dataesr/scientific-paragraphs-categorization huggingface dataset:
T-cell subsets associated with the malignant transformation of SMM Immune dysfunction is a hallmark of benign and active stages of MM. 9, 29, 40  However, which immune cell types are significantly associated with its malignant transformation remains largely unknown. 29, 41 So we evaluated the performance of FlowCT for immune biomarker discovery in 150 PB samples from patients with SMM. In spite of the large data set and the length of time needed to gather it (22 months), technical variability was successfully corrected using the gaussNorm method. Afterward, we narrowed the analysis to CD4 and CD8 T cells that were subclustered after initial clustering of PB lymphocytes using FlowSOM (Figure  3A ). FlowCT identified 22 CD4 and 18 CD8 T-cell subsets. A statistical comparison between patients with SMM who had stable disease (n 5 124) and those who had progressive disease (n 5 26) uncovered significantly higher frequencies of TCD4 1 CD28 -TIGIT 1 CD127 low , TCD4 1 CD28 -CD127 1 , TCD8 1 CD28 -TIGIT 1 , and TCD8 1 CD28 - TIGIT 1 CD127 1 T cells in the latter (supplemental Figure  9 ).
Discussion Standard flow cytometry has been frequently used for immune monitoring  43  and newer instruments measuring 30 or more parameters will undoubtedly enhance the ability of flow cytometry to profile patients with cancer. However, data interpretation based on multiple biaxial plots that increase quadratically with the number of parameters being analyzed is a dimensionality explosion that should not be prolonged. 43 Thus, the analysis of high-dimensional single-cell flow cytometry should rely on computational methods that leverage the multiparametric nature of this technology. Here, we present FlowCT to empower flow cytometry users with a relatively simple workspace to process, analyze, and visualize large data sets. Final results can be exported in tabular format for statistical analysis and in FCS format for classical flow cytometry interpretation using other software; however, both can be performed within FlowCT.
We also performed an objective comparison between lymphocyte distribution in paired PB and BM samples from patients with SMM and observed that most subsets showed a significant correlation. Exceptions were TCD4 SCM or TCD4 CM cells, which could be related to the diversity of antigens present in PB and BM, or in other sites from which CD4 T cells egressed into the PB. Using big data and machine learning for clinical cancer care is now a realistic and achievable goal, as demonstrated recently by analyzing samples from solid tumors. 48 Several machine learning algorithms are included in FlowCT and could be used to build predictive models of survival (gradient boosting  49  and random forest  50  ) or to learn classification rules (ie, to identify variables associated with a specific feature, such as partial least squares [discriminant analysis], random forest, and support vector machines).
Acknowledgments This work was supported by grants from the  Instituto de Salud Carlos III/Subdireccion General de Investigacion Sanitaria  and co-financed by  FEDER  funds ( CB16/12/00233 ,  CB16/12/ 00369 ,  PI17/01243  and  PI20/00048 ), and together with FCAECC for iMMunocell Transcan-2 ( AC17/00101 ), the  Cancer Research UK  ( C355/A26819 ), Fundaci on Cient ıfica de la Asociaci on Española Contra el C ancer and  Italian Association for Cancer Research (AIRC)  under the  Accelerator Award Program  (EDITOR), 2017  Multiple Myeloma Research Foundation Immunotherapy Networks of Excellence, Black Swan Research Initiative  of the  International Myeloma Foundation ,  European Hematology Association  nonclinical advanced research grant ( 3680644 ),  European Research Council   2015 Starting Grant (MYELOMANEXT grant   680200 ), the  Cancer Research Innovation in Science Cancer Foundation  ( PR_EX_2020-02 ), the  Leukemia Lymphoma Society , unrestricted grants from  Bristol-Myers Squibb/Celgene  and  Takeda ,  Roche imCORE program  ( NAV-15 ), Innovative immunotherapeutic treatments of human cancer,  Multi-Unit Regional  (n.
