dataesr/scientific-paragraphs-categorization huggingface dataset:
To do this, we use data from the Programme for the International Assessment of Adult Competencies (PIAAC), a programme for the assessment and analysis of adult competencies. PIAAC measures adults' proficiency in key information-processing skills (literacy, numeracy and problemsolving) and provides information on labour market outcomes, including detailed occupation information and the usage of numeracy skills at work, for a large representative sample of approximately 150,000 working-age adults in 31 (mostly developed) countries (see details in Supplementary Methods). Math-related jobs are identified using the 4-digit level International Standard Classification of Occupations (ISCO) available in PIAAC. The probability of working in such a job increases with math ability for working-age males 86% more than for their female counterparts (Supplementary Fig.
The PISA target population is made up of all students in any educational institution between the ages of 15 years and 3 months and 16 years and 2 months at the time of the assessment. This specific age has been chosen because it is close to the end of compulsory education in most countries. Efforts have been made to ensure the absence of cultural or national biases in the test and in the evaluation of performance. We analyse data from the PISA 2012 survey  41, 42. The student dataset in 2012 contains around 510,000 observations, which roughly represent a population of 28 million 15-year-olds attending seventh grade or above in 64 countries, including the 34 countries belonging to the OECD in 2012.
Data availability The main data sources used in this research come from the 2012 Programme for International Student Assessment (PISA 2012, https:// www. oecd. org/pisa/pisaproducts/pisa2012databasedownloadabledata. htm) and the Programme for the International Assessment of Adult Competencies (PIAAC, https://www. oecd. org/ skills/piaac/publicdataandanalysis/). Both are managed by the OECD, and the data are publicly available. HSLS:09 data are publicly available at https://nces. ed. gov/surveys/hsls09/. The robustness checks for France presented in Supplementary Table  9  are based on data used in ref. 24  and whose access is restricted. To access these data, a request must be sent to the statistical institute of the French Ministry of Education (the DEEP, see ref. 24  for details).
8 0] [11]  In addition, poor glycaemic control in patients with type 1 diabetes can contribute to the development of microvascular (retinopathy, nephropathy and neuropathy) and macrovascular (cardiovascular disease) complications. 12, 13 Nevertheless, most research to date on the association between OSA and diabetes has focused on type 2 diabetes. 8 5] [16] [17] [18] [19]  Estimated rates for the prevalence of OSA in type 1 diabetes range from 10. 3% to 61%,  [14] [15] [16] [17] [18]  despite the relative absence of common OSA risk factors such as obesity and older age  20  in this patient group. Recently, a population-based retrospective cohort study based on a UK primary care database found that individuals with versus without type 1 diabetes were at increased risk of developing sleep apnoea.
| Study design and oversight This prospective cohort study (NCT02796534) was conducted at 52 centres in France between July 2016 and June 2020. The study protocol was approved by the ethics committee at Grenoble University Hospital (14-CHUG-26; N ID RDB: 2014-A00974-43). All study procedures were performed in accordance with the principles of Good Clinical Practice and the Declaration of Helsinki. Potential participants were provided with written information about the study and signed a consent form agreeing to take part.
| Diagnosis of sleep apnoea Home care nurses assisted with the set-up of oximetry to determine the oxygen desaturation index (ODI) over a single night of recording. Data obtained were centrally analysed by study coordinators at Grenoble University Hospital (J. L. P. and R. T. ). No sleep apnoea was defined as an ODI of <15 events/h, mild-to-moderate sleep apnoea as an ODI of 15 to <30 events/h, and severe sleep apnoea as an ODI of ≥30 events/h.
| DISCUSSION As the largest prospective dataset investigating sleep apnoea in patients with type 1 diabetes, this study had a number of key findings. Sleep apnoea with an ODI of ≥15 events/h was present in nearly 16% of patients with well-controlled type 1 diabetes, and these patients had few symptoms of sleep-disordered breathing. As described previously for the general population,  20  male sex, higher BMI and older age were significant risk factors for sleep apnoea in study participants, along with presence of hypertension. Although no association between sleep apnoea severity and glycaemic control was documented in these patients with type 1 diabetes using an insulin pump, there was a clear relationship between presence/severity of sleep apnoea and both macrovascular complications and retinopathy.
