signature was compared between cohorts and a box-plot was generated using ggplot2 (v. 3. 3. 2).
RS)  74  , calculated using ssGSEA. Finally, the deconvoluted immune cell abundancies by ConsensusTME  70  and ICR score  10  were included among the immune traits. In total we used 103 immune traits (including ConsensusTME) (Supplementary Source Data 6 provides gene signatures and corresponding references). The pairwise Pearson correlation between all immune traits was calculated and the resulting correlation matrix was plotted using ComplexHeatmap (v. 2. 1. 2) with hierarchical clustering. Co-clustering immune traits that formed distinct modules were visualized and labeled according to the immune traits' enrichment. The clustering was compared to previously defined immune trait modules within a pan-cancer setting, by annotation of the correlation matrix with the previously defined clusters in Sayaman et al.
Values for (productive) clonality range from 0 to 1, with values near 0 reflecting more polyclonal samples and values near 1 representing samples with just few predominant rearrangements dominating the observed T cell repertoire (TRB gene). A high T cell clonality implies presence of expanded T cell clones. Relationships between ICR score, immune traits, number of productive templates and productive clonality were tested using Pearson's correlation and visualized by scatter-plots using ggplot2 (v. 3. 3. 2). Similarly, Pearson's correlation coefficient was calculated between productive clonality and each of the 18,270 genes in the expression matrix. A volcano plot was used to visualize significant results (ggplot2). The top 50 genes with the highest correlation with TCR productive clonality were mapped to the Global Molecular Network and core network analysis was performed using Ingenuity Pathway Analysis software.
Pearson's correlation coefficient between the fraction tumor-enriched TCR sequences and ICR score was calculated. MiXCR for TCR repertoire derived from bulk RNA-seq. The software MiXCR (v. 3. 0. 13)  30  was used to retrieve the VDJ repertoire from bulk RNA-seq data aligned to reference genome GRCh37. MiXCR was run through docker and with the single command analyze shotgun. The R package 'immunarch' was used to analyze the MiXCR output into the R environment. For the TCRβ locus (TRB), the TCR clonality was calculated as 1 - normalized Shannon entropy (see Calculation section for details) for all samples, except seven cases for which MiXCR failed to identify clones.
Whole-exome-sequencing data analysis Somatic mutation calling and small insertions and deletions. SNVs were called using mutect (v. 1. 1. 7) and somatic small insertions and deletions (indels) using strelka2 (bcbio-nextgen v. 1. 1. 1). We applied an optimized variant filtering pipeline (Extended Data Fig. 5b ). To filter out false-positive single-nucleotide polymorphism calls, fpfilter was used, the applied filtering parameters are specified in the fpfiler. pl script shared on GitHub. Subsequently, MAF files were generated using VCFtoMAF tool (v. 1. 6. 16), which also appended the SIFT (sorting intolerant from tolerant), PolyPhen and Exome Aggregation Consortium annotations. MAF files were loaded into R where indels with low complexity regions were excluded. For both SNVs and indels, a cutoff for minimum allele fraction of 5% and tumor depth of more than three reads was applied.