dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally conduction heat transfer in the thickness of materials and in air as well as solar powe directly impacting the receiver are neglected. At last, the protocols is designed to ensur that there is no concentrated solar flux at the receiver during measurements. Thus, the disparity in the results expressed by the R 2 = 0. 91 shows the strong dependence of heat losses on wind conditions. However, due to the few experimental points and because the local wind conditions are not precisely monitored along the entire lines (a single weather station for the entire site), it is difficult to define an empirical functional relation with wind conditions. Thus, a 1D single-phase thermohydraulic model based on MATLAB is developed to express theoretical heat loss correlations, however, limiting it to the influence of the wind speed for the sake of simplicity.
--------------In order to meet the above challenges, in this paper, we propose a new SNA-DF based on Neighbor2Neighbor method. Our contributions can be summarized as follows:  We develop a novel framework for speech denoising using single noisy audio samples, putting an end to the need for noisy-clean pairs and multiple noisy audio samples.  We design an effective complex-valued speech denoising network, which inserts the proposed complex TSTM (cTSTM) into the DCUnet by modeling the correlation between magnitude and phase.  Our method performs very favorably against compared denoising methods not only on synthetic datasets, but also on real world datasets, demonstrating the superiority and potential of our denoising method.
Experimental setup Datasets. The dataset is generated by overlapping two noise categories over clean audios. The clean audios are selected from Voice Bank + DEMAND dataset  [14]. While the first noise dataset uses white gaussian to generate synthetic noisy audio dataset, the second is UrbanSound8K (US8K) dataset  [15] , which is used to generate real world noisy audio dataset. Evaluation metrics. To evaluate the quality of denoising effect, the following metrics are used: signal-to-noise ratio (SNR), segmental signal-to-noise ratio (SSNR), wide-band perceptual evaluation of speech quality (PESQ-WB)  [16] , narrow-band perceptual evaluation of speech quality (PESQ-NB)  [16] , short term objective intelligibility  [17].
Datasets This study utilizes multiple datasets, including administrative division data 57 , MOD13A2 normalized vegetation index data  58  , MCD12Q1 land cover type data  59  , MOD11A2 land surface temperature data (LST)  60  , MOD09A1 land surface reflectance data  61  , the ERA5-Land reanalysis dataset  62  , WorldPop population density datasets  63  , the impervious surface data  64  , the harmonized global nighttime light dataset  65  , Global Urban Boundaries data  66  and climate zone data  67. The administrative division data came from the National Catalogue Service for Geographic Information (NCSGI. ). MODIS data were obtained from the National Aeronautics and Space Administration (NASA); MOD13A2 and MOD11A1 were used as indicators to evaluate the CPUG on UST, and MCD12Q1 was used as precision verification data for urban boundaries.
MOD09A1 was used to calculate the impermeability index (IBI)  68. The IBI (UI), MOD13A2 (UG), MOD11A1 (UST), ERA5-Land reanalysis, WorldPop population density and Harmonized global nighttime light (NTL) datasets were used for sensitivity analysis. CHEQ data were obtained from the National Earth System Science Data Center and used to evaluate the urban eco-environmental benefits brought by UG. Detailed information on the data used in this study is shown in Supplementary Table  4.
Method flowchart The method flowchart and concept map of this study are shown in Supplementary Fig. 8. The study process comprises five main parts. First, all data were resampled to a 1 km resolution. Second, the Global Urban Boundaries data were combined for all years for maximum value synthesis to extract the LUB and then verified with MCD12Q1 data. Third, we synthesized the annual UI, UG and UST after noise removal by the Savitzky-Golay (SG) filter  69.
Code availability In order to enhance the efficiency of CPUG calculations, we developed a Node. js and ML. js-based program for quantitative evaluation of the impact of UG on UST. This program enables convenient simulation of UST changes in various cities across the globe. The ultimate goal of this research is to encourage fellow scholars to further investigate this topic in greater detail. The program code can be accessed at https://github. com/codeOH/simulated. git. Extended Data Fig. 2 | Ranking in descending order of CPUG (a) and linear relationship between CPUG and contribution to climate mitigation (b) in 79 eligible cities.
