dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIAL AND METHODS Cohort description. Training cohort, GSE14580 8 : Twenty-four patients with active UC, refractory to corticosteroids or immunosuppression, underwent colonoscopy with biopsies from diseased colon within a week prior to the first intravenous infusion of 5 mg infliximab per kg body weight. Response to infliximab was defined as endoscopic and histologic healing at 4À6 weeks after first infliximab treatment (8 patients as responders and 16 patients as inadequate responders). This data also included 6 healthy controls. Total RNA was isolated from colonic mucosal biopsies, labeled, and hybridized to Affymetrix Human Genome U133 Plus 2. 0 Arrays. Validation cohort, GSE12251:  8  Twenty-two patients underwent colonoscopy with biopsy before infliximab treatment. Response to infliximab was defined as endoscopic and histologic healing at week 8 (12 patients as responders and 11 patients as inadequate responders).
R E T R A C T E D For one patient, data from 2 samples taken at different timepoints were available. RNA was isolated from preinfliximab biopsies, labeled and hybridized to Affymetrix Human Genome U133 Plus_2. 0 Array. This study was performed in accordance with the principles outlined in the Declaration of Helsinki. Microarray analysis. The 2 datasets were downloaded using GEOquery R package. Before-treatment gene expression data were extracted by setting the visit time point to baseline. Probe IDs were converted to gene Entrez ID using the hgu133plus2. db database. The 2 datasets were merged by the common probe IDs. Batch effects were removed using ComBat from the sva R package. 9, 10 To retain the biological differences between responders and inadequate responders, cohortspecific biomarkers were derived prior to applying ComBat.
Human Interactome. The Human Interactome, previously described,  6, 11  contains experimentally determined physical interactions between proteins. These interactions include, regulatory, metabolic, signaling, and binary interactions. The Human Interactome amalgamates data from more than 300 thousand interactions among 18 thousand proteins. Identification of classifier genes. For all genes, the Pearson correlation between the gene expression values and the response to treatment was determined. 12 The signal-to-noise ratio of each gene correlation was calculated by randomly shuffling the response outcome 100 times. Selected genes were then mapped onto the consolidated Human Interactome and the largest connected component (LCC) was determined. Classifier design and validation. Genes identified as discriminatory between responders and inadequate responders to infliximab that were in the LCC were used as features of a probabilistic neural network.
Identification of gene expression features predictive of inadequate response to infliximab. To identify genes whose expression best distinguished responders from inadequate responders to infliximab, 2 publicly available UC patient gene expression datasets were downloaded for which the clinical outcomes data were available. 8 The training cohort data were analyzed to find genes with significant gene expression deviations between responders and inadequate responders  12  (response prediction genes). Unlike conventional differential expression methods that look for large foldchanges in gene expression between 2 groups, this analysis investigated small but significant changes-a high signal-to-noise ratio-between the 2 groups (see Material and methods). Genes were ranked by decreasing value of signal-to-noise ratio and the top genes with the highest signal-to-noise ratio were selected as infliximab response discriminatory genes (Fig  1A ).
Refinement of molecular signature genes using the Human Interactome. The top genes from the training cohort for which expression values across patients were significantly correlated to clinical outcome after infliximab treatment were selected and mapped onto the Human Interactome network map of protein-protein interactions (see Material and Methods) (Fig  1B-C ). The cut-off of the top 180 genes was empirically determined as the minimum number of genes needed for the LCC size to plateau (Supplementary Fig S1 ). Although these genes were identified from gene expression data only, proteins encoded by these genes formed a significant cluster of 39 proteins on the Human Interactome. The 39-protein LCC on the Human Interactome was significantly larger than expected by chance (z-score of 2. 91) (Fig  1B-C  and Classifier training and blinded cross-cohort validation.
Next, the expression of the 39-LCC classifier genes was compared to that of healthy controls, comparing the fold changes relative to responders or to inadequate responders. The patients who were inadequate responders showed the largest divergence in gene expression pattern from that of healthy controls (Fig  3B ). Unsupervised hierarchical clustering analysis showed that the baseline expression profiles of the patients who responded to infliximab more closely resembled the expression pattern of healthy controls than did the inadequate responders (Fig  3C ). The UC infliximab response module is a subnetwork on the Human Interactome. The Human Interactome can serve as a blueprint to better understand the interconnectivity and underlying biology of the inadequate response prediction genes. Thus, the top 180 genes with the highest signal-to-noise ratio between responders and inadequate responders among the validation cohort data were also determined.
