dataesr/scientific-paragraphs-categorization huggingface dataset:
Both models achieve similar results using only HRV features, without combining multiple modalities, as opposed to some of the studies in the table. It shows that the RF model achieved a slightly lower accuracy of 67% on the MMSD dataset, however, it had a higher accuracy of 81% on the UWS dataset. This discrepancy in performance can be attributed to various reasons such as the dataset size and diversity. Models perform best when the data are diverse and contain a variety of features and samples. Furthermore, the use of different physiological signals in some studies may also account for the good results. This is because stress can manifest in differently in various physiological systems, and by considering multiple signals, the model may be able to better capture these different stress responses.
Even using only HRV features, some studies have achieved slightly better scores using RF and LR models compared to the results obtained in this study  [36] [37] [38]. However, the use of the Leave-One-Subject-Out (LOSO) cross-validation method in this study may account for the lower performance. An RF model applied the MMSD dataset without using a LOSO cross-validation, resulted in an f1 score greater than 80%  [39]. Studies using the same procedure also tend to yield similar results  [40, 41]. The main advantage of LOSO is that it makes the most efficient use of the available data. It provides an estimate of the model's performance on new data, which is important when the goal is to generalize the model to new subjects. Additionally, LOSO is especially useful when the number of subjects is small as is the case for the datasets used in the study.
It is also important to keep in mind that accuracy, as presented in Table  6 , is not an effective metric for evaluating imbalanced datasets. In such cases, the majority class can skew the accuracy metric, leading to a false sense of high model performance even if the model is not accurately predicting the minority class. Other metrics such as f1-score and class-specific metrics are more appropriate for evaluating performance on imbalanced datasets. For instance, even though the LR model on the UWS dataset has a seemingly high overall accuracy of 70%, it still has poor performance on the minority class (stress) with an f1-score of only 45%. Next, RF and LR models are evaluated using cross-database validation as a semisupervised learning framework where models are trained on the MMSD dataset and tested on UWS dataset.
Cross Dataset Analysis The same f1 score of 63% is reached in the cross-dataset analysis and the MMSD independent analysis using the RF model. For the LR model on the other hand, a loss of about 14% can be noticed on the f1-score in the cross-dataset analysis. The stress class seems to be less well recognized by both models. Although the LR model gave better classification results in the independent analysis of the MMSD dataset, the RF model is more generalizable. This is because the RF model is less prone to over-fitting issues thanks to parameters tuning. Moreover, for the final decision, the RF classifier aggregates the decisions of individual trees; consequently, RF classifier exhibits good generalization capabilities. In  [41]  as well, the RF model was found to have good generalizability across datasets.
The results showed that while RF achieved slightly lower F1-score and Accuracy than other HRV based models on within dataset evaluations for binary stress classification, it outperformed them in the cross-dataset analysis. Moreover, ref. [42]  also demonstrates the generalization capabilities of RF models across different datasets using HRV features alone and coupled with electrodermal activity features. The results from both studies confirm that RF is robust against variations in environmental factors such as recording devices or types of stressful scenarios. According to ROC-AUC scores in Table  7 , RF and LR models equally identify true positive cases (stress). The LR model however has a higher specificity which indicates that the model is able to correctly identify more true negative cases (non stress).
Both models are considered to have an average performance. A higher AUC-ROC and specificity is desired but a balance between these two metrics needs to be achieved. One of the main challenges faced in the study is the inter-individual and measurement variability. Since HRV can vary greatly between individuals and can be affected by various factors such as measurement equipment, recording conditions, and physiological factors, it is difficult to develop a model that is applicable to a wide population. In this study, labels in the UWS dataset may not be as accurate, and the stress response may vary between the two datasets as the MMSD dataset uses artificial stressors whereas the UWS dataset uses stressors from daily life events. Additionally, the devices used to collect data in the two datasets are also different, which could affect the generalization of the models and contribute to the lower stress class recognition scores.
In the MMSD dataset, the inter-individual variations were taken into consideration in the experimental protocol by controlling the stressors, the environment and subject-related variations by establishing a subject profile  [18]. By training the models on laboratory data (MMSD dataset), it is possible to ensure that they are able to accurately identify the relevant features or patterns associated with stress. On the other hand, testing on ambulatory data (UWS dataset) allows for an evaluation of its performance in real-world scenarios, and can help identify any limitations or challenges that need to be addressed in order for the model to be effectively used in these settings. Overall, training on laboratory data and testing on ambulatory data is a way to evaluate the model's performance on different types of data and ensure that it is robust and generalizable enough to be used in real-world settings.