To evaluate ENLIGHT's performance, we analyzed 21 new cohorts not analyzed in the SELECT study, overall spanning 697 patients receiving 15 different drugs in 11 cancer indications, and, importantly, kept them blinded for evaluation purposes. We find that while SELECT is reassuringly predictive on the subset of those datasets for which it could produce results, ENLIGHT still performs better when evaluated using clinically relevant performance measures. We show that (1) ENLIGHT produces predictions for all the drugs used in these cohorts; (2) high ENLIGHT-matching scores (EMS) are associated with better response (odds ratio [OR] = 2. 59; 95% confidence interval [CI] [1. 85, 3. 55]; p = 3. 41eÀ-8). (  3 ) ENLIGHT can be used to successfully exclude patients from clinical trials, achieving overall more than 90% of the response rate attainable under an optimal exclusion strategy for both immunotherapies and targeted monoclonal antibody (mAb) treatments.
Tuning and evaluation datasets To tune the parameters for ENLIGHT, including the GI network size and a decision threshold on the EMS, which is used for predicting a favorable response (see below), we selected eight tuning sets-six datasets  [34] [35] [36] [37] [38] [39]  already analyzed in Lee et al. 29  along with two other datasets  40, 41  from the public domain. These were selected as they span a range of different treatments, therapeutic classes, response rates, and sample sizes, reflecting diverse real-world data, covering five targeted therapies and one ICB therapy. To study the value of ENLIGHT on real-world data, which was not previously analyzed by either SELECT or ENLIGHT, we surveyed the public domain (GEO,  42  ArrayExpress,  43  CTRDB,  44  and the broader literature) for available datasets of patients receiving targeted or immunotherapies, containing both pre-treatment transcriptomics and response information.
In addition, we publish here a new dataset of breast cancer patients who received alpelisib or ribociclib that was obtained as part of a collaboration with Massachusetts General Hospital. Overall, we identified, obtained and set aside 21 datasets  20, 24, 34, 39, [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61]  -the evaluation sets-as unseen data for evaluation. See Table  S1  and STAR Methods for full details. Overview of the ENLIGHT pipeline ENLIGHT's drug response prediction pipeline generally follows the flow of SELECT,  29  which comprises two steps (Figure  1A ): (1) given a drug, the GI engine identifies the clinically relevant GI network of the drug's target genes. As was done in SELECT, we start with a list of initial candidate SL/SR pairs by analyzing cancer cell line dependencies with RNAi, CRISPR-Cas9, or pharmacological inhibition, based on the principle that SL/SR Figure  1.
(C) The PPV (precision) and sensitivity (recall) on the aggregate tuning cohort (y axis) as a function of the decision threshold (x axis). interactions should decrease/increase tumor cell viability, respectively, when activated (e. g. , in the SL case, when both genes are downregulated). Among these candidate pairs, we select those that are more likely to be clinically relevant by analyzing a database of tumor samples with associated transcriptomics and survival data, requiring a significant association between the joint inactivation of a putative SL gene pair and better patient survival, and analogously for SR interactions. Finally, among the candidate pairs that remain after these steps, we select those pairs that are supported by a phylogenetic profiling analysis (see STAR Methods for more details).
(5) To improve the prediction engine, for immunotherapy and other mAbs, which are highly target specific, the EMS incorporates the expression of the target as an additional score component. The ENLIGHT pipeline is described in detail in STAR Methods, where we also describe a web service enabling researchers to apply ENLIGHT to RNA expression data. We emphasize that the GI networks are generated without using any treatment response data. Notably, the parameters of ENLIGHT, namely the network size and thresholds for the GI engine tests, are optimized on the tuning datasets, but their values are then held fixed to predict all treatment outcomes across all 21 evaluation sets. These conservative procedures markedly mitigate the risk of deriving overfitted predictors that would not predict response in new datasets.
