dataesr/scientific-paragraphs-categorization huggingface dataset:
The daily emissions of biomass burning used in MERRA-2 come from the Quick Fire Emission Dataset (QFED) version 2. 4-r6  (Darmenov and da Silva, 2015)  (after 2010). Based on the FRP, the QFED implement the cloud correction method from the Global Fire Assimilation System (GFAS,  Kaiser et al. (2012) ), and applied a sophisticated treatment of emissions from non-observed land areas  (Darmenov and da Silva, 2015). In this study, we used the mass mixing ratios from the MERRA-2 assimilated aerosol mixing ratio data product inst3_3d_aer_Nv (Global Modeling and Assimilation Office (GMAO), 2015a). We used linear interpolation of MERRA-2 aerosol profile data to spatially collocate the model data with CALIOP profiles. For temporal collocation, we used the nearest timestep of the MERRA-2 aerosol data to match with CALIOP overpass.
Acknowledgements. This work was supported by the  Academy of Finland  [grant number  339885 ], and the  Atmosphere and Climate Competence Center ACCC Flagship , funded by the  Academy of Finland  [grant number  337552 ]. EW and the  MPLNET  project are funded by the  NASA   Radiation Sciences Program  and  Earth Observing System. This research was supported by data and services obtained from the  PANhellenic Geophysical Observatory of Antikythera (PANGEA)  of the  National Observatory of Athens  (NOA). EM was supported by the  PANGEA4CalVal  project [Grant Agreement  101079201 ] funded by the  European Union. VA acknowledges the the  Hellenic Foundation for Research and Innovation  (Project  Acronym: StratoFIRE , Project Number: 3995). MS was supported by the  REALISTIC  project [Grant Agreement  101086690 ] funded by the  European Union.
ARG was supported by the  Agencia Estatal de Investigación  [grant number  PID2019-103886RB-I00 ], the  H2020 Environment  [grant numbers  871115  and  101008004 ] and the  H2020 Excellent Science  [grant number  778349 ] programs. We acknowledge  ACTRIS ,  University of Granada , and the  Finnish Meteorological Institute  for providing the data set which is available for download from https://cloudnet. fmi. fi/. We acknowledge PollyNET for the data collection, calibration, processing and dissemination. We thank  M_Map  for the open source code for the map plots used in this paper (Pawlowicz, R. , 2020. "M_Map: A map-.
Data availability. The CALIPSO data were obtained from the NASA Langley Research Center Atmospheric Science Data Center (https: //subset. larc. nasa. gov/calipso/, last access: 22 August 2023). MERRA-2 data are available at MDISC https://disc. gsfc. nasa. gov/datasets? project=MERRA-2 (last access: 22 August 2023), managed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The CAMS data are available at the Atmosphere Data Store at https://ads. atmosphere. copernicus. eu/ (last access: 22 August 2023). MPLNET data are available at https://mplnet. gsfc. nasa. gov/download_tool/ (last access: 22 August 2023). Visualization of lidar products of PollyNet are available at https://polly. tropos. de/ (last access: 22 August 2023), lidar data are available upon request.
Patients' characteristics, inhospital management, 12-month clinical outcomes, and adherence to evidence-based therapies were evaluated. The registry enrolled 3013 patients at 71 centers, including private and public centers, in Brazil, representing the 5 regions of the country. Interestingly, the clinical characteristics of the population differ slightly from those of previous registries in Latin America, as the population aged, and ischemic or hypertensive causes were the leading etiologies of HF (mean age, 65 years vs 60 years), and chagas disease represented only the 6th most likely cause of HF 2 (Table  1 ). Interestingly, compared to BREATHE-1, BREATHE-Extension suggested that diabetes and HF with preserved ejection fraction have become more prevalent in recent years. Together, these data show that the Brazilian population with HF is becoming more and more similar to the European and North American populations with HF.
