dataesr/scientific-paragraphs-categorization huggingface dataset:
Complementary information about baseline RPPG algorithms can be found in  [10]  and  [11]. RPPG estimation from facial video sequences using spatiotemporal deep neural networks has been newly proposed as in  [12] ,  [13]  and  [14]. From an RPPG signal, PRV can be computed following the same definition as for the BVP. A few publicly shared datasets are adapted to psychophysiological studies, and, to the best of our knowledge, no publicly available datasets analyze social stress using remote physiological measures. In fact, there exist datasets that are intended for emotion analysis, such as CASME  [15]  and CASME 2  [16] , but they target facial expression detection and recognition. Zhang et al. [17]  introduce a multimodal dataset that involved 140 participants who were exposed to emotion eliciting videos.
They use various imaging techniques, extract facial expression features and collect several physiological data, such as EDA and heart rate, in order to study the human emotional behavior. Other examples of datasets allow to obtain RPPG signals. This is the case, for instance, of the UBFC-RPPG dataset  [18]  which proposes 42 videos along with corresponding contact pulse signals measured from participants who played a time-sensitive mathematical game. VIPL-HR dataset  [19]  was created to train a deep network to estimate heart rate, based on a total number of 3130 visible light and near-infrared videos. Stricker et al. present the PURE dataset  [20]  that is constituted of 60 image sequences of 1 min and contact pulse signals, collected from 10 subjects who were asked to perform head motions while they were filmed.
In  [21] , 204 uncompressed videos and ECG signals are gathered from 17 participants to test video-based HR estimation robustness to illumination and motion variations. However, these datasets are adapted for remote PPG computation but do not cover emotion or stress analysis. Some researchers that were interested in stress analysis through physiological responses had to create their own datasets, such as Kurniawan et al. who detect stress using speech and EDA, gathered from 10 participants  [22]. The only studies that have used remotely acquired physiological signals to measure or recognize stress states use private and quite small databases. For example, Bousefsaf et al. worked on mental stress detection among 12 subjects using remote heart rate and EDA  [23]. In  [24] , McDuff et al. detect cognitive stress based on contact and non-contact physiological measurement (the heart rate, the breathing rate and the HRV), from 10 participants.
This article introduces a dataset, named UBFC-Phys, that was collected to analyze the impact of social stress on physiological responses. Participants experienced stressful situations while they were filmed and were wearing a contact bracelet, in order to measure contact and non-contact physiological data. A Form based on the Competitive State Anxiety Inventory (CSAI)  [25] , allowed to quantify for every participant three dimensions of self-reported anxiety, namely cognitive anxiety, somatic anxiety and self-confidence. The experience followed a rigorous protocol inspired from the well-known Trier Social Stress Test (TSST)  [26] , and was conducted through three steps: a rest, a speech and an arithmetic tasks. The two latest tasks were organized following two levels of difficulty, as explained in section II.
Physiological signals that were measured for the study are RPPG, BVP, remote and contact PRV and EDA. Facial expressions were also extracted from collected videos. This multimodal, large, and publicly shared dataset proposes a total number of 168 videos, contact BVP and EDA signals, as well as self-reported state anxiety scores, and can serve for studies related to affective computing and psychophysiology. In this study, we show that physiological responses can be assessed during stress using the non-contact RPPG technique. Actually, non-contact PRV measurement can substitute contact reference measurement (correlations up to 99. 83%) in similar experience conditions. Compared to existing datasets, this study proposes a further non-contact PRV analysis, through statistical and classification results.
PRV extracted features were compared to EDA features and facial expressions. We obtained interesting results that show that non-contact PRV features can separate statistically the rest state from the stress state, which is supported by an 85. 48% classification accuracy. In addition to this result, contact PRV and EDA narrowly surpassed remote PRV in recognizing the experience three steps (respective accuracies of 65. 71% and 63. 09%). Remote PRV performed best when it came to classifying the two levels of difficulty (with an accuracy of 69. 73%). In all the classification tests, remote PRV gave better results than contact PRV. The rest of the article is organized as follows: data collection, experiment protocol and dataset organization are explained in section II. Physiological signal processing as well as PRV, EDA and facial feature extraction are detailed in section III.
A. Data collection The experience took place in a laboratory room. 68 undergraduate psychology students participated in the experience. Participants were filmed during the experience with an EO-23121C RGB digital camera by Edmund Optics, with a Motion JPEG compression and a 35 frame per second rate. The frame resolution was of 1024 × 1024 pixels. An artificial light source was used to ensure uniform lighting conditions for all the participants. Participants were seated around 1m away from the camera and the light source. In front of each participant an experimenter was seated. The experimenter used Contact measurements were realized using the Empatica E4 wristband  1  , which records BVP, skin temperature and EDA responses. The E4 bracelet has also an accelerometer and computes the Inter-Beat Intervals (IBI) from the BVP signal, which constitute the PRV.