Importantly, the decision threshold, set on the tuning cohorts, is fixed, and is used as is in evaluating ENLIGHT's performance on all cohorts and treatments. The EMS distributions in the tuning cohorts show that the EMS are significantly higher among responders compared with non-responders (Figure  1B ), testifying to its discriminatory power. Figure  1C  shows that a decision threshold of 0. 54, which will be used henceforth, maximizes the PPV while still maintaining a reasonable sensitivity on the tuning cohort (for the dataset-specific PPV and sensitivity curves see Figures  S1  and S2 ). ENLIGHT's performance is evaluated using the OR for response, which denotes the odds to respond when the treatment is recommended (ENLIGHT-matched, i. e. , EMS above the decision threshold), divided by the odds when it is not.
ENLIGHT successfully predicts patient treatment response in 21 different test datasets We next turned to evaluate how ENLIGHT performs in identifying the true responders in the 21 unseen patient cohorts that we have collected, spanning ICBs, mAbs, and targeted small molecules. Notably, the response data for all evaluation cohorts was unblinded only after finalizing the ENLIGHT pipeline, including fixing the decision threshold and calculating EMS for all patients. Figure  2A  shows that ENLIGHT-matched treatments are associated with better patient response (OR > 1) in all cohorts except for two (sorafenib 2 and one ICB cohort), with an aggregate OR of 2. 59 (95% CI, 1. 89-3. 55; p = 3. 41eÀ8, n = 697). Correspondingly, Figure  2B  shows that the overall PPV obtained for ENLIGHT-matched cases is markedly higher than the overall response rate (52% versus 38%, a 36.
1, **p < 0. 05. alternatives with high EMS scores for each patient. We observe that 91/96 patients (94. 8%) had at least one treatment with which they were ENLIGHT-matched, highlighting the potential coverage of ENLIGHT in real-world cases. Except for tuning very few parameters on the tuning cohorts, as described above, ENLIGHT is essentially an unsupervised prediction method that relies on a series of biologically motivated statistical tests that underlie the identification of GIs and their utilization and is not trained on treatment outcome data. We next turned to compare its predictive power with that of transcriptomics-based biomarker signatures generated via supervised machine learning on specific training cohorts. To this end, we have studied four of the evaluation datasets, for which a supervised classifier was presented in the original publication and data enabling us to perform this comparison was provided (Figure  3A ).
In one of these datasets, Sammut et al. 20  used supervised learning to predict response to chemotherapy with or without trastuzumab among HER2+ breast cancer patients. We focused on the subset of 56 patients who received trastuzumab and for which the model's scores were published. ENLIGHT achieved a slightly higher AUC than that reported in the original study, without ever training on these data, nor on any other data of response to trastuzumab, and without using any clinical features. Similarly, Cui et al. 24  developed a biomarker for response to anti-PD1 in melanoma (see anti-PD1 5 in Table  S1 ) and Raponi et al. 54  and Raponi et al. 55  developed biomarkers for response to tipifarnib in AML to which we compared ENLIGHT's performance. Overall, in three of the four datasets, ENLIGHT's predictive performance is comparable with that of the biomarkers developed using a supervised approach for a specific treatment and indication.
However, in one of these datasets (''Raponi et al. Blood''), while ENLIGHT has a slightly higher PPV, its sensitivity is much lower than that achieved by the supervised method. Taken together, these results demonstrate the remarkable predictive power of ENLIGHT as a pan-cancer and pan-treatment response predictor, which quite successfully competes with supervised models specifically trained for narrow predictive scenarios. We next turned to compare ENLIGHT's performance to (1) that of its predecessor, SELECT, (2) several known transcriptomics-deduced metrics (interferon [IFN]-g signature,  17  proliferation signature,  64  cytolytic index,  65  and the drug target expression levels), and, finally, (3) several GI-based scores. This comparison was done comprehensively on all 21 evaluation cohorts (see STAR Methods).
Finally, (4) we added a comparison to three ICB specific biomarkers (exhausted T cell 66 TIDE  16  and CD8+ T cell abundance  67  ) on the immunotherapy datasets. The performance of ENLIGHT could not be compared with that achieved by DNA-based markers, such as TMB or MSI, since all evaluation datasets available include only mRNA data. We note that several of the widely used biomarkers included in these comparisons do not provide differential scoring for different drugs, i. e. , they cannot be used to guide therapy (e. g. , proliferation signature), while others are specific to predicting response to ICB (e. g. , exhausted T cell). Nevertheless, all are included for completeness. As all biomarkers other than SELECT and TIDE do not suggest a threshold for determining favorable treatments, we set the decision threshold for those using the tuning sets to match ENLIGHT's recall (see STAR Methods).