Even in the absence of sleep apnoea, poor sleep has been identified as an independent risk factor for poor diabetes control,  32, 33  and lower HbA1c values have been reported in type 1 diabetes patients with good versus poor sleep quality (mean difference À0. 19%, 95% CI À0. 30, À0. 08). 15 Thus, there are a variety of mechanisms by which sleep apnoea could contribute to poor outcomes in patients with type 1 diabetes. This is by far the largest prospective study conducted to date looking at sleep apnoea in patients with type 1 diabetes. It was a multicentric dataset from a well-defined population, providing sufficient statistical power to determine associations between sleep apnoea and diabetes complications. Some limitations also need to be considered, however, when interpreting our findings.
A strength of this study was that the inclusion of patients with type 1 diabetes on insulin pump therapy resulted in a homogenous cohort of patients and allowed OSA screening to be facilitated by home care providers. However, complications were self-reported by patients to home care provider nurses, and this could have been subject to recall bias. Nevertheless, the presence of complications and comorbidities was validated by the diabetologists who were in charge F I G U R E 1 Association between sleep apnoea severity and micro-/ macrovascular complications. CI, confidence interval; N, number of events; OR, odds ratio; SA, sleep apnoea. *P < 0. 01 of the patients and had access to the electronic health record. The high proportion of missing data on the duration of diabetes is another limitation; this information was only available for 178/769 patients in the study (23%).
The duration of diabetes could influence the risk for, and the rate of, complications (based on the small amount of data available), so this is a confounding variable that we were not able to fully control for in the current analysis. Finally, missing data on therapeutic factors such as total daily insulin dosage significantly limited our ability to evaluate associations between sleep apnoea and metabolic phenotype. In conclusion, this study provides a unique description of sleep apnoea in patients with type 1 diabetes treated with an insulin pump, providing clinically relevant information on the prevalence, the phenotypes at risk, and the associations with complications. Longitudinal data were collected for this cohort and should offer important insights into incident cardiovascular events and mortality.
Role of the funding sourceThis study was an academic/industry partnership and the coauthors from ResMed/VitalAire are clinical/data scientist researchers who helped with the design of the study, data collection, interpretation and presentation of the findings, and the decision to submit the paper for publication. However, data analyses, clinical interpretation and validation of final scientific messages were the sole responsibility of the first and last academic authors. Data analysis was conducted under the supervision of Sebastien Bailly (second academic author) in the artificial intelligence chair "health trajectories" of Grenoble-Alpes University funded by National research agency [ANR-19-P3IA-0003], independently of the sponsors. In addition, the corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
2. 7 | 7 Role of the funding sourceThis study was an academic/industry partnership and the coauthors from ResMed/VitalAire are clinical/data scientist researchers who helped with the design of the study, data collection, interpretation and presentation of the findings, and the decision to submit the paper for publication. However, data analyses, clinical interpretation and validation of final scientific messages were the sole responsibility of the first and last academic authors. Data analysis was conducted under the supervision of Sebastien Bailly (second academic author) in the artificial intelligence chair "health trajectories" of Grenoble-Alpes University funded by National research agency [ANR-19-P3IA-0003], independently of the sponsors. In addition, the corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Acknowledgements DGA is supported by the  Department of Aquatic Sciences and Assessment (SLU). MB is supported by a  NHMRC Senior Principal Research Fellowship and Leadership 3 Investigator grant  ( 1156072  and  2017131 ). AI is partially supported by grants from  Atlantic Philanthropies  ( Atlantic Institute Ideate Granton art  and health);  ANID/FONDECYT Regular  ( 1210195  and  1210176  and  1220995 );  ANID/FON-DAP/15150012 ;  ANID/PIA/ANILLOS ACT210096 ;  ANID/FONDEF ID20I10152 ;  ANID/FONDAP 15150012 ;  Takeda   CW2680521 ,  Alzheimer's Drug Discovery Foundation and Target ALS , and the  MULTI-PARTNER CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN LATIN AMERICA [ReDLat , supported by  National Institutes of Health, National Institutes of Aging  ( R01 AG057234 ),  Alzheimer's Association  ( SG-20-725707 ),  Rainwater Charitable foundation-Tau Consortium , and  Global Brain Health Institute) ].