This network-based approach evaluates protein interactions to select genes that reflect the biology of disease at the individual patient level. The cross-cohort validation of the predictive classifier developed using a response module found in the Human Interactome, suggests the existence of a molecular signature in baseline tissue samples that characterizes UC patients who will have an inadequate response to TNFi therapy. Further development of such a test would decrease the time to treatment response, thus allowing patients to get back to their normal, productive lives sooner while decreasing the burden on supportive family members. Furthermore, this method of biomarker discovery and classifier development can be applied across multiple disease areas with complex phenotypes and datasets containing molecular information.
Background: Increased occurrence of mucormycosis in India during the second wave of the COVID-19 pandemic in early 2021 in India subsequently prompted us to undertake a multi-site case-control investigation. The objectives were to examine the monthly trend of Covid-19 Associated Mucormycosis (CAM) cases among in-patients and to identify factors associated with it. Methods: Eleven study sites were involved across India and archived records since 1 st January till 30 th September, 2021 were used for trend analysis. The cases and controls were enrolled during 15 th June 2021 to 30 th September 2021. Data were collected using a semi-structured questionnaire. Among 1211 enrolled participants, 336 were CAM cases and 875 were COVID-19 positive non-mucormycosis controls. Results: Admitted CAM-case number reached highest point in May 2021 after a month of peak admission for COVID-19.
With an allocation ratio of 1:2, the sample size was calculated as 73 cases and 145 controls at the power of 95% and alpha error of 5%. In each zone (North, East, West and South plus Central), 218 patients (73 cases of CAM and 145 controls of COVID-19 without mucormycosis) were attempted to be enrolled with overall sample size of 872. Due to the dynamic situation of the pandemic and differential burden of disease during the study period, there was unequal enrollment from different geographic locations. The zone wise enrolment of cases and controls is depicted in Additional Fig. 1. Study tools and data collection: Data were collected using a structured questionnaire with the following domains: i. socio-demographic pro le, ii. present hospitalization, iii. past hospitalization (in last 3 months), and iv.
home-based care for COVID-19. Data were extracted from the medical records of eligible cases and controls (as described above) by designated project staff posted at each of the study sites. They were trained by using virtual platform on how to use the paper clinical investigation reporting form (CRF) prior to data collection. The cases were enrolled while they were admitted for treatment at the hospital while the participants selected as controls were contacted telephonically. Their contact details and other pertinent clinical information were retrieved from hospital records. However, con dentiality of the consenting participants was maintained, and patient identi ers were not entered in the CRF. They were requested to share their laboratory reports and treatment records on WhatsApp or via electronic mail (if not available from the hospital records).
Data management and analysis Information collected on paper CRFs were entered in web portal designed by ICMR with unique login IDs for each participating site. Collation, validation, cleaning and veri cation were carried out at the ICMR Headquarters. Chi-square test was used for trend analysis. Descriptive reporting of socio-demographic, clinical and laboratory pro les of all cases and controls were undertaken. Statistical analyses were performed using student 't' or Mann Whitney U test as appropriate for continuous variables while categorical variables were compared using Chi square or Fischer's exact test. Probability at 5% level was considered as statistically signi cant. Multivariable logistic regression was used to identify factors associated with the development of CAM. Variables with signi cance at p value < 0.
Thus, 1211 patients were enrolled in the study from 10 sites; 336  (  was observed in CAM cases. However, one sample each for former two and two samples each for latter two were observed in the control group. Details of the genomic reads mapped, total reads, pangolin lineage and the accession numbers for each of the strains retrieved is presented in Additional Table  1. A phylogenetic tree depicts a clear segregation of the clades (Figure  3 ). The amino acid variation observed in the spike gene regions is depicted in Additional Figure  2. It indicates the presence of similar mutations across the CAM cases and controls. Clinical and management pro le of participants treated at hospital due to COVID-19: The clinical, laboratory characteristics and management pro le of the CAM cases were compared with the controls as presented in Table  2.
We may hypothesize that this led to more severe immunosuppression, rendering the lung tissue incapable of getting rid of the fungal spores. Strengths and limitations of the study: The current investigation was a large, multi-site study conducted across ve regions of the country. Validated methods were used to ascertain cases and controls. However, the current study had a few limitations as well. The information related to laboratory parameters and treatment was retrieved from the paper-based medical records, which were not uniformly available across all institutes, hence not included in the multivariate model. Due to the nature of data collection (i. e. , telephonically for controls) the information for dusty environments, use of alternative medicines or over-the-counter steroids could not be explored in-depth in the current investigation.