Once the binary stress detection model is developed and validated, it can be used as a pre-processing step to filter out instances that are not stress. A second model can then be trained on a dataset that includes labelled instances of different levels of stress according to a standardized scale such as the DASS21 used in the UWS dataset. The scores can be interpreted based on the cut off points provided by the creators of the scale, which are: Normal: 0-7, Mild stress: 8-9 Moderate stress: 10-12 and Severe stress: 13-16. Such models can be trained to recognize patterns in the HRV data that are specific to different levels of stress.
Conclusions As the Internet of Things (IoT) becomes more prevalent, there is growing interest in utilizing HRV as a means of remotely and continuously monitoring stress in a non-invasive manner. However, a major challenge in this field is the availability of data for stress recognition. This, combined with the ethical considerations surrounding the collection of biomedical data, highlights the need for models that are able to accurately predict stress across various datasets and populations. This study aims to evaluate the generalizability of two HRV-based models, logistic regression (LR) and random forest (RF), by using two stress datasets (MMSD and UWS) differing in many aspects including stressors' type, intensity and acquisition devices. The study first conducts an independent analysis on each dataset using a LOSO cross-validation, followed by a cross-dataset analysis.
The use of laboratory data from the MMSD dataset in the training set ensures that the models identify only the relevant features and patterns associated with stress. The models' performance is then evaluated on ambulatory data to ensure that they are robust and generalizable enough for real-world applications. Fairly good classification scores are obtained in the independent and crossdataset analysis with better generalisation capabilities demonstrated by the RF model (62% F1 score), although it performed slightly less well than the LR model on MMSD independent evaluation. These results align well with those obtained in a similar study found in literature using the same standards (LOSO and cross-dataset analysis)  [41]. Future work could expand upon this study by testing the models on additional datasets, as well as using other machine learning models.
Specifically, deep learning models, such as Convolutional Neural Networks (CNNs), with HRV time-series data could potentially lead to more accurate classification results. Additionally, it would be valuable to understand the specific factors that impact the generalizability of the models, such as the type of acquisition device used, the type and intensity of stressors and other features. This could be achieved by controlling for certain factors and comparing the models' generalizability across datasets. Studies on the generalizability of models using various physiological signals for ambulatory stress detection could have several potential benefits on Point-of-care testing (POCT), where stress can be monitored at patient's location. This can be particularly beneficial for patients who have mobility issues.
Multi-Modal Stress Dataset (MMSD) for Real-Time, Continuous Stress Detection MMSD [18] is a multimodal acute stress dataset from a laboratory study (IRB 202077, 31 August 2020). It includes physiological signals such as Electrocardiograms (ECG), Photoplethysmograms (PPG), Electrodermal Activity (EDA) and Electromyograms (EMG) from 74 subjects recorded in three different states successively: 15 min relaxation; 20 min stress and 10 min recovery. The sample includes 38 women aged 19 to 63 years old (mean age: 33 y. o ± 12. 5) and 36 men aged 21 to 79 years old (mean age: 35 y. o ± 13).
2. 1. 1. 11 Multi-Modal Stress Dataset (MMSD) for Real-Time, Continuous Stress Detection MMSD [18] is a multimodal acute stress dataset from a laboratory study (IRB 202077, 31 August 2020). It includes physiological signals such as Electrocardiograms (ECG), Photoplethysmograms (PPG), Electrodermal Activity (EDA) and Electromyograms (EMG) from 74 subjects recorded in three different states successively: 15 min relaxation; 20 min stress and 10 min recovery. The sample includes 38 women aged 19 to 63 years old (mean age: 33 y. o ± 12. 5) and 36 men aged 21 to 79 years old (mean age: 35 y. o ± 13).
