dataesr/scientific-paragraphs-categorization huggingface dataset:
The total tissue lysate was then collected and purified using 20 mg/mL proteinase K. Second-strand DNA was synthesized by the nick translation method. For in vitro transcription reaction, synthesized cDNAs were transcribed to RNAs using a T7 Transcription Kit. After the collection of ROI-specific RNAs, the RNAs were further reverse-transcribed, followed by paired-end sequencing on the Illumina platform (Read 1: UMIs and barcode, Read 2: cDNA). Sequences were separated by the sample barcodes with UMI-tools and mapped to the reference genome using HISAT2. UMI-tools and featureCounts were used to generate UMI count data assigned to genes. DEGs were extracted by DESeq2 (FDR = 0. 1). The DEGs were further filtered with adjusted P-values (padj) < 0. 1. DESeq2 was also used to transform the count data into regularized log data before performing PCA using the R prcomp function.
Enrichment maps were constructed with Cytoscape 3. 9 and the BiNGO plug-in using the default settings  (53). The degree of enrichment for each GO was assessed and considered significant when P < 0. 05. GSEA was performed on normalized counts of RNA-seq datasets. Enrichment P-values were estimated by 1,000 permutations. The gene set databases used were Curated (C2; n = 6,449), Hallmark (H; n = 50), Gene Ontology (C5; n = 15,703), and Pathway Interaction Database (C2; PID; n = 196) gene set collections  (54, 55, 57, 64). Specifically, GO results were visualized using the Enrichment Map plug-in for Cytoscape 3. 9 (P-value < 0. 005, FDR q-value < 0. 05). Clusters of functionally related enriched GO terms with ≥ 5 nodes were manually detected. Lipidomic analysis. Lipidomic analysis was performed by electrospray ionization-liquid chromatography-mass spectrometry/mass spectrometry according to a published protocol  (95).
Lipids from each sample were extracted by the method of Bligh and Dyer  (96). To determine the amount of each phospholipid, lipid phosphorus was measured by Bartlett's method  (97). The analysis of phospholipid species was performed using a hybrid triple quadrupole-linear ion trap mass spectrometer (4500Q-TRAP; AB Sciex, Japan) with Nexera UPLC system (Shimazu, Japan). For internal standards, the phospholipid mixture containing 12. and separated using an ACQUITY UPLC HSS T3 column (Waters, Japan) or SeQuant ZIC-HILIC column (Merck Millipore, Germany). The acquired data from Analyst software (AB Sciex, Japan) were processed with MultiQuant software (AB Sciex, Japan). Lipid peaks were identified according to retention times and multiple reaction monitoring transitions, and quantified relative to the internal standard using the peak area ratio method.
Chromatographic settings were based on those previously described  (99). Mass spectrometry analysis was carried out on a Waters Xevo TQ-XS Triple Quadrupole Mass Spectrometer (Waters Corp, UK). This was operated in negative ion mode with a source temperature of 150°C and a desolvation gas temperature of 600°C. Nitrogen was used as the desolvation and cone gas, at a flow rate of 1000 and 150 L/Hr respectively. The capillary voltage was maintained at 3. 5 kV and the cone voltage was set to 30 V. Mass spectral data was analyzed using Masslynx V4. 2 software (Waters Corporation, UK). Mass spectra of all CL and MLCL species was obtained by scanning the m/z range 1000-1600, between 3. 5-7 minutes (scan time 0. 2 seconds). Extracted ion chromatograms were then generated for the individual CL and MLCL species and the peak areas were integrated.
Analyte values were obtained by calculating the ratio of the CL and MLCL peak areas to the internal standard (C14:0)4 CL peak areas. The values were then standardized to protein levels (Lowry). MLCL made in-house (100), (18:2)4-CL and (C14:0)4 CL standards were used to confirm the retention times of these CL species. Multiple reaction monitoring, using specific parent and daughter ion transitions, was also used to confirm the identity of these species. Data availability. Sequencing data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GSE229956). Statistics. Quantitative data were generated in at least three independent experiments. All data are expressed as mean ± SD or SEM, as indicated in the figure legends. Significant differences between two groups were assessed by the unpaired Student's t test with Welch's correction.