Acknowledgements The authors thank the members of the  ICMR-Mucormycosis group. Anita M. Shete   1 , Triparna Majumdar 1 , Priya Abraham 1 , Anudita Bhargava 2 , Rupa Mehata 2 , Ripu Daman Arora 2 , Richa Tigga 2 , Gopa Banerjee 3 , Vijay Sonkar 3 , HS Malhotra 3 , Neeraj Kumar 3 , Rajashri Patil 4 , Chandrashekhar G Raut 4 , Kumkum Bhattacharyya 5 , Preetam Arthur 6 , L Somu 6 , Padma Srikanth 6 , Naresh K Panda 7 , Dipti Sharma 7 , Wasil Hasan 8 , Aftab Ahmed 8 , Meeta Bathla 9 , Sunita Solanki 9 , Hiren Doshi 9 , Yash Kanani 10 , Nishi Patel 9 , Zincal Shah 9 , Alok Kumar Tembhurne 11  ,  Chhaya Rajguru  (Waghmare)  12  ,  Lalitkumar R Sankhe 12  ,  Shrinivas S Chavan 12  ,  Reetika Malik Yadav 11  ,  Vikas Deswal 13  ,  Kuldeep Kumar   13. 1ICMR - National Institute of Virology, 2 All India Institute of Medical Sciences, Raipur, 3 King George's Medical University, 4 D.
In addition, we observed one of three standards (HD 185144, HD 9407 and HD 89269 A) in HE mode nightly, which we used to track and correct for any long-term instrumental drift following procedures established in  Santerne et al. (2014)  and  Courcol et al. (2015). Our radial velocities were determined by cross-correlating each spectrum with a K5V mask. These methods are described in  Baranne et al. (1996), and Courcol et al. (2015) , and have been shown to produce precisions and accuracies of a few meters per second (e. g. Bouchy et al. 2013; Hara et al. 2020) , well below what we typically obtain on this system. As in  Baranne et al. (1996), and Pollacco et al. (2008) , we correct our data from lunar contamination by first scaling the calculated CCF (cross-correlation function) on fibre A and B (to account for slightly different efficiencies between the two fibres) before subtracting the two CCFs.
2001). In addition, we measure the H stellar activity indicator following  Boisse et al. (2009). These are provided in Table  A1. Two measurements are immediately excluded from our analysis. On 2017-09-06  (BJD 2,458,003. 32008) and 2018-10-06 (BJD 2,458,398. 33382) , when the fibre was mistakenly placed onto another star. This is obvious from the FWHM we extract from these measurement, and from their radial velocity. They are appropriately flagged in Table  A1. In the end, we achieve a mean radial velocity precision of ∼ 10. 6 m s -1 on the remaining 141 measurements. Radial velocity measurements of the primary have also been obtained with TRES and Keck's HIRES  (Doyle et al. 2011; Winn et al. 2011 ). These datasets were not used in this analysis for several reasons. First, the TRES data has a mean precision of 21 m s -1 , which would be insufficient to detect the planet.
Second, the HIRES data, despite offering a precision of a few m s -1 , was only taken on a single night and is contaminated by the Rossiter-McLaughlin signal of the eclipse. Finally, by solely using our own SOPHIE data we may produce a near independent detection. In a similar spirit, we do not use any of Kepler's or TESS's photometric data to conduct our search and parameter estimation. Finally,  Bender et al. (2012)  collected near-infrared spectra to reveal the secondary's spectral lines (i. e. observing Kepler-16 as a double-lined spectroscopic binary). Similarly we do not use these measurements in our analysis, although we do use their estimate for the primary mass.
MODELLING OF THE RADIAL VELOCITIES In order to ascertain our capacity to detect circumbinary planets using radial velocities, as an independent method, we decided to use two different algorithms with different methods for measuring a detection probability. Before describing this, we will detail our procedure to remove outliers. As a reminder, we only use SOPHIE data (see above), where Kepler-16 appears as a single-lined binary and we only observe the displacement of the primary star around the system's barycentre.