For TIDE, we used the threshold of 0, as suggested in Jiang et al. ,  16  and for SELECT we used 0. 44, as suggested in Lee et al. 29  For 11 of the 21 evaluation cohorts (n = 400), the GI networks produced by SELECT were too small to produce robust predictions (having less than four GIs). Figure  3B  compares the OR of ENLIGHT and SELECT on the remaining 10 cohorts (n = 297). While SELECT provides a beneficial discriminatory power on the evaluation cohorts to which it could be applied, especially for ICBs (Figure  3D ), ENLIGHT can analyze a wider range of treatments than SELECT, with overall higher precision. Figures 3C and 3D compare ENLIGHT's performance to all the biomarkers enumerated above. This is presented separately for non-ICB cohorts (n = 512, the selinexor dataset was excluded since IFN-g, cytolytic index and target expression could not be calculated on this dataset due to missing data) and for the ICB cohorts (n = 152, the anti-PD1+-Anti-CTLA4 dataset was excluded since missing data prevented calculation of the proliferation signature), since three of the markers are (D) OR (y axis) of ENLIGHT (blue) and the other biomarkers detailed in (C), as well as SELECT and three ICB-specific transcriptomic biomarkers, calculated for the ICB cohorts (n = 161).
Biomarkers denoted by ''X'' and ''Y'' (e. g. , ''ENLIGHT & IFN-g'') were calculated as the geometric mean of X and Y. ''Predictive biomarker'': a biomarker that is drug specific, i. e. , yields a different prediction per drug. ''Prognostic biomarker'': a biomarker that is drug agnostic, i. e. , it is predictive of patient survival/general inclination of response but cannot be used to guide therapy. ''Predictive biomarker-ICB specific'': a biomarker that is used to predict general response to immunotherapy but without differentiation between treatments. Asterisks represent the corrected p value for a one-sided test of an OR greater than 1: *p < 0. 1, **p < 0. 05. ICB specific, and since SELECT could only be applied to a few of the non-ICB datasets, as described above. Table  S4  contains the detailed statistics of all comparisons.
In short, ENLIGHT has significantly higher OR than SELECT on the datasets for which SELECT could produce predictions (Figure  3B , p = 0. 0487). In addition, ENLIGHT has higher OR than all other markers, with the improvement being statistically significant in all non-ICB datasets (Table  S4 ). In light of the high predictive power of several ICB-specific markers (namely, proliferation signature, cytolytic index, and IFN-g signature), we studied if ENLIGHT's combination with these biomarkers can further enhance the overall predictive power for response to ICB. To this end, we tested the predictive power of new combined biomarkers computed as the geometric mean of: (1) ENLIGHT and each of the three individual markers; (2) ENLIGHT and all three biomarkers; and (3) the three markers without ENLIGHT (for comparison).
For each percent of patient exclusion (columns), the response rate among the remaining patients when excluding based on increasing EMS is given as a percentage of the upper bound response rate achieved by the ''all knowing'' optimal classifier that excludes only true non-responders. predictive on very narrow and specific treatment cohorts. This comparison is of much interest since, in theory, given sufficiently large datasets, such supervised methods are expected to yield higher performance on unseen datasets than unsupervised methods like ENLIGHT. However, if the training sets available are small (as is regrettably the case with the vast majority of the data currently available), practice may differ from theory and supervised methods may overfit and underperform. ENLIGHT is also more predictive than its predecessor, SELECT, and other transcriptomics-based biomarkers.