Other statistical analyses were performed using one-way ANOVA followed by Dunnett's multiple comparisons. All statistical analyses were done using Graph-pad Prism9 software (GraphPad Software, USA). P-values < 0. 05 indicated a significant difference between groups. In organoid experiments for quantifying specific area or cell number, n represents the number of individual cortical units (the number of organoids tested is specified in figure legends). Organoid and NPC experiments were performed on biological replicates. For each independent experiment, the samples were newly generated from distinct passages of each iPSC line. The measurement of mitochondrial OXPHOS using skin fibroblasts was performed on technical replicates from the Patient 1 sample (n = 1). Study approval. Genetic and functional analysis using samples from study participants was.
Acknowledgments Human iPSCs (Windy) were kindly provided by  Dr Umezawa  of the  National Center for Child Health and Development (Tokyo, Japan ). We thank  Dr Padraig J. Flannery  and  Dr Nina Patel  ( Neurometabolic Unit, The National Hospital for Neurology and Neurosurgery, London, UK ) for their technical assistance in the mass spectrometry analysis. This study was supported by grants from the  Japan Society for the Promotion of Science  (Grant Numbers  JP21463512 ,  JP20K07907 ,  JP21K07869 ,  JP20K21584 , and  JP21K07803 ) and the  Japan Agency for Medical Research and Development (AMED)  under grant numbers,  JP21wm0425007 ,  JP20ek0109488 ,  JP22ek0109486 ,  JP22ek0109549 , and  JP22ek0109493. This study was also supported by grants from the  Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics , and the  Ono Medical Research Foundation  to YN, the  Daiko Foundation ,  The Hori Science and Arts Foundation , the  Mochida Foundation for Medical and Pharmaceutical Research , the  Takeda Science Foundation ,  iPS Academia Japan Inc.
, Leave a Nest  Co. Ltd. ( Ikeda   Rika  award), and a  Grand-in-Aid for research in Nagoya City University  ( 2013009 ) to ISS, and the  Takeda Science Foundation  to YK. HSD is supported by the  Cologne Clinician Scientist Program /Faculty of Medicine/University of Cologne  and funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation , Project No. 413543196 ) as well as the  Koeln Fortune Program   / Faculty of Medicine, University of Cologne , (Project No. 371/2021 ,  243/2022 ). R. D. S. P. and M. F. are supported by a  Medical Research Council (UK)   Clinician Scientist Fellowship  ( MR/S002065/1 ) and  Medical Research Council (UK)  award  MC_PC_21046  to establish a  National Mouse Genetics Network Mitochondria Cluster  (MitoCluster). R. D. S. P. is supported by  Medical Research Council (UK)  strategic award  MR/S005021/1  to establish an  International Centre for   Genomic Medicine in Neuromuscular Diseases (ICGNMD)  and is grateful to The  Lily Foundation  for funding.
Physical and chemical analysis Surfaces were analyzed at the nanometric scale by atomic force microscopy (AFM) in air at room temperature. We used a JPK Nano Wizard II equipped with a glass block Canteliverholder 21,414-E-01 and Budget sensor levers TAP -Al -G coupled to a Nikon eclipse Ti microscope, at the Institut des Matériaux Jean Rouxel (Nantes Université, France). Then, data were processed with the free SPM data visualizer and analyzer Gwyddion software. Surface average roughness (Sa) measurements were performed on the same software using the Roughness average (Ra) calculation option. Raman spectrometry was performed using a Jobin-Yvon LabRam spectrometer equipped with a 2400 lines/mm diffraction grating, a CCD detector and a x50 lens (focal distance = 300 mm), at the Laboratoire de Planétologie et Géosciences (Nantes Université, France).
The light source was provided by an Ar laser Innova 300-5 W from Coherent© operating at 514. 5 nm, a power of 200 mW whit 500 μm hole and μm slit. Spectra were acquired continuously between 100 and cm -1 segmented in 5 by the acquisition software for a duration between 20 and 40 s with an average of 5 repetitions per segment. Raman spectra were then processed and deconvoluted using the Originx64 software. We also carefully characterized the Raman properties of each solution in order to distinguish between contributions due to solution remnants in sample pores from those due to opal structure modifications during experiment. Q n proportion and NBO/T calculations are based on the decomposition of peaks areas, and hence to a spectral reading. These values do not exactly correspond to the true proportions of Q n species, because the spectral signatures may be affected by numerous parameters such as the presence of network-modifying cations (Al) or chargecompensating cations (Na + , K + , Ca 2+ , etc.