For deep sequencing of the KCNJ13 locus, gDNA was amplified using Illumina-specific primers with adapter sequences (amplicon size, -150 bp) (Supplemental Table  4 ). Unique indexes (i7 and i5) were ligated to each custom amplicon by PCR (amplicon size, 250 bp), and the indexed amplicons were pooled and purified using AMPure XP beads (Beckman Coulter, A63881). The indexed library was run on an Illumina MiniSeq instrument with a read length of 150 bp. Deep-sequencing data were analyzed using RGEN Cas-analyzer  (51)  and CRISPResso2 (52) software. Off-target analysis by deep sequencing. The potential off-target sites for the hW53X-sgRNA were identified by an in silico tool, Cas-OFFinder  (36). The parameters used were an NG/NGG/NAG PAM with up to 4 mismatches to the sgRNA sequence (Supplemental Table  5  and Supplemental Figure  12 ).
We also considered a DNA and RNA bulge of 1 nucleotide, which occurs due to an extra unpaired base in the DNA sequence concerning sgRNA or an extra unpaired base in sgRNA for DNA sequence in the genome, respectively. From the treated and untreated stable cells, fibroblasts, and iPSC-RPE, gDNA was isolated and amplified using primers specific to off-target sites. All the primer sequences are listed in Supplemental Table  6. Deepsequencing and data analysis were performed as described above. rhAmp off-target analysis in W53X mice. As the gDNA yield from the mouse optic cup was too low to amplify all the off-target sites separately, we used a highly efficient RNase H2-dependent (rhAmp) PCR technique that can amplify different targets using a single PCR reaction. Amplification and sequencing were performed according to IDT rhAmp instructions.
10. 0. 0). ERG in mice. ERG was performed in mice using a standard protocol described elsewhere before and after the base editing to evaluate the function of the retina. Briefly, the mice were dark adapted overnight prior to ERG. ERG signals were captured in full darkness using an Espion Ganzfeld full-field system (Diagnosys LLC). When using a contact electrode, a drop of 2% hypromellose solution was applied to the eye in order to keep the cornea wet and make electrical contact. Mice were subjected to mfERG testing with the Celeris system (Diagnosys LLC), in which the retina is divided into 19 hexagonal areas and stimulated by a pseudorandom sequence of black and white hexagons that alternate multiple times per second. Data acquired were analyzed with Espion software (Diagnosys LLC; version V6.
graphs and values behind any reported means, are in the Supporting Data Values file. Additional data can be made available from the corresponding author upon request. Electrophysiology assay. A high-throughput automated patch clamp (Q Patch II, Sophion) was used to measure the whole-cell current from the HEK WT , HEK W53X , and base-edited HEK W53X stable cells as described earlier  (53). Briefly, the cells were grown in a T75 flask for 48 to 72 hours and then detached gently using Detachin. The cells were centrifuged at 90g for 1 minute and resuspended in serum-free medium containing 25 mM HEPES. The cells (3 M/ mL) were kept on a shaker for 20 minutes before the experiment. Forty-eight cells were recorded in parallel on single-hole disposable Qplates with individual amplifiers. A pressure protocol was used to achieve cell positioning (-70 mbar), Giga seal (-75 mbar), and whole-cell configuration (5 pulses with -50 mbar increment between the pulses, first pulse of -250 mbar).
1 current. An extracellular solution with 20 mM Cs + was used to block the Kir7. 1 current. The data were analyzed using Sophion Analyzer, version 6. 6. 44. Whole-cell manual patch-clamp recording of base-edited hiPS-RPE cells was performed according to the standard protocol described elsewhere  (7). There was no reporter or selection marker to aid in identifying the recipient or edited cells. Therefore, these cells were picked up randomly for the electrophysiology assay. Statistics. The data analysis was done using Origin software (Origin 2020, OriginLab Corp. ) and expressed as mean ± SEM. Twotailed, unpaired Student's t test was used to determine statistical differences. One-way ANOVA was used for multiple comparison, followed by Tukey's honestly significant difference (HSD) method for adjustment of multiple comparisons.