In the present study we perform a process-oriented evaluation of CAMSRA O 3 during STT events, selected from the CAMSRA tropopause folds database by  Akritidis et al. (2021) , for the European region and over the time period 2003-2018. Compared with other regions worldwide, the European region exhibits relatively high observational data availability for the examined period. In addition, the role of IFS chemical data assimilation in O 3 STT is explored. Section 2 describes WOUDC (World Ozone and Ultraviolet Radiation Data Centre) and IAGOS (In-service Aircraft for a Global Observing System) O 3 data used for the evaluation, ECMWF IFS system and CAMSRA data, the 3-D labeling algorithm applied for tropopause fold detection, and the methodological approach for the selection of STT events. Section 3 presents the main evaluation results, and finally Sect.
CAMS reanalysis CAMSRA is the latest reanalysis dataset produced by ECMWF, including three-dimensional fields of meteorological, chemical, and aerosol species for the period from 2003 onwards. It comes as a follow-up of the previous successful reanalysis products, the Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA)  (Inness et al. , 2013)  and the CAMS interim reanalysis (CAM-SIRA)  (Flemming et al. , 2017). CAMSRA is based on the ECMWF's IFS CY42R1 cycle and the 4D-VAR data assimilation system  (Inness et al. , 2019a). In more detail, it is based on the minimization of a penalty function that takes the deviations of the model's background fields from the observations to provide the optimal forecast during 12 h assimilation windows (from 09:00 to 21:00 and 21:00 to 09:00 UTC) by modifying the initial conditions accordingly.
Merging the STT events record with the rest of events record provides the climatology of ozonesonde profile. The same approach is followed (steps a, c, and d) for the IAGOS data with one difference in step b. Since the aircraft measurement profiles during takeoff and landing are not strictly vertical, a tracking of the aircraft position is performed and the respective CAMSRA grid cells that include the aircraft route are extracted. Subsequently, the presence of a tropopause fold with p ≥ 50 hPa is explored if it is found in at least one of the extracted grid cells. A schematic representation of the applied methodology for the STT events selection is illustrated in Fig. 2. For direct comparison with observations, CAMSRA O 3 concentrations are also vertically interpolated (linear) with a step of 25 hPa.
The role of chemical data assimilation Here we investigate the role of chemical data assimilation in CAMSRA O 3 representation during the selected STT events. To this end, we present CAMSRA no DA and observed O 3 profiles averaged during the selected STT events and the rest of events, at the WOUDC ozonesonde sites and IAGOS airports in Figs. 8  and   The respective MNMB values illustrated in Fig. 10b  reveal overall an underestimation of O 3 during the STT events. The median FGE and MNMB values depicted in Fig. 7  suggest an overall improvement of MNMB and FGE in CAMSRA due to chemical data assimilation between 500 and 400 hPa and a deterioration above 350 hPa reflecting the aforementioned CAMSRA O 3 overestimation in the upper troposphere.
Conclusions A process-oriented evaluation of CAMS reanalysis O 3 during tropopause folding events over the period 2003-2018 is performed using WOUDC ozonesonde data and IAGOS aircraft measurements. The selected STT events were obtained from the CAMSRA tropopause folds database by  Akritidis et al. (2021) , which was constructed with the implementation of the 3-D labeling and fold detection algorithm by  Škerlak et al. (2015). Moreover, the role of chemical data assimilation in O 3 representation during the examined STT events was investigated using a CAMS control simulation without chemical data assimilation. The most notable findings of the study are summarized as follows. -CAMSRA reproduces the observed O 3 increases in the troposphere during the examined folding events, which as indicated by the respective O 3s profiles are of stratospheric origin.
-For some sites CAMSRA misses the observed return of O 3 concentrations back to normal tropospheric levels, resulting in an overestimation of O 3 in the upper troposphere, with FGE values at 350 hPa ranging from 0. 13 to 0. 38 (median of 0. 28) at the observational sites. -The use of chemical data assimilation in IFS is found to be beneficial for the representation of CAMSRA O 3 enhancements in the troposphere during the STT events. However, it leads to an overestimation of O 3 concentrations at the upper portion of O 3 increases. -Overall, and in terms of O 3 bias and absolute bias, only a small improvement is found between 500 and 400 hPa due to chemical data assimilation implementation. The present analysis indicates that CAMSRA satisfactorily reproduces the observed O 3 increases in the troposphere during the tropopause folding events.