Acknowledgments We are thankful for the help of  Laurent Lenta  ( Laboratoire de Planétologie et Géosciences, Nantes Université, France ) for sample preparations. We thank  Marion Rivoal  for the assistance and monitoring during LA-ICP-MS analyses ( Laboratoire de Planétologie et Géosciences, Nantes Université, France ). SEM images and analysis were acquired thanks to the help of  Nicolas Stéphant (Institut des Matériaux Jean Rouxel, Nantes Université, France). We Thank  Sami Soudani  and  Nicolas Récalde  for constructive discussions that helped strengthen this paper. Francesco Mazzero  (Holaster SAS) provided most samples for this study. This work was supported by the  French National program   EC2CO  ( Ecosphère Continentale et Côtière ), grant  12934 , and the  Agence Nationale de la Recherche  ( ANR, France ), grant PALEOSILICA  ANR-20-CE49-0013.
, 2017; Manibusan and Touart, 2017; OECD, 2018; Audouze et al. , 2020; Browne et al. , 2020; Kublbeck et al. , 2020; Legler et al. , 2020). Screening assays for relevant nuclear receptors, which bind certain MDCs and therefore constitute an early event in the molecular pathogenesis of some metabolic disruption related pathways, are underway  (Manibusan and Touart, 2017) , but there is also a need for in vitro methods that can cover endpoints corresponding to adverse outcomes of metabolic disruption with relevance to human health. Recent endeavors in the development of such assays are part of the ongoing H2020 EU projects EDCMET, OBERON and GOLIATH, addressing important organs, such as liver, pancreas and adipocytes. However, these assays are at different readiness levels from pre-development towards the (pre)-validation stage for some assays.
Using a combined approach with phenotyping, interactome studies to decipher the molecular modes of action  (Türkowsky et al. , 2019) , and multi-omics data to identify relevant signaling pathways affected  (Großkopf et al. , 2021). Data for AOP elaboration and quantitative in vitro-in vivo extrapolation will be delivered. Furthermore, detailed analyses of the mTOR signaling pathway, a key regulator of cellular metabolism, will be conducted to decipher the exact modes of action by which MDCs affect this pathway and contribute to the observed adverse outcome. Our studies will enable the provision of biomarkers to support human biomonitoring, and the work carried out in these projects will initially support hazard assessment and ultimately integrated risk assessment of chemicals, and will include adipogenesis and obesity as further endpoints.
| Statistical analyses Data for total lipid content of digestive glands were analyzed using a univariate two-way PERMANOVA (species and season as factors). The PERMANOVAs were preferred to classical ANOVA tests because they do not require assumptions of normality and homoscedasticity for balanced designs  (Anderson & Walsh, 2013). When significant, we used a pairwise PERMANOVA to determine whether the observed differences were significant across all groups (i. e. , all species and/or seasons). Seasonal and interspecies variation in the fatty acid composition of both neutral and polar lipids was visualized using two principal component analyses (PCAs). The PCAs were based on transformed fatty acid data using the Hellinger distance to reduce the weight of rare fatty acids on the ordination  (Legendre & Gallagher, 2001).
, 2019; Rysgaard et al. , 1999)  it is likely that A. moerchi and H. arctica, unlike M. discors, are unable to feed for most of the year. Although fjord-shelf exchange may bring organic matter into the fjord from primary production occurring in the open ocean (which is ice-free in May,  Boone et al. , 2017) , the low quality of organic matter (i. e. , high dominance of SFA) during this period suggests that this food source is primarily refractory  (Bridier et al. , 2019). Sea ice breakup occurred very late for Young Sound during our study (August 2, 2018, i. e. , the latest observed breakup in a century, Greenland Ecosystem Monitoring database), as sea ice retreat usually occurs around mid-July  (Holding et al. , 2019). Considering that primary production in Young Sound F I G U R E 6 Ratio of polar fatty acids of gills to total fatty acids of particulate organic matter.
Background Unlike other viruses, the pathogenicity of human metapneumovirus (hMPV) in adults remains uncertain. To address this question, a retrospective monocentric cohort including all patients admitted to ICU with hMPV infection between January 1, 2010, and June 30, 2018 was performed. The characteristics of hMPV infected patients were studied and compared to matched influenza infected patients. Consecutively, a systematic review and metaanalyses investigating PUBMED, EMBASE and COCHRANE databases was conducted to explore the hMPV infections in adult patients (PROSPERO number: CRD42018106617). Trials, case series, and cohorts published between January 1, 2008 and August 31, 2019 compiling adults presenting hMPV infections were included. Pediatric studies were excluded. Data were extracted from published reports.