Data collection Investigators collected data from the patient's file or the physician who performed the sedation, including centre characteristics (palliative care service type and team composition), patient characteristics (compliance with eligibility criteria, sociodemographic data and main pathology) and sedation characteristics (location of induction, expected duration, patient consent, collegial procedure and drugs used). According to French guidelines, the collegial procedure is a dialogue between the physician in charge, the care team and at least one physician outside of the team (online supplemental file 2). In patients who were already sedated on the day of enrolment, sedation characteristics at the time of induction were collected retrospectively.
Statistical analysis The primary endpoint was the prevalence of sedation among all terminally ill patients in all participating centres. The number of cases was also determined for each type of palliative care provider (PCU, LISP, hospital-based mobile teams, home-based mobile teams/networks, HAD) and sedation duration (transitory, undetermined and maintained until death). The characteristics of the sedated patients were also obtained, together with the sedation indications and characteristics (consent type, collegial procedure and therapeutic modalities). Qualitative variables are described as numbers and percentages (with 95% CIs for primary endpoints), and quantitative variables as medians with first and third quartiles (Q1-Q3). SAS software (V. 9. 2; SAS Institute, Cary, North Carolina, USA) was used for the statistical analysis.
Drugs Almost all sedations used midazolam alone as the sedative (85. 9%), particularly in cases of intended undetermined duration. Midazolam in combination with neuroleptics was used in one case of sedation maintained until death, while four transitory sedations were induced using ketamine. Opioids were used in 24. 4% of cases, twice as often for sedation maintained until death as for those with an undetermined duration. In around two-thirds of cases (66. 0%), sedation was induced intravenously. This proportion increased to 74. 5% among cases where sedation was intended to be maintained until death. Sedatives were administered via the subcutaneous route in almost 20% of cases, mainly of transitory sedation. Data collected did not identify oral drugs. Continuous flow and an additional bolus were typically used for induction, particularly in cases where sedation was maintained until death.
C. Stimuli We aimed at testing the general usefulness of sensory reflectance matrices, rather than testing effects of specific hyperspectral images or illumination colors. For this reason, we measured a large variety of combinations instead of systematically combining a small number of images with a small number of illumination colors (see column 1 of Table  S1  for overview). There were seven different images from four databases  [20] [21] [22] [23] [24]  in the first and second measurements (both measurements had one image in common). The four images in the first measurement were rendered with four main illumination colors, and the four images in the second measurement were rendered with four intermediate illumination colors [cf. Fig. 1(a) ; see Table  S2  for numerical details]. The third measurement featured four images of fruits (apple, banana, dragon fruit, orange) rendered with the four main illumination colors  [25].
RESULTS For both narrowband and broadband illuminants, we had to discard the data for two stimuli in the third measurement of the online experiment due to an artifact that could have affected participants' choices. As a result, in the third measurement of the online experiment, we had 30 instead of 32 stimuli. We assumed that more than 50 ms are required for perceptual decisions and motor responses (e. g. ,  [30] ), and 2 min was enough time to thoroughly inspect the image. Hence, we discarded trials where participants responded below 50 ms (0. 11%, 0. 11%, and 0. 06% of responses in all the three measurements of the online experiment) or above 2 min (0. 34%, 0. 25%, and 0. 17%) to avoid spurious responses. For the resulting data, we calculated the proportion correct (accuracies) for identifying the target rendered based on sensory reflectance matrices among the three spectrally rendered comparisons.