Differential egg-adaptation pathways in historical vaccine strains Our previous study has shown that the two major egg-adaptive mutations G186V and L194P are incompatible  [14]. To examine whether different human H3N2 strains have different preferences of egg-adaptation pathways, HA sequences from egg-passaged strains were collected from the Global Initiative for Sharing Avian Influenza Data (GISAID)  [20]  (Supplementary Table  1 ). Specifically, we focused on egg-passaged strains that were derived from WHO-recommended candidate vaccine viruses for influenza seasons between 2008 and 2020. Egg-passaged strains that were derived from different parental viruses have acquired different egg-adaptive mutations (Figure  1A ). For example, all egg-passaged strains from A/Victoria/361/2011, A/Texas/50/2021, A/Switzerland/9715293/2013, and A/Hong Kong/2671/2019 have acquired G186V, whereas those from A/Hong Kong/4801/2014, A/Singapore/INFIMH-16-0019/2016 (Sing16) and A/Switzerland/8060/2017 (Switz17) have acquired L194P.
Sequence analysis Full-length HA sequences of WHO-recommended H3N2 candidate vaccine virus strains from 2008 to 2020 were downloaded from the Global Initiative for Sharing Avian Influenza Data (GISAID; http://gisaid. org)  [20]. Information for these sequences is shown in Supplementary Table  1. Information for the full-length HA sequences of viruses from 2019 to 2021, with and without egg-passaging, is shown in Supplementary Table  4. Sequences were aligned by MAFFT version 7 (https://mafft. cbrc. jp/alignment/software/) with default setting  [35]. Passaging history was determined by parsing the regular expression in FASTA headers as described  [36]. The frequencies of amino acids were plotted using WEBLOGO (https://weblogo. berkeley. edu/logo. cgi)  [37]. Phylogenetic tree was built using Maximum Likelihood (ML) method by Geneious Prime (Geneious), with 100 bootstraps.
Analysis of next-generation sequencing data Sequencing data was obtained in FASTQ format and analyzed using in-house Python and shell scripts. Briefly, paired-end reads were merged by PEAR  [43]. The merged reads were then parsed by SeqIO module in BioPython and translated into protein sequences  [44]. For each sample, the frequency of amino acid variant 𝑖 was computed as follows: 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 ! = 𝑟𝑒𝑎𝑑 𝑐𝑜𝑢𝑛𝑡 ! 𝑠𝑒𝑞𝑢𝑒𝑛𝑐𝑖𝑛𝑔 𝑑𝑒𝑝𝑡ℎ where 𝑟𝑒𝑎𝑑 𝑐𝑜𝑢𝑛𝑡 ! represents the number of reads that contain amino acid variant 𝑖 and 𝑠𝑒𝑞𝑢𝑒𝑛𝑐𝑖𝑛𝑔 𝑑𝑒𝑝𝑡ℎ represents the total number of reads in the sample of interest.
ACKNOWLEDGEMENTS This work was supported by a fellowship from the  Pasteur Foundation Asia  (W. L. ), a  Calmette  and  Yersin scholarship  from the  Pasteur International Network Association  (H. L. ), a startup fund from the  University of Illinois at Urbana-Champaign  (N. C. W. ), the  Health and Medical Research Fund  (no. 19180932 ) (C. K. P. M) and the  National Research Foundation of Korea (NRF)  grant funded through the  Korea government  ( NRF-2018M3A9H4055203 ) (C. K. P. M. ). C. K. M. P is a visiting scientist at the  Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Data availability Raw sequencing data have been submitted to the NIH Short Read Archive under accession number: BioProject PRJNA800806. Codes for analyzing next-generation sequencing data have been deposited to https://github. com/Wangyiquan95/HA_egg_passage. Code and source files for structural modeling using Rosetta have been deposited to https://github. com/timothyjtan/HA-RBD_height.
5 at above 2 kHz only for thickness 20 mm. Only lobia shell showed good absorption coefficient from thickness 5 mm. Another bio waste material as acoustic absorber was published by Yun et al. [10]  by utilising coffee wastes from coffee shops. Sample absorbers for various particle sizes were tested and the measured results were simulated through architectural acoustics software to present reduction of reverberation time in a room when the coffee waste absorber is applied as the room ceiling. Sing and Mohanty  [11]  applied the jute felt and waste cotton to reduce the noise level from HVAC unit of a motor vehicle. The outcome was the reduction of noise level by 4 dB(A) and loudness level by 7 sones. Subjective data from passengers were also collected and revealed that application of the natural materials successfully reduced the annoyance caused by the HVAC noise.