Matching was performed using the local database considering age (± 5 years), admission year, and underlying hematologic malignancy. hMPV patients presenting viral coinfections and patients admitted to the ICU for other reason than acute respiratory failure were not included in the pairing process. When pairing was not possible, cases were excluded from the matching analysis. All clinical data were retrospectively collected from medical charts: age, sex, comorbidities, medications, clinical, biological, chest imaging characteristics at ICU admission, length of ICU and hospital stay, and mortality at hospital discharge. Regarding clinical symptoms, flu-like syndrome was defined as the association of fever, myalgia, and headache. Coinfection was considered when other pathogens (virus, bacteria, or fungi) were detected on respiratory samples within 1 day before and 3 days after the diagnosis of hMPV infections.
First chest X-rays after ICU admission was analyzed as well as the closest chest Computed Tomography (CT) scan to the day of hMPV infection diagnosis. Imaging patterns were classified as alveolar consolidation or interstitial pattern according to the predominant anomaly on chest X ray. Pleural effusion, and the number of quadrants involved were also recorded. CT-scans were described regarding several patterns: alveolar consolidations, ground glass opacities, crazy-paving, septal thickening, and bronchial lesions. Chest X-ray and CT-scans were independently analyzed by two intensivists (NK, VL). In case of a mismatch, interpretation was discussed until agreement. All data were expressed as n (%) or median (IQR) as required. Clinical characteristics of hMPV infected patients were first described.
Search strategy and selection criteria PUBMED, EMBASE, and COCHRANE databases were systematically searched between January 1, 2008 and July 31, 2019 using structured search equations (Additional file 1: Appendix 1). Trials, case series, prospective, and retrospective cohorts investigating adult patients with confirmed hMPV infection were included whatever test was used to confirm the hMPV infection. Studies including pediatric patients (under 18 years) were excluded. Meta-analyses, reviews, studies evaluating new diagnosis tests, fundamental research on vaccinology, and epidemiologic reports were not included. The research was restricted to English-written abstracts with full-text articles available. Both upper (URTIs) and lower (LRTIs) respiratory tract infections were included. URTIs were defined as coryza, pharyngitis, sinusitis, or cough with a clear chest CT-scan or X-ray.
LRTIs were defined as exacerbations of chronic obstructive pulmonary disease following Anthonisen's criteria or acute pneumonia with hypoxia and a new infiltrate on chest X-ray. The reference lists of the eligible articles and published meta-analyses were manually searched to identify undetected eligible studies. All identified articles were downloaded and read for duplicates exclusion and eligibility assessment. Searches and data extraction were done alongside by two investigators (NK and VL). Conflicts over inclusion were discussed until agreement with a third investigator (MD). For each included trial, the following information were extracted from published reports: authors, publication year, study design, inclusion period, and country, patients median age, viral diagnosis technique, diagnosis samples, number of patients diagnosed with hMPV-infections, hMPV-URTIs, hMPV-LRTIs, number of ICU admissions, ICU and hospital mortality rates, and viral, bacterial, and fungal coinfections rates.
Data analysis The primary endpoint was the LRTIs rate among hMPV infected patients. Secondary endpoints were ICU admission and pooled hospital and ICU mortality rates among hMPV infected patients. Overall untransformed LRTIs, ICU admission, hospital and ICU mortality rates were assessed, and their 95% confidence interval was calculated using both fixed and random-effects models. Publication bias was assessed by visually inspecting the funnel plot. Cochran's χ 2 test and I 2 test for heterogeneity were used to assess inter-study heterogeneity  [6]. The χ 2 test assesses whether observed differences among results are compatible with chance alone and the I 2 describes the percentage of the variability in effect estimates that results from heterogeneity rather than from the sampling error. An I 2 test for heterogeneity above 0.