E4 wristband performances have been validated in several studies  [27]   [28] , and relate to various research areas such as sleep monitoring  [29] , driving safety [30], and emotion arousal assessment  [31]. Accelerometer data and the IBI given by the E4 wristband were not used in this study. In fact, the IBI provided by the E4 bracelet are strongly filtered using a proprietary algorithm, which limits the reproducibility of this research. In this study, PRV extraction and filtering were realized based on standard algorithms as explained in section III-B, making it possible to compare PRV obtained from contact and remote pulse signals. Self-reported data were collected using forms that were given to the participants at the beginning and at the end of the experience. These forms were built following the CSAI, and aimed at mirroring their state anxiety through three dimensions: cognitive anxiety, somatic anxiety and selfconfidence.
This was an important step in order to facilitate comparisons between participants' responses. The rest task lasted 10 minutes. The speech and arithmetic tasks were interactive and had two possible scenarios, conceived so that two stress levels can be treated: a hard scenario (that we called test) and an easier one (named ctrl in our dataset). Participants were randomly assigned to one of the two versions. In the test scenario, the speech task was a simulation of a job interview. Participants had to imagine their dream job and convince the experimenter to hire them. In the ctrl scenario, the subjects had to either recall a positive holiday memory or imagine a dream vacation, and persuade the experimenter to wish to have the same holidays. In both scenarios, participants had the possibility to prepare a draft for their speech before the task started.
C. Dataset organization The final UBFC-Phys dataset proposes data collected from 56 healthy subjects (12 participants were eliminated due to technical problems or data sharing refusal). Participants are all aged between 19 and 38 (mean age is 21. 8 and standard deviation is 3. 11). Among these participants 46 are female and 10 male. Data is organized into 56 folders, corresponding to every participant. Thus, for each subject, three videos are available, one video per task. For concerns of equalizing the duration of the three tasks and alleviating the dataset size, only three minutes were kept. For data related to the rest task, the 3 minutes started from the middle, while they started at the beginning for the speech and the arithmetic tasks. Alongside the videos, contact blood volume pulse and electrodermal activity signals obtained from the E4 wristband are given.
There are three BVP and three EDA signal. csv files for each participant, respective to the three experience tasks. A. txt file contains information relative to the experiment, such as the subject associated number (from 1 to 56), his sex, the date and time the video recording began. In the same file the experience scenario (test or ctrl) is indicated. Pre and postsession self-reported state anxiety scores are given in a. csv file. Figure  3  summarizes the conduct of the experience sessions. It shows selected timeframes (indicated by a red broken line) that define available data extraction. Pink windows refer to introduction, transition and ending times (respectively denoted as 1, 2 and 3 in Figure  3 ). During introduction time, which lasted approximately 5min, the experimenter first explained the objective of the experience and the equipment used to collect data, then gave consent and pre-session forms, before presenting the rest task T 1.
B. PRV After resampling RPPG (initially sampled at the frame per rate, i. e. 35) and BVP (initially sampled at 64 Hz) to 128 Hz, remote PRV and contact PRV were extracted. From both contact and remote pulse signals, peaks were detected. Then, Pulse-to-Pulse (PP) intervals, which are time differences between successive peaks expressed in seconds (s), were computed to constitute PRV signals. Two steps have been followed in PRV signal processing: signal filtering (explained in III-B1) and feature extraction (detailed in III-B2). 1) PRV filtering: We were inspired by the Kubios HRV software thresholdbased artefact correction algorithm  [37]  for PRV signal filtering. PRV signals were median filtered using a 51 sample wide median filter (let us denote this width as L, thus L = 51), yielding a new signal medP P.
C. EDA Skin electrodermal activity signal reflects the influence of the SNS during stress. It is characterized by two components: tonic Skin Conductance Level (SCL), and phasic Skin Conductance Response (SCR). The first type is the smooth baseline level, and the latter represents rapid reactions to an external stimulus. EDA is measured in microSiemens (µS) and sampled at 4 Hz. Multiple EDA signals corresponding to the rest task presented no phasic SCR responses, which led us to only calculate tonic SCL. Tonic skin conductance level was computed using Continuous Decomposition Analysis, introduced in  [40] , using the Ledalab Matlab toolbox. Therefore, EDA signal mean eda and standard deviation stdEda values were computed, as well as SCL mean scl and standard deviation stdScl. Besides, we extracted the SCL minimum and maximum values (respectively minScl and maxScl), and subtracted the 10 first sample mean value from the 10 last sample mean value (we denote this difference as diffScl) to characterize the SCL variation during the experience tasks.