Optimization Function The genetic algorithm considered in this paper uses an optimization function based on the comparison between the experimental absorption coefficient α (exp) and the theoretical absorption coefficient α (teo) from Equation  (6). The target criterion was set as  [26] β = N ∑ n=1 α (exp) n -α (teo) n α (exp) n 2 (7) where n is the index and N is the total number of discrete points of frequency considered in the experiment. A similar methodology was previously considered by Pereira et al. [27]  for porous concrete materials, where the Horoshenkov-Swift model  [28]  was used for modeling the theoretical absorption. A MATLAB/OCTAVE routine was used on the computational composition of the genetic algorithm. The function ga calculates the minimum error of the objective function β in Equation  (7).
Methods: A cross-sectional observational analysis was conducted using company data from the Rock Health Digital Health Venture Funding Database, the US Food and Drug Administration, and the US National Library of Medicine. Companies were included if they sell products targeting the prevention, diagnosis, or treatment phases of the care continuum. Clinical robustness was defined using regulatory filings and clinical trials completed by each company. Public claims data included clinical, economic, and engagement claims regarding product outcomes made by each company on its website. Results: A total of 224 digital health companies with an average age of 7. 7 years were included in our cohort. Average clinical robustness was 2. 5 (1. 8 clinical trials and 0. 8 regulatory filings) with a median score of 1.
Most studies focusing on clinical impact are narrowly defined to specific clinical therapeutic areas (eg, diabetes, cardiac arrhythmia), making it difficult to extrapolate findings to the broader field of digital health  [16] [17] [18] [19] [20] [21] [22]. Additional limitations include a small sample of companies and the use of publications as a proxy for clinical impact. Other studies in digital health have examined larger clinical trends, such as growth in clinical trials, but these often lack data on clinical and customer focus  [23, 24]. As a result, the literature is often too narrow or too broad to enable an understanding of clinical impact. In addition, no studies have examined both clinical rigor and public claims made by companies. To address these limitations, we sought to comprehensively examine the topic of clinical robustness in digital health companies by using a more comprehensive definition of clinical rigor and examining companies' public claims across the most in-depth database of US-based digital health companies.
Population Companies were identified using the Digital Health Venture Funding Database maintained by Rock Health Inc, a digital health venture fund and advisory firm, which has been used in prior studies  [25] [26] [27]. The database includes all digital health companies with headquarters in the United States that have raised at least one venture funding round of US $2 million or more since 2011. Our analysis included companies that sell products targeting the prevention, diagnosis, or treatment phases of the care continuum, which raised at least one round of funding between 2011 and 2020. Digital health companies are defined as those that build and sell digital technologies in health care  [28, 29].
Company Variables Total venture funding, clinical area(s) of focus, care continuum phase(s), and customer data were collected for each company. Clinical areas represented 1 of 20 specific clinical domains (eg, cardiovascular, nephrology). Care continuum phase was defined according to the following: prevention, diagnosis, or treatment. Customer type referred to the category of buyer for a company's products, such as payer (ie, health insurance companies), biopharma (ie, pharmaceutical or biopharmaceutical companies), and medical devices (ie, companies that manufacture medical devices). Companies can be categorized into multiple categories (ie, companies may address multiple phases of the care continuum or multiple clinical areas). Company variables were gathered from the Digital Health Venture Funding Database, which Rock Health maintains using publicly available information such as company websites, press releases, and US Securities and Exchange Commission filings.
Clinical Robustness Variables We collected regulatory data from the Food and Drug Administration (FDA), including the number of 510(k), De Novo, and premarket approval filings (where the company was listed as the "Requester" on fda. gov). In addition, we collected the number and type of clinical trials by searching ClinicalTrials. gov where the company was listed as "Sponsor / Collaborator. " Data on both FDA filings and clinical trials were collected between July 1, 2021, and September 2, 2021, through a combination of web scraping and manual searching. Data collection on claims was completed by at least 3 authors, with blinded cross-review to ensure consistency in data collection. A "clinical robustness" score was calculated for each company, defined as the sum of the number of regulatory filings and clinical trials.