These strength points are common with fluoride optical fibers, which offer extremely low Rayleigh scattering and low attenuation with the possibility to guide light in the transmission This work was supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, "Telecommunications of the Future" (PE00000001 -program "RESTART", CUP: D93C22000910001) -DREAMS -Antennas & Devices foR mixing, dEtection And Manipulation of mmWaves; MIUR "Agriculture Green & Digital -AGREED", PNR 2015/20, n. ARS01_00254; H2020-ICT-37-2020 "Photonic Accurate and Portable Sensor Systems Exploiting Photo-Acoustic and Photo-Thermal Based Spectroscopy for Real-Time Outdoor Air Pollution Monitoring -PASSEPARTOUT" n. 101016956; MIUR PRIN 2022, NRPP -DD n. 1181 del 27-07-2023 -InnoVative tEchnoloGies for non-invasivE window 𝜆 = 0.
Questionnaire survey The questionnaires were used to evaluate the thermal comfort vote of the occupants. Data obtained by the questionnaire were related to the age of the buildings, the number of occupants in a building, factors that improve or reduce thermal comfort, and the means and methods applied to regulate  that comfort: energy consumption, construction material(s), cooling and heating systems and sizes of openings. The thermal sensation, preferences and satisfaction scale of the study participants were evaluated using the ASHRAE 7-point heat sensation scale  [34]. Table  4  below presents the indices used to assess the thermal sensation and comfort of participants who were asked to rate their thermal sensation, preference and satisfaction on the ASHRAE sevenpoint scale. The data collected provide information on how the occupants felt in their indoor environment  [35].
Temperature and humidity data collection In this study, digital thermometers (see Fig. 6  and Table  5  ) were used to measure indoor temperature and humidity. Accuracy is acceptable within the studied measurement range, 0. 5 °C for temperature between 0 and 40 °C and 2% for humidity between 20 and 80%. Collected data from both traditional houses as well as contemporary houses were recorded in both cases, in the absence and in the existence of mechanical cooling, ventilation system and heating system. Data from both measurements were recorded over 37 days in different dwellings, traditional and contemporary, 30 days between January and February, and 7 days in August in the city of Byblos. Among the four seasons in the study area, winter and summer cause the major discomfort due to the presence of relatively high humidity at drop temperatures in winter and elevated temperatures in summer.
Hence, it is important to assess the indoor thermal comfort in these two seasons. The months of January, February and August were considered in this study. From January 1 to February 29, 2021 and from August 1 to 7, nine homes were equipped with data logger sensors (UNI-T USB) to measure temperature, humidity, and pressure in the living room of each apartment. The sensors were positioned at a height of 1-1. 5 m in a stable environment protected from direct sunlight and air movements. The objective was to evaluate the overall indoor hygrothermal environment quality of the home and compare it to outdoor comfort limits defined in bioclimatic analysis. Every three months, energy consumption (electricity) was monitored, and residents were interviewed about their practices. A five-week measurement campaign was also conducted in all three types of homes: traditional houses, contemporary houses, and contemporary apartments.
Thermal acceptability The internal temperature of the apartments ranged between 22 °C and 31 °C Over the summer study periods. a wide range of temperatures from 22 °C through to 25 °C was acceptable to over 80% of the population during summer ( Fig. 7  ) as shown by the selected studies taken from the ASHRAE database. The percentage of the population voting 'acceptable' at 26 °C dropped below 80% in summer seasons.
Material and methods The current literature was systematically reviewed up to September 10, 2023. To ensure a comprehensive and rigorous analysis, the research was conducted separately by two different researchers on multiple databases including PubMed, Cochrane, Web of Science, Scopus, and Embase. This method ensured to avoid mistakes and that no pertinent studies were overlooked during the initial screening process. The research was conducted by using a carefully selected set of keywords and their synonyms. The search string was the following one: "Microbiome" or "Microbiota" or "Microflora" or "Microbes" AND "Upper Small Intestine" or "Jejunum" or "Duodenum" AND "Obesity" or "Overweight" or "Metabolic Syndrome" or "Diabetes" or "High fat Diet" and all their synonyms. The search string was limited to "[Title/Abstract]" and publication date did not serve as a search criterion.
Still, differences in sample collection methods and variations in analytical methodologies (e. g. , database for taxonomic identification) across studies limit a current and extended vision on proper taxonomic classification of the USI microbiome. More studies are also needed to decipher the mechanistic aspects connecting gut microbiome composition, mucus, intestinal layer, host metabolism, and inflammatory status. Animal studies were principally conducted on the murine C57BL/6 strain undergoing HFD and USI microbiome exploration was mostly performed using 16S rRNA gene sequencing. However, none of the studies reviewed in this paper reported methods to prevent coprophagy in mice, and, therefore, the possibility that the fecal microflora affected that of the USI cannot be denied. When conducting research on USI using rodents, coprophagy should be avoided as it alters the microbiome of the USI and shifts its composition closer to that of the colon, such as altering the BA profile in the small intestine of mice  [75].