Acknowledgment. We thank  Irem Ozdemir ,  Neslihan Ozhan ,  Veronica Pisu , and  Michaela Trescakova  for help with piloting, and  Alban Flachot  and  Giulio Palma  for comments on the manuscript. Disclosures. The authors declare no conflicts of interest. Data availability. Data underlying the results presented in this paper are available in Ref. [26]. Supplemental document. See Supplement 1 for supporting content.
Study design, setting, and participants This was a cross-sectional study that involved pharmacists from all over Lebanon. Data were collected via an anonymous online questionnaire using Google Forms, over a period of 2 months during the COVID-19 outbreak from May 8 to July 5, 2020. A snowball sampling technique was used to collect data across the eight governorates (Mohafazat) of Lebanon and to target only Lebanese pharmacists who work in community, hospital, drug company, academic, and other settings.
Measurement tools The questionnaire consisted of three sections. The first and second sections were concerned with the participants' sociodemographic data and the pharmacists' knowledge and concerns about COVID-19. In the following section, four validated scales that serve the purpose of the study in measuring the mental health status of the participants during the COVID-19 pandemic have been included. The 7-item Generalized Anxiety Disorder (GAD-7), 7-item Insomnia Severity Index (ISI), Patient Health Questionnaire 9-item depression module (PHQ-9), and Impact of Event Scale revised (IES-R) have been used to assess the psychological impact. The questionnaires were administered in English language because it is understood by the vast majority of Lebanese pharmacists who speak English. In fact, the OPL has adopted English as the language for their continuing education.
Thereafter, four binary logistic regression models using the forward method were performed to preclude potential confounding. The dependent variables were the anxiety, insomnia, depression, and IES-R scores in the first, second, third, and fourth models, respectively. The participants' characteristics having a p-value of less than 0. 05 in the bivariate analysis were included as independent variables to account for them as potential confounding. The models were tested for adequacy in all the analysis. An alpha of 0. 05 was used to determine statistical significance. All analyses were performed using the IBM's Statistical Package for the Social Sciences (SPSS) version 22. 0 (IBM, Inc. , Chicago, IL).
Demographic data of Lebanese pharmacists A total of 311 Lebanese pharmacists participated in the study whose sociodemographic characteristics are shown in Table  1. The analyzed sample included participants from all Lebanese districts, of whom 251 (80. 7%) were females and 181 (58. 2%) aged between 26 and 35 years. As for the professional field, 57. 2% work in community pharmacy, 6. 1% work as hospital/clinical pharmacists, 14. 8% in drug companies, 6. 8% work in academia, 1% work in regulatory departments of pharmaceutical companies, and 14. 1% in other pharmaceutical fields. Educational variables showed that 56. 3% had a BPHARM degree and 43. 7% had a BPHARM and postgraduate degrees (MS/PharmD/PhD). The majority (n = 112, 36%) belonged to the middle socioeconomic class with an average family income of more than 4,000,000 LBP per month.
Ours is the first study to investigate the hierarchical organization of neural oscillations, and their response to speech at birth. We analyzed data from 40 full-term, healthy newborns (mean age: 2. 55 days; range: 1-5 days; 17 females), born to French monolingual mothers, within their first 5 days of life. Their experience with speech was, therefore, mostly prenatal. We tested newborns at rest (preand post-stimulation) and while listening to repetitions of naturally spoken sentences ($2 s in duration) in three languages: their native language, i. e. , the language heard prenatally, French, a rhythmically similar unfamiliar language, Spanish, and a rhythmically different unfamiliar language, English (Figure  1C ) (see Audios S1-S9). While these languages also differ in other phonological properties such as phoneme repertoire or syllable structure, newborns have not been shown to rely on features other than rhythmic class for language discrimination.