V. CONCLUSION AND FUTURE WORKS This article presents a multimodal dataset involved with social stress effect on contact and remote physiological re-    sponses. Social stress was induced following a rigorous and well-prepared protocol, based on the commonly-used in psychophysiology TSST test. Electrodermal activity and blood volume pulse signals, as well as videos and self-reported anxiety scores of 56 participants are proposed. In this study, we validated the experimentation protocol through several tests and particularly proved that remote pulse rate variability could substitute contact PRV in similar experience conditions. This was supported by satisfying extracted HR comparison results. Moreover, stress state, task and level classifications showed remote PRV performed better than contact PRV.
Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c.
Diabetes also has a long subclinical period defined by hyperglycemia and can remain undiagnosed without screening or other mechanisms for early identification  18. Some studies have assessed sensitivity and specificity of diabetes diagnosis using either FPG or HbA1c relative to the OGTT or have compared diabetes prevalence based on these different glycemic biomarkers, but most did not provide a direct comparison of HbA1c and FPG  [19] [20] [21]. Most population-based studies on the concordance and discordance Article https://doi. org/10. 1038/s41591-023-02610-2 seven of eight world regions (Extended Data Table  1 ). We had no study that measured both FPG and HbA1c from the region of Oceania, which consists of Pacific island nations. The number of studies in other regions ranged from seven in sub-Saharan Africa to 48 in the high-income western region (Table  1 ).
For example, in the latest global analysis  44  , only ~15% of surveys had measured both FPG and HbA1c. We assembled a global database of population-based studies that had measured both FPG and HbA1c. Using these data, we quantified the regional variation in the extent of diabetes diagnosis, with diabetes defined as in the Methods. We also quantified, among those who were previously undiagnosed and were detected as having diabetes through screening in the survey, the concordance and discordance of having FPG and HbA1c above common diagnostic thresholds (7. 0 mmol l -1 for FPG and 6. 5% for HbA1c). We refer to this group as screen-detected diabetes, which is an epidemiological definition, because many clinical guidelines recommend two measurements for diabetes diagnosis  [10] [11] [12] [13].
We then used regression analysis to examine what individual and study-level factors were associated with whether participants with screen-detected diabetes were identified by elevated FPG, elevated HbA1c or elevated levels of both. It has been shown that having elevated levels of both biomarkers has high positive predictive value for subsequent clinical diagnosis and risk of complications  14, 47  , and hence this group is similar to clinically diagnosed diabetes. Finally, we leveraged the global coverage of the dataset and its large sample size to develop prediction equations that estimate, for any given FPG level, the probability that a person without previously diagnosed diabetes would have HbA1c above the clinical threshold for diabetes had it been measured, and vice versa. We aimed to develop and validate global and generalizable prediction equations that account for both personal characteristics and regional differences.
Data sources We used data collated by the NCD Risk Factor Collaboration (NCD-RisC), a global consortium of population-based health examination surveys and studies with measurement of both FPG and HbA1c, and with data on previous diagnosis of diabetes, as described in the Methods. The criteria for including and excluding studies are stated in Methods. Within each study, we excluded participants who had missing data or were pregnant, under 18 years of age or from follow-up rounds of studies that had multiple measurements of the same cohort over time (Fig. 1 ). After exclusions, we used data on 601,307 participants aged 18 years and older with information on whether they had been previously diagnosed with diabetes, of whom 364,825 participants also had measured FPG and HbA1c. The difference between the number of participants with data on previous diagnosis and with biomarker data is mostly because many studies do blood tests on a subsample of those with questionnaire data.
Prediction equations We developed nine prediction equations (Extended Data Table  2 ) that estimate, for any given FPG level, the probability that a person without previously diagnosed diabetes would have HbA1c above the clinical threshold for diabetes had it been measured, and vice versa. The variables in the prediction equations included FPG as well as sex, age, BMI, whether FPG was measured in a laboratory or using a portable device, and region. We assessed the performance of the models in predicting (1) individual participants' status of having HbA1c ≥ 6. 5% based on their FPG and (2) the prevalence of HbA1c ≥ 6. 5% for an entire study. We used the same method for predicting the probability of having FPG ≥ 7. 0 mmol l -1 based on HbA1c. The performance at the individual level reflects how well the prediction equation works for triaging patients for further measurement for diabetes, and the performance at study (or population) level assesses how well the prediction equation works for diabetes surveillance.