Some of the biomarkers in these comparisons are prognostic in nature, i. e. , provide information about the patient's overall cancer outcome, regardless of therapy (e. g. , proliferation signature) or are specific to predicting response to ICB (e. g. , T cell exhaustion). Unlike ENLIGHT, such markers cannot add information when one aims to choose between various treatments. Specifically, IFN-g had an OR comparable with that of ENLIGHT on the ICB datasets, which are almost exclusively anti-PD1. However, unlike ENLIGHT, it will not be able to offer differential scoring as more ICB treatments enter the clinic, since its score is not treatment dependent. The combination of ENLIGHT and IFN-g has increased predictive performance for ICB therapy, obtaining an impressive OR of 4, and offering differential scoring per treatment, due to ENLIGHT's predictive nature.
While ENLIGHT's performance is evaluated here on a broad array of unseen clinical trial datasets, its performance should obviously be further evaluated in prospective studies. Indeed, based on the results obtained so far by SELECT and ENLIGHT, the design of such a multi-arm study-SYNTHESIS-is now underway at the National Cancer Institute's Center for Cancer Research. We hope that the results presented here will elicit additional prospective studies in other clinical centers across the globe and we are committed to facilitating such studies.
Limitations of the study Like any response prediction approach, ENLIGHT has several limitations that should be acknowledged. First, as it operates on the drug target level, it has very limited utility in predicting response to chemotherapies and, more generally, its prediction accuracy depends on the accuracy with which the targets of a given drug have been identified. Second, most datasets included in this study are based on mRNA expression derived from microarrays, while the standard is shifting toward RNA-seq data. Third, current applications of ENLIGHT have focused on bulk tumor transcriptomics. Future work is needed to study its application for analyzing single-cell tumor transcriptomics, to build predictors that consider tumor heterogeneity and the important interplay between a tumor and its immune microenvironment.
Fourth, while gene expression is at times strongly associated with protein levels, in other cases it is not, especially for lowly expressed genes. Notably, some of these limitations may point us to future directions for building even better GI-based predictive approaches. This includes approaches for identifying and harnessing cell-type specific interactions from single-cell and deconvolved expression data, approaches that chart the GI landscape at different regions of the tumor, and more. Finally, one may reasonably anticipate that the advent of much larger molecular and response datasets will on its own contribute to the accuracy of the methods already extant.
STAR+METHODS STAR+METHODS KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER evaluations or (ii) binary classifications of responders and non-responders that was not exclusively defined using RECIST and in several cases was not specified. In this study, we classify a patient as responder if he/she had a RECIST evaluation of CR/PR, or if he/she had a binary classification of responder. The rest were classified as non-responders. In each dataset we only analyzed patients for whom both pre-treatment transcriptomics and response data was available. N in the table denotes the number of patients that were analyzed. The Selinexor dataset encompasses data from a clinical trial containing several arms, each with a different treatment dosage which significantly affected the response rate. Here we only analyzed the largest arm of the trial (N = 24).
Scoring The ENLIGHT Matching Score (EMS) is defined as the fraction of SL/SR interactions that are in an advantageous predisposition for drug admission. That is, a drug that inhibits gene A is expected to work better in patients for whom an SL/SR-DU B of A is underexpressed, or for whom an SR-DD partner B of A is overexpressed. Thus, the fraction of under/over expressed partners in advantageous states (with respect to the interaction type) is expected to be positively associated with response. A gene is determined to be underexpressed if its normalized expression is equal to or below 1/3 (i. e. is in the bottom tertile across samples in the same dataset), or overexpressed if its normalized expression is equal to or above 2/3 (i. e. is in the top tertile across samples in the same dataset), similar to Lee et al.
Biomarkers compared with ENLIGHT In order to compare the OR for identifying beneficial treatments between different biomarkers, the focus and objective of this study, one has to set a clinical threshold for binary response classification. For two of the biomarkers compared in this study, such thresholds were given in the original publications. For SELECT, we used the threshold of 0. 44 as described in Lee et al. 29  and for TIDE we used the threshold of 0 as suggested by Jiang et al. 16  For the remaining markers, we used the tuning sets to assign thresholds. For each biomarker, we calculated its scores in the tuning datasets, and searched for a threshold that matches ENLIGHT's recall: 1. For comparing ENLIGHT to other biomarkers on non-ICB datasets (Figure  3C ), we identified a threshold for each of the presented biomarkers based on all tuning datasets except Anti-PD1.