Acknowledgments We thank the  Stem Cell & Regenerative Medicine Center (University of Wisconsin-Madison)  for fellowship funding support to MK. We acknowledge the  Retina Research Foundations Kathryn  and  Latimer Murfee Chair  (to KS), the M. D. Matthews Research Professorship  (to BRP), the  Retina Research Foundation Emmett A. Humble Distinguished  Directorship (to DMG), and the  McPherson ERI Sandra Lemke Trout Chair  in Eye Research (to DMG). The research was also supported by  University of Wisconsin-Partnership Education and Research Committee  funds and the  Harrington Discovery Institute 's Gund-Harrington Scholar award  (to KS). We thank the  Helen Hay Whitney Foundation , the  Howard Hughes Medical Institute , and the  NIH  ( K99  award  HL163805 ) for support to GAN. This work was supported by  NIH  awards  R01 EY024995  and  R24 EY032434  (to BRP) and  U01 AI142756 ,  RM1 HG009490 , and  R35 GM118062  (to DRL);  Core Grant for Vision Research  from the  NIH  to the  University of Wisconsin-Madison  ( P30 EY016665 );  NIH  grants  S10OD026957  and  S10OD018221 ; and  Research to Prevent Blindness.
The results from 16 ESMs (Table  2 ) 1 that completed the LS4P-I's major experiments are presented in this paper. Each ESM conducted an approximately 2-month long simulation from late April through June 2003 with their normal setting for a S2S prediction of atmospheric and land initial conditions and ocean surface boundary conditions. Except for the CNRM-CM6-I-CMIP (hereafter referred to as CNRM CMIP) and the ECMWF-IFS, every ESM used daily reanalysis data to specify May and June 2003 SST and sea ice conditions. The CNRM-CMIP and the ECMWF-IFS specified ocean initial conditions at the beginning of the integration and ran the model in a fully coupled land-atmosphere-ocean configuration. The LS4P-I protocol required at least six ensemble members, but most groups provided more than 10 ensemble members (Table  2 ).
For the LS4P Phase II, the year 1998 has been selected because it features a very cold spring in the Western U. S. , severe summer drought in Texas and Oklahoma  (Hong and Kalnay 2002) , very warm spring over the TP, and severe summer flooding in the Yangtze River Basin  (Diallo et al. 2022). More information on the Phase II experiments, including its protocol, can be found on the LS4P website (http:// ls4p. geog. ucla. edu). Up until now, land-atmosphere interaction studies have largely focused on the local feedbacks between land and atmosphere. A lack of observational data has presented a crucial limitation. In the LS4P experiment, the T2m, which has the highest quality among measured land variables and the longest meteorological observational record with global coverage and dense measurements, has been applied as a reference to explore the possible remote effects of large-scale spring LST/SUBT anomaly over high mountains on summer precipitation anomalies.
Data availability The processed simulation datasets and materials used in this study are available upon request to (i) the corresponding author (yxue@geog. ucla. edu), or (ii) Ismaila Diallo (idiallo. work@gmail. com), or (iii) Hara Nayak (hpnayak@g. ucla. edu). Also, it is worth pointing out that all simulation datasets are available from http:// data. tpdc. ac. cn/ en/. See  Xue et al. (2021) , [https:// gmd. coper nicus. org/ artic les/ 14/ 4465/ 2021/] for data access and download instruction. The reference datasets are downloaded from open sources. The CMA observational datasets are available from http:// data. cma. cn/ en. CRU datasets are available from https:// cruda ta. uea. ac. uk/ cru/ data/ hrg/ cru_ ts_4. 05/. CAMS datasets are available from https:// psl. noaa. gov/ data/ gridd ed/ data.
A large number of non-viral proteins, which should include virulence proteins, could be identified by mass spectrometry within the VLPs (Table  S9 ). Proteins described in VcVLPs were first sought in the CcapiVLP protein dataset to determine whether the same proteins are enveloped in VLPs in both wasp species. The VLP1 (PHGPx), VLP2 (RhoGAP) and VLP3 (Neprilysin) proteins of VcVLPs were not found in CcapiVLPs. Candidate VLP virulence proteins were selected according to their peptide abundance (PSM for Peptide-Spectrum Matches) value and only proteins with a PSM value higher than 50 were retained (Table  S10 ). Some proteins that have been described in parasitoid wasp venoms  (Inwood et al. , 2023; S. Moreau & Asgari, 2015; Poirié et al. , 2014) , such as a serpin, a peroxiredoxin, a calreticulin and an aminopeptidase, have been found in CcapiVLPs.