Data We used data collated by the NCD-RisC. The data sources included national and multi-country measurement surveys that were either publicly available or identified and accessed through contacts with relevant government or academic partners. Additionally, we searched and reviewed published studies as detailed previously  44  and invited eligible studies to join NCD-RisC, as we did with participating studies in previous pooled analyses of cardiometabolic risk factors  [96] [97] [98] [99]. The NCD-RisC database is continuously updated through the above routes and through periodic requests to NCD-RisC members to suggest additional sources in their countries. The inclusion criteria for this analysis were (1) data were collected using a probabilistic sampling method with a defined sampling frame; (2) data were from population samples at the national, subnational (defined as covering one or more subnational regions, more than three urban communities or more than five rural communities) or community level (defined as having up to three urban communities or up to five rural communities); and (3) both FPG and HbA1c were measured.
At least two independent people ascertained that each data source met the inclusion criteria. All NCD-RisC members were asked to review the list of data sources from their country, to verify that the included data met the inclusion criteria and were not duplicates. When FPG and/or HbA1c data were missing for more than 10% of participants in a survey, we checked the study design documentation to verify missingness at random so that the above inclusion criteria were met. Questions and clarifications were discussed with NCD-RisC members and resolved before data were incorporated in the database. For each data source, we recorded the study population, sampling approach, years of measurement and measurement methods, including whether FPG and HbA1c were measured in a laboratory or using a portable point-of-care device.
5% and FPG < 7. 0 mmol l -1 , referred to as isolated elevated HbA1c) or elevated levels of both FPG and HbA1c. We report crude and age-standardized prevalence. We calculated crude prevalence using data from all participants regardless of age. We calculated age-standardized prevalence as the weighted mean of the age-specific values using the World Health Organization standard population 103. We also graphically described the relationship of FPG and HbA1c among people without diagnosed diabetes. Association with individual and study characteristics. We fitted regression models to examine what individual and study-level factors were associated with whether participants with screen-detected diabetes were identified by elevated FPG, elevated HbA1c or elevated levels of both. We fitted three separate log-binomial regressions, with each of the three outcomes (isolated elevated FPG, isolated elevated HbA1c and elevated levels of both) as a distinct dependent variable.
Acknowledgements This study was funded by the  UK Medical Research Council  (grant number  MR/V034057/1  to M. E. ), the  UK Research and Innovation (Research England Policy Support Fund  to M. E. ) and the  US Centers for Disease Control and Prevention  (to E. W. G. ). B. Zhou  is supported by a fellowship from the  Abdul Latif Jameel Institute for Disease and Emergency Analytics , funded by a donation from  Community Jameel , at  Imperial College London. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. For the purpose of open access, the author has applied a Creative Commons Attribution license to the Author Accepted Manuscript version arising from this submission.
Statistics The SigmaPlot software for Windows, version 14. 0, was used for the statistical analyses. For ordinal and continuous parameters, a two independent groups t-test was used. For responder analyses (binary outcomes), a Chi-squared or Fisher's exact test was used for group comparisons. This sample size was chosen after taking into consideration previous historical data on the anti-pruritic effect of emollient treatment and allowing for a 10% drop-out rate. Sixty subjects (two groups of 30) were randomized.
Distinct roles of Chinmo in larval and progenitor cells A critical feature of the MGN factors is that their sequential expression is achieved through a series of regulatory interactions between them. Therefore, we next sought to characterise the regulatory interactions of Chinmo with the pupal specifier Br-C and the adult specifier E93. To this end, we measured the expression of Br-C and E93 in chinmo-depleted SGs and wing discs. Contrary to recently published data  (Truman and Riddiford, 2022) , both tissues showed a significant and premature increase of Br-C protein levels as early as in L1 larvae, while no increase in E93 protein levels was detected in any tissue (Figure  4 ). In view of these results, we speculated whether the impairment of larval development observed in chinmo-depleted animals could be the result of precocious presence of the wrong stage-identity factor, in this case, Br-C.
Role of chinmo in hemimetabolous development As full metamorphosis is an evolutionary acquisition of holometabolous insects from hemimetabolous ancestors with no pupal stage  (Truman, 2019) , we sought to determine whether the role of chinmo as a larval specifier was also present in hemimetabolous insects. To this end, we used the German cockroach Blattella germanica as a model for hemimetabolous development. Blatella goes through six juvenile nymphal instars (N1-N6) before developing into an adult. Metamorphosis takes place during N6 and is restricted to the transformation of the wing primordia into functional wings, the attainment of functional genitalia, and changes in cuticle pigmentation  (Ureña et al. , 2014). A detailed Tblastn search in the Blatella genome database revealed the presence of a chinmo orthologue (Bg-chinmo).