Nevertheless, the current lack of an experimental structure for the ATAC complex limits the rationalization of its Co-TA network. The fact that, when available, the highresolution structural observations in multiprotein complexes perfectly agree with the Co-TA data argues that new pair-wise interactions revealed by studying Co-TA represent a valuable source of information to set the basis for mapping interaction domains and guiding the structural analyses. ATAC biogenesis strikingly differs from those described for TFIID or SAGA in that neither ATAC free subunits nor building blocks could be detected in the cytoplasm (Figure  2D ). Despite the robust detection of co-translational interactions among ATAC subunits in this compartment, the fully synthetized components and the holocomplex were exclusively found in the nuclear fraction.
Moreover, DiSP is agnostic on the identity of the pairing between partners undergoing Co-Co assembly, since it simply detects transcripts enriched in the disome fraction. Thus, we know that a certain protein undergoes Co-Co assembly, but we ignore the identity of the partner. By applying a stringent set of criteria, the authors provided a list of high-and lower-confidence candidate proteins undergoing simultaneous Co-TA in human cells. The high-confidence list was depleted of monomeric proteins, while highly enriched with proteins annotated to form homodimers, pointing at Co-Co assembly in cis. Yet, the dataset was also enriched with proteins part of heteromeric assemblies. 46  Thus, this technique allowed to detect also co-translational heteromeric interactions established in trans, between nascent chains encoded by different transcripts.
Interestingly, Co-Co assembly candidates annotated as nuclear proteins (34%) are slightly, but significantly enriched in the DiSP dataset (Figure  3B ). Among them, the number of Co-Co candidates annotated as TFs is lower than what it would be expected by chance, suggesting that simultaneous Co-TA is not prevalent among TFs (Figure  3B ). Yet, the dataset contains 231 proteins annotated as TFs in The Human Transcription Factors database, 106 suggesting that 14% of human TFs homo-or heterodimerize in the cytoplasm using simultaneous Co-TA (Figure  3C ). Nuclear proteins without TFs (non-TF nuclear proteins) are found robustly enriched in the Co-Co assembly candidate list (Figure  3B ). Indeed, the dataset is rich in subunits belonging to large nuclear multiprotein complexes. Figure  3D  shows a non-comprehensive overview of the complexes involved in transcriptional regulation with subunits found to undergo simultaneous Co-TA by DiSP.
46  The dataset includes subunits from several functionally-distinct complexes, such as the Mediator complex, histone acetyltransferase (HAT) complexes (SAGA, ATAC, NuA4, MORF, MOZ, HBO1, MSL, NSL), histone methyltransferase (HMT) complexes (SET1/MLL, PRC2), histone deacetylase (HDAC) complexes (SIN3A, MIDAC, NuRD), nucleosome remodeling complexes (SWI/ SNF, NuA4, NoRC, NuRD), GTFs (TFIID, TFIIF, TFIIH), promoter pausing factors (Integrator, NELF), the TREX mRNA export complex, the cohesin complex, the spliceosome and Pol II itself (Figure  3D ). This transcriptome-wide analysis provides strong evidence for the widespread use of simultaneous Co-TA as a basis for the assembly of a plethora of nuclear multisubunit protein complexes in human cells.
Perspectives This review summarizes the current understanding of co-translational interactions, with a special focus on the assembly of complexes that regulate gene transcription. We outlined the expected advantages -and evidences -of Co-TA in guiding complex formation (1), we proposed a series of explanations for the allocation "dilemma" of paralog and moonlighting subunits in the light of co-translational interactions (2), we described as a paradigm the distinct strategies employed for the   46  The plot shows the resulting odds ratios and P-values for each category against the reference set. The reference set was built by the total number of expressed genes retrieved from the processed datasets in HEK293-T and U2OS cells (GEO ID: GSE151959). Nuclear protein annotation was retrieved from UniProt and TFs annotation was based on The Human Transcription Factor database.