Custom, dual-indexing adapter-primers were used to allow for in silico demultiplexing of each sample  (Glenn et al. , 2016). Following stub ligation and PCR, adapterdimers were removed by performing a 0. 8X bead cleaning using AMPure beads. The library was pooled with other samples at equimolar concentrations and sequenced at 4 nM as single lanes on Illumina NovaSeq 6000 S4 platform (2x150; Illumina Inc. , San Diego, CA). The library for long-read data was prepared using the Nanopore ligation kit, complemented by reagents from the NEBNext Ultra II library prep kit (New England Biolabs, Ipswich, Massachusetts, U. S. A. ), and then sequenced at a MinION platform (Oxford Nanopore Technologies, Oxford, U. K. ) using the manufacturer's specifications. For both wasp species, raw data are available in NCBI (accession number of the BioProject: PRJNA936130).
RNA quantity of each pool was quantified using the Qubit TM RNA HS assay kit (Invitrogen) designed for the Qubit2. 0 fluorometer (Invitrogen). Samples of extracted RNA were then stored at -80°C. For genome annotation, total RNA was extracted from two pools of four entire emerging female wasps using the NucleoSpin®RNA kit (Macherey-Nagel). Total RNA was finally resuspended in 40µL of RNAse free water. RNA quantity of the two pools was quantified using the Qubit TM RNA HS assay kit (Invitrogen) designed for the Qubit2. 0 fluorometer. Samples of extracted RNA were then stored at -80°C. The sequencing of library preparations and the generation of paired-end reads were performed on an Illumina platform by the Novogene company (UK). Raw data are available in NCBI (accession number of the BioProject: PRJNA936130).
Genome assembly and annotation For C. capitator, genome and K-mer sizes were estimated beforehand using KmerGenie  (Chikhi & Medvedev, 2014). Raw reads were then corrected and trimmed using respectively Canucorrect and Canu-trim modules (Canu software v1. 8)  (Koren et al. , 2017)  in order to improve the base accuracy and to retrieve high quality sequence portions only. The genome was then assembled using the Canu-assemble module. The newly assembled genome was polished using Racon (v1. 4. 21) with the longest 80X raw reads to further improve the base accuracy using the longest raw reads. In parallel, C. nolae raw reads from Nanopore sequencing were filtered using Filtlong (v. 0. 2. 1) before being assembled using Flye (v. 2. 9-b1768)  (Kolmogorov et al. , 2020). The produced assembly was then polished with Hypo (v.
1. 0. 3. flye) using the Illumina reads. Scripts for C. nolae genome assembly are available at https://gitlab. in2p3. fr/marie. cariou/campoplex_assembly/. The assembled genome completeness was assessed with BUSCO (Benchmarking Universal Single-Copy Orthologs, v5. 4. 2)  (Manni et al. , 2021)  using Insecta and Hymenoptera lineage datasets (odb10) and genome contamination was checked with Blobtools (v1. 1. 1)  (Laetsch & Blaxter, 2017)  for both genomes. For both genomes, repeated elements were annotated de novo using the TEdenovo pipeline comprised in the REPET pipeline (v2. 5)  (Flutre et al. , 2011). Two rounds of the TEannot pipeline (contained in the REPER pipeline) were then used to annotate repeated elements using existing databases. Repeated elements were then masked using Bedtools (v.
2. 29. 1)  (Quinlan & Hall, 2010 ) before gene prediction. Gene models were generated for C. capitator using BRAKER-2 (v2. 1. 6)  (Brůna et al. , 2021)  with assembled transcriptomes by spades (v3. 15. 5)  (Bushmanova et al. , 2019). Models were then functionally annotated using InterProScan (v5. 53-87. 0)  (Blum et al. , 2021)  and blastp (NCBIblast+ v2. 13. 0) with the nr database (downloaded the 14 th of April 2023), before being polished by AGAT (v1. 0. 0)  (Dainat et al. , 2023). Proteins were extracted from gene models using the agat_sp_extract_sequences. pl tool from AGAT software. Finally, the genome completeness was assessed again using the protein sequences with BUSCO. In total, 44 CcapiENV genes were annotated with the automated annotation process. Using the gene models predicted previously, additional viral genes were then annotated by reciprocal blasts: translated sequences from predicted genes were aligned using blast tools on the NCBI nr database filtered for sequences of endogenous and exogenous nudiviruses.