Limitations Our definition of clinical robustness was limited to clinical trials and regulatory findings, which remain proxies for effectiveness and require companies to register their activities. However, we believe these elements offer a better estimation of clinical rigor than publications, which can lag in timing and may not always relate to clinical outcomes. We chose to focus on clinical trials and regulatory filings because they were publicly available data and are generally undertaken to demonstrate an impact on clinical outcomes. Our approach to measuring clinical robustness equally accounts for clinical trials that demonstrate a technology does and does not work. Additionally, our data collection methodology could have missed clinical trials that were not registered on ClinicalTrials.
Another study published in 2021  [18]  enrolled 603 MDS patients transplanted using an HID (n = 176) or MUD (n = 427) from the Center for International Blood and Marrow Transplant Research database. Multivariate analysis revealed higher relapse (hazard ratio [HR] 1. 56; p = 0. 0055; 2-year relapse rate, 48% vs. 33%) and lower disease-free survival (DFS) rates after allo-SCT with HID (HR 1. 29; p = 0. 042; 2-year DFS, 29% vs. 36%). However, OS rates did not differ between donor type (HR 0. 94; p = 0. 65; 2-year OS, 46% for HID and 44% for MUD) because of the mortality associated with chronic graft versus host disease (GvHD) in the MUD group. Therefore, we decided to conduct a retrospective analysis to investigate the impact of HID versus HLA-matched donor (including MSD and MUD) on patient outcomes in allografted MDS.
Study design We conducted a retrospective multicenter study. This study was approved by the scientific committee of the Francophone Society of Bone Marrow Transplantation and Cellular Therapy (SFGM-TC). Informed consent was obtained for each patient before inclusion, and the study was conducted according to the Declaration of Helsinki. Participating centers were asked to verify the recorded data for each patient and to provide additional information. We included patients transplanted from an HID or HLA-matched donor (10/10) between 2010 and 2019. Inclusion criteria were MDS defined according to the WHO 2008 classification, age ≥18 years, and first allo-SCT. Poor cytogenetics (-7, inv(3)/t(3q)/del(3q), double including -7/del(7q) and 3 abnormalities) and very poor (complex: > 3 abnormalities) according to IPSS-R were included in this study.
One major limitation of our study is the heterogeneity of the conditioning regimens depending on each center's policy, even if we tried to gather them into three main groups. The second main limitation is that practitioners may extend GvHD prophylaxis in patients with HIDs, but data on immunosuppressant tapering were not provided by the Promise database. It has been demonstrated that early tapering of immunosuppressive agents can improve the survival of patients with advanced acute myeloid leukemia, and this strategy might be of benefit for high-risk MDS. The third limitation is the imbalanced characteristics between the groups at transplant in terms of graft type, conditioning regimen, and GvHD prophylaxis. However, the three groups were comparable in regard to the MDS characteristics. Because the main differences between the three groups were intrinsically linked to the backbone of the haploidentical allo-SCT platform, we did not choose to perform a propensity score analysis and focused, instead, on the multivariate analysis.
Introduction Complementary and alternative therapies (CATs) refer to a diverse range of approaches that can be used as add-on or an alternative to conventional therapies. While a number of individual studies and systematic reviews (SRs) or meta-analyses (MAs) have investigated the effectiveness of specific types of CATs to treat depressive symptoms at specific moments of the perinatal period, an overarching synthesis of the literature is currently lacking. We will conduct an umbrella review of SRs and MAs to assess to which extent CATs are associated with depressive symptoms reduction during pregnancy or after childbirth. Methods and analysis We will search a broad set of electronic databases (MEDLINE via Ovid, Embase. com, CINAHL via EBSCOhost, PsycINFO via Ovid, AMED and Google Scholar).
MAs will be performed by using the data from the individual RCT studies included in the SRs or MAs. Sensitivity analyses restricted to studies with a low-moderate risk of bias will be realised. Publication bias will be examined visually by using a funnel plot, and formally using the Egger's test and test of excess significance. Ethics and dissemination We intend to publish the results of the umbrella review in an international peerreviewed journal. Oral presentations in congresses and internal diffusion through the Rise up-PPD European COST Action network are also planned. PROSPERO registration number CRD42021229260.