106 (C) Venn diagram showing the fraction of DiSP hits annotated as TFs. (D) Non-comprehensive overview of the DiSP hits which are part of characterized multiprotein complexes involved in Pol II transcriptional regulation. All the depicted subunits are found either as high-(dark yellow) or lower-confidence (light yellow) hits in the published DiSP dataset. 46  The depicted identified subunits are represented in the corresponding protein complex box. Subunits shared with multiple complexes are shown with a dotted line and are positioned between the alternative complexes. The assignment of each protein to the corresponding human complex is based on the Complex Portal database. 107  Co-TA of three key transcription regulatory complexes (TFIID, SAGA and ATAC) (3) and we summarized the current evidence for the widespread use of Co-TA for the assembly of nuclear regulatory complexes (4).
Acknowledgements We thank the present and past members of the  Tora lab  and  HT. M. Timmers  for insightful discussions. This work was financially supported by  ERC-Advanced grant  ( ERC-2013-340551 ,  Birtoaction ), by  Agence Nationale de la Recherche (ANR)   ANR-PRCI-19-CE12-0029-01  ( EpiCAST );  ANR-20-CE12-0017-03 ,  ANR-22-CE11-0013-01_ACT ;  Fondation pour la Recherche  Medicale ( EQU-2021-03012631 );  NIH MIRA  ( R35GM139564 ); and  NSF  (Award Number: 1933344 ) grants. A. B. has been supported by the  Fondation ARC pour la recherche sur le cancer  ( ARCPOST-DOC2021080004113 ). This work, as part of the  ITI 2021-2028 program  of the  University of Strasbourg , was also supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program.
106 (C) Venn diagram showing the fraction of DiSP hits annotated as TFs. (D) Non-comprehensive overview of the DiSP hits which are part of characterized multiprotein complexes involved in Pol II transcriptional regulation. All the depicted subunits are found either as high-(dark yellow) or lower-confidence (light yellow) hits in the published DiSP dataset. 46  The depicted identified subunits are represented in the corresponding protein complex box. Subunits shared with multiple complexes are shown with a dotted line and are positioned between the alternative complexes. The assignment of each protein to the corresponding human complex is based on the Complex Portal database. 107.
However, the size and characteristics of the dataset makes our findings and recommendations relevant to urban areas around the world which experience resilience and sustainability issues  46  due to urban development and climate change  50. These concerns are also a critical issue in small to mid-sized cities  51  looking to save natural and agricultural land while creating equitable and affordable residential landscapes. Four main questions were investigated in a step-by-step approach to model and propose a range of sustainable morphologies that can achieve greening objectives. 1. Density-greening balance: Is FAR density really a key metric? We hypothesised that FAR may have little weight compared to morphology which affects building coverage and open space pressures. Indeed, OSR could be a more relevant metric reflecting a balanced and qualitative density i.
Plots (i. e. "land property unit" which is the aggregation of adjoining parcels belonging to the same owner) were extracted from the Fiscal Land Property Dataset distributed by the French Centre for Studies and Expertise on Risks, the Environment, Mobility and Urban Planning (CEREMA)  53. This MFR stock in the Lyon metropolitan area represented 20,906 plots and accounted for 98% of apartments (76% of whole housing stock). From these, only plots built over the past 100 years (1917-2017) were selected to conduct statistical analyses representative of current modern practices of urban planning and building. These 11,593 plots studied covered 3,400ha and 460,000 dwellings, accounting for 85% of the land surface, 83% of floor areas and 82% of the whole MFR housing stock.
Morphological Metrics Many indicators of urban morphology have emerged due to the recent availability and quality of largescale data sets, reinforcing the interest of researchers in buildings  57. The set of morphological metrics used in this paper is listed and illustrated in Supplementary Figure  3. Studying morphology required information on floor surfaces, provided also by the plot-scale dataset of the CEREMA  53. Density was calculated according to Floor Area Ratio (FAR)  33. Open Space Ratio (OSR) is a more qualitative index of density, used by urban planners and architects to control the balance between unbuilt open space and floor areas. OSR is assumed here to mediate frequentation (footfall) and potential conflicts of use (services), which in the case of green spaces is particularly informative to assess the durability of ecosystem services provision  58.