Time-frequency responses We filtered the continuous EEG data, segmented it into epochs time-locked to the beginning of each sentence, and rejected epochs containing artifacts (see STAR methods). We then subjected each non-rejected epoch to time-frequency analysis to uncover oscillatory responses using the MATLAB toolbox 'WTools'. 26  This toolbox performs a continuous wavelet transform of each epoch using Morlet wavelets (number of cycles was 3. 5). The time-frequency responses were averaged across non-rejected epochs for each condition and channel separately. Figure  1D  displays the group mean time-frequency response for each condition at channel F8 as an example (Figure  S1  displays the response at other channels). We submitted the time-frequency responses to various statistical analyses to compare newborns' oscillatory activation (i) across language conditions, (ii) between pre-stimulus rest and speech and (iii) between pre-and post-stimulus rest.
Language differences We were first interested in determining whether neural oscillations are modulated by language familiarity and/or rhythm. To assess possible differences in activation across languages, we submitted the time-frequency responses to the three language conditions to permutation testing involving repeated measures ANOVAs with the within-subject factor Language (French/Spanish/English). (Figure  S2A  presents P-maps for this comparison). Chance distribution was established and multiple comparisons were corrected for by permuting the language labels in the original dataset (see STAR methods and Figure  S2C , which illustrates the chance distributions for the significant clusters). These analyses yielded activation differences across languages in the delta and theta bands, discussed later in discussion.
Nesting To investigate whether neural oscillations are already hierarchically organized at birth, which is necessary for processing linguistic units at different levels simultaneously, we evaluated the coupling between the phase of low-frequency oscillations, and the amplitude of high-frequency oscillations in our newborn data. To do so, we subjected each non-rejected epoch to a continuous wavelet transform using the MATLAB toolbox 'WTools'  26  to obtain the phase and amplitude of the oscillations for each trial (see STAR methods). We then computed the modulation index  49  : we first obtained the modulation index as a function of analytic phase (1-30 Hz) and analytic amplitude (1-50 Hz) for each non-rejected epoch, channel and condition separately, and then we averaged it across non-rejected epochs.
The increase in oscillatory activity observed in the delta and theta bands is in line with our prediction that low frequency neural oscillations are active during speech processing, reflecting the processing of sentence prosody. Indeed, in a previous study using the same dataset, we have found that newborns' neural activity in the theta band (more specifically 3-6 Hz) tracks the speech envelope of sentences in the three languages,  30  as is also the case in older infants. 27, 31  One interesting possibility is that this may be related to the low-pass filtered nature of the prenatal signal. Our results do not imply that newborns cannot process phonemes. Indeed, young infants' ability to discriminate phonemic contrasts has been well documented. 50, 51  There is converging evidence, however, that suprasegmental units, such as the syllable or larger prosodic units, may play a particularly important role in infants' early processing and representation of speech.
e. , prenatal experience. This cannot be established conclusively, as the results may also reflect responses triggered by the specific acoustic properties of French and Spanish as opposed to English independently of their familiarity. Future research testing the same stimuli with prenatally English-exposed newborns will provide a definitive answer. Importantly, these differences in oscillatory activity across languages do not relate to the neonate brain's ability to track the speech envelope. In a previous study  30  using the same dataset, we found that newborns' brain responses track the envelope of the speech signal both in phase (in the 3-6 Hz range) and in amplitude (in the 1-40 Hz range) regardless of language familiarity. Our previous and current findings taken together suggest that the ability to discriminate rhythmically different languages at birth requires the simultaneous processing of time and frequency information along multiple scales.
1 kHz. There were no significant differences between the sentences in the three languages in terms of minimum and maximum pitch, pitch range and average pitch (see Table  S1 ). The audio files (. wav) of the 9 utterances used as stimuli are included as part of the supplementary information (Audios S1-S9). Figure  S10  displays the sentences' time-series, and Figure  S11  displays their frequency spectra, obtained using the Fast Fourier Transform function in MATLAB (fft). We computed the amplitude and frequency modulation spectra of the sentences in the three languages as defined by Varnet and colleagues  64  to explore if utterances were consistently different across languages (Figure  S12 ). We found that utterances were similar in every spectral decomposition. The intensity of all recordings was adjusted to 77 dB.