25 was considered to indicate moderate heterogeneity. Statistically significant heterogeneity was considered present at χ 2 P < 0. 10 and I 2 > 50%. All effect sizes with a p < 0. 05 were considered significant. Tests were two-sided. In way to explain heterogeneity, a metaregression was performed. Last, three sensitivity analyses were performed in ways to assess impact of publication bias in the observed results using, namely, trim and fill method  [7, 8] , Copas method  [9] , and Outcome Reporting bias method  [10]. All analyses were carried out with software R, version 3. 6. 2. The 'Meta' , the 'metasens' , and 'metafor' packages were used to produce forest plots and to run metaregression and sensitivity analyses. Quality assessment was conducted using the Newcastle-Ottawa quality assessment scales for case-control and cohort studies  [11].
A first simulated time series is generated using a hidden Markov model with external signals and seasonal components as defined above (shmm-es). As external signal (W t ) t 1 , a signal of the fashion dataset introduced in  [David et al. , 2022]  is used to provide a realistic setting. The external sequence is smoothed using a moving average with a sliding window of length 8 and divided by the mean of the first year to rescale the signal. In addition, the smoothed signal is duplicated to simulate an arbitrary long sequence (Y t ) t 1. Figure  2  displays the resulting external signal duplicated 4 times to reach the length of 1000 time steps. We define a set of parameters θ * of a shmm-es with T = 52: π * = π * 1 1 -π * 1 = 0 1 δ * = δ * 11 δ * 12 δ * 13 δ * 14 δ * 21 δ * 22 δ * 23 δ * 24 = 3.
Application to a single time series A sequence 1  from the fashion dataset introduced in  [David et al. , 2022]  was selected as it shows a sudden change of level, seasonality and noise intensity as illustrated in Figure  5. Several models are trained using this time series. hmm, shmm, hmm-es and shmm-es described in Section 4. 1 are considered in this first application. We also consider an autoregressive HMM (ar-hmm), an autoregressive HMM with seasonal components (ar-shmm), an autoregressive HMM with external signals (ar-hmm-es) and an autoregressive HMM with seasonal components and external signals (ar-shmm-es). A complete description of these additional models can be found in Appendix A. 3. 2. To fairly compare and evaluate HMM-based methods, several benchmarks are also evaluated. Four statistical benchmarks are proposed: snaive, thetam, tbats, ets.
Complete descriptions and references for these models can be found in  [Hyndman et al. , 2015]. A recurrent neural network (RNN) model  [Hochreiter and Schmidhuber, 1997]  denoted lstm is also considered. Finally the hybrid model hermes introduced in  [David et al. , 2022]  is added in the pool of benchmarks. Combining the strengths of statistical approaches and RNNs, this model achieved impressive results on the fashion dataset and demonstrated the benefit of the inclusion of influencers signal. As all the previous benchmarks do not include the external signal, variations of lstm and hermes using external signal and called lstm-es and hermes-es are also considered. For HMM-based models and statistical benchmarks, a complete code is publicly available 2. Concerning the training of the HMM approaches, additional information can be found in Appendix A.
4. For lstm, lstm-es, hermes and hermes-es, pre-trained models on the fashion dataset introduced in  [David et al. , 2022]  are directly used without retraining. We evaluate our candidates on a 1-year forecasting task. The 52 points of 2020 are hidden during the training procedure and used to evaluate the accuracy of the models. For each model, a forecast of the test set is computed and the three metrics MAE, MSE and MASE are used to evaluate the prediction. In addition, for HMM-based models, ten independent training procedures are performed   2. In this challenging framework, the 4 statistical benchmarks do not reach the same performance as models with external signals. The same remark can be made with the hmm and shmm approaches. However, the inclusion of the external signal considerably improves the performance of the HMM-based model and they achieve a level of accuracy comparable to RNN-based models hermes-es and lstm-es trained on the whole fashion dataset and using external signal.
Application on a sample of time series In this section, the different approaches are compared using 10 sequences from the fashion dataset introduced in  [David et al. , 2022]. Name and additional materials concerning these trends can be found in Appendix A. 3. For each time series, the same models and training process as in Section 4. 2. 1 are considered. Table  3  summarizes the results on each sequence in terms of MASE. The inclusion of the external signal always largely improves the accuracy of the HMM models. ar-hmm-es method reaches the highest level of accuracy over the 10 time series followed by hmm-es, lstm-es,hermes-es and ar-shmm-es. • On the first fashion sequence, HMMs including the influencers signal leveraged the influencers external signals resulting in a significant improvement of the accuracy compared to the benchmark models.