Two other indicators reflect urban morphology as mediators of density : Building Coverage (BC) and plot-average Building storeys (BS) because FAR = BC * BS. Densification and morphological dynamics were illustrated from the same plot-scale dataset of the CEREMA  53  which gives floor areas, their uses and the construction year, thus making it possible to calculate density and other morphological metrics and trace it back in time. Evolution of morphology and land cover in multi-family residential seen through the plots still existing in 2017 (Supplementary Figure  2 ) shows FAR, OSR and BC relatively coordinated upward and downward trends over time, while BL follows a distinct trend that overall increases until the 2000s, then drops and stabilises. The greening decreases drastically for constructions made within the last 10 years while the highest greening is achieved for 1980s constructions.
ACKNOWLEDGMENTS Thomas Boutreux  and  Fabien Commeaux  were supported by the  Lyon Urban School  ( Future investment programme   ANR-17-CONV-0004 ) of the  Université de Lyon , funded by the  French Research National Agency. This work was supported by the  Metropolitan Council of Lyon  and the  LabEx IMU  ( ANR-10-LABX-0088 ) of the  Université de Lyon , within the  «Plan France 2030 » operated by the  French National Research Agency (ANR) , as part of the  COLLECTIFS research  project (https://collectifsbiodiversite. universite-lyon. fr/).
7, 8  Expiratory flow limitation was identified when the stimulated FVL was partially or entirely superimposed on the natural FVL. Data are summarised as median [min-max]. Patients with and without expiratory flow limitation were compared using Mann-Whitney U test and Fischer's exact text. Statistical significance was set at p <0. 05. Thirteen patients were studied. Their characteristics are described in Table  1. ERV markedly decreased when supine (À35% [À22;À70]), the fall exceeding 20% in all patients. Supine changes in VC were modest and inconsistent (-8% [À3; À32], falls exceeding 15% in only four patients and 20% in only two). The same was true for IC (1% [À0. 21; +0. 19]). Supine expiratory flow limitation occurred in 8 cases (example in Fig. 1A  and 1B ; perfect concordance between negative expiratory pressure and manual abdominal compression).
Background: We aimed to describe the event rates and risk-factors for symptomatic venous thromboembolism (VTE) and major bleeding in a population of hospitalized acutely ill medical patients. Methods: Patients ≥40 years old and hospitalized for acute medical illness who initiated enoxaparin prophylaxis were selected from the US Optum research database. Rates of symptomatic VTE and major bleeding at 90-days were estimated via the Kaplan-Meier (KM) method. Risk factors were identified via the Cox proportional hazards model. Results: A total of 123,022 patients met the selection criteria. The KM rates of VTE and major bleeding at 90-days were 3. 5 % and 2. 2 %, respectively. Among subgroups, the risk of VTE varied from 3. 0 % in patients with ischemic stroke to 6. 9 % in patients with a cancer-related hospitalization, and the risk of major bleeding varied from 1.
The study population The study population was developed from the Optum Market Clarity Research Database representing individuals in the USA enrolled in commercial and Medicare Advantage health plans. The latter represented a health plan from a private entity that contracted with Medicare to provide eligible patients (mostly with age ≥ 65 years) with insurance benefits. The overall database represented approximately 71 million unique patients, with linked medical and pharmacy claims and electronic health records (EHR) data across the continuum of care from outpatient and inpatient settings. The database included integrated information from the following sources: inpatient and outpatient claims, health plan enrollment, electronic health records (EHR; inpatient and outpatient), prescriptions, and labs.
Data collection First, we selected patients with primary hospitalization for an acute medical illness representing the following conditions: infection, respiratory insufficiency, inflammatory condition, cancer, heart failure, and ischemic stroke. Next, we determined if enoxaparin was initiated during hospital admission, which resulted in the candidate pool for qualifying hospitalizations (Fig. 1 ). In case there were multiple qualifying hospitalizations for a given patient, one at random was selected to avoid potential analytical difficulties in handling multiple episodes per patient  [14]. The index date was defined as the initiation of enoxaparin prophylaxis. In addition to hospitalization for acute medical illness and initiation of enoxaparin prophylaxis, inclusion criteria represented age ≥ 40 years and at least one-year of continuous enrollment in a health plan with medical and prescription coverage prior to index.