Analysis using the genetic algorithm Y Y is a radial velocity fitting tool used for exoplanet detection, described in  Ségransan et al. (2011). It assumes Keplerian orbits (see Sect 3. 5). Y first performs a Lomb-Scargle periodogram of the observations, which is then used to initiate a genetic algorithm that iterates over the orbital period , the eccentricity , the argument of periastron , the semi-amplitude , a reference time  0 , and one systemic velocity per dataset  (for conventions, see  Hilditch 2001). Once the algorithm has converged on a best solution, the final parameters are estimated by using a least-square fit. The tool has been routinely used to identify small planetary companions successfully (e. g. Mayor et al. 2011; Bonfils et al. 2013) , and represents a more traditional, and possibly a more recognised way of identifying a new planetary system than the nested sampler we use subsequently (see  Sect.
Y , K is only deployed on SOPHIE data, and excluding outliers described in Sec. 3. 1. Our K analysis of the Kepler-16 data yields a Bayes Factor BF > 10000 in favour of a three Keplerian model (secondary star, planet and cubic drift). Our posterior shows over-densities at orbital periods of ≈ 230 and ≈ 2000 days, corresponding to the signal of Kepler-16 b and the cubic drift seen in Y. We then apply the clustering algorithm  HDBSCAN (McInnes et al. 2017)  to isolate and extract the resulting planetary orbital parameters, which can be found in table  1.
Note on circumbinary planets' orbital elements The main differences between our fitted parameters and those from  Doyle et al. (2011)  are caused by our parameters being akin to mean parameters (e. g. the mean orbital period), whereas  Doyle et al. (2011)  provide osculating parameters, which are the parameters the system had at one particular date, and which constantly evolve following three-body dynamics  (Mardling 2013). Also, the planetary signal is significantly more obvious in the Kepler transiting data than in our radial velocities. Each measurement within a planetary transit over the primary star produces an SNR = 243 (SNR = 14 when over the secondary). This is why Kepler can derive osculating elements, by solving Newton's equations of motion: the planetary motion is resolved orbit after orbit (transit after transit).
DISCUSSION Our data clearly shows an independent detection of the circumbinary planet Kepler-16 b, the first time a circumbinary planet is detected using radial velocities, and the first time a circumbinary planet is detected using ground-based telescopes as well. We re-iterate that our model fits are solely made using radial velocities and completely ignore any Kepler or other photometric data, to emulate BEBOP's blind search. Importantly our results show we can achieve a precision close to 1 m s -1 on a planetary signal, with the 1 uncertainties on the semi-amplitudes being only just 1. 5 m s -1. This is compatible with the semi-amplitudes of super-Earths and sub-Neptunes (e. g. Mayor et al. 2011). The closest previous detection to a circumbinary planet made from the ground was produced by  Correia et al.
ACKNOWLEDGEMENTS First, we would like to thank the staff, in particular the night assistants, at the  Observatoire de Haute-Provence  for their dedication and hard work, particularly during the COVID pandemic. The data were obtained first as part of a DDT graciously awarded by the OHP director (Prog. ID 16. DISC. TRIA), and then by a series of allocations through the  French PNP (Prog. IDs   16B. PNP. HEB2 ,  17A. PNP. SANT,  17B. PNP. SAN2 ,  18A. PNP. SANT, 18B. PNP. SAN1, 19A. PNP. SANT). The French group acknowledges financial support from the  French Programme National de Planétologie (PNP, INSU). This research received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n  • 803193/BEBOP ) and from the  Leverhulme Trust  (research project grant n •  RPG-2018-418 ).
EW acknowledges support from the  ERC   Consolidator Grant funding scheme  (grant agreement n  • 772293/ASTROCHRONOMETRY ). Support for this work was provided by  NASA  through the  NASA Hubble Fellowship  grant number  HF2-51464  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. P. C. thanks the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC, NSF  Cybertraining Grant # 1829740 , the  Brinson Foundation , and the  Moore Foundation ; her participation in the program has benefited this work. SH acknowledges CNES funding through the grant  837319. ACC acknowledges support from the  Science and Technology Facilities Council (STFC)  consolidated grant number  ST/R000824/1.
DATA AVAILABILITY The radial velocity data are included in the appendix of this paper. The reduced spectra are available at the SOPHIE archive: http://atlas. obs-hp. fr/sophie/. Data obtained under Prog. IDs 16. DISC. TRIA, 16B. PNP. HEB2, 17A. PNP. SANT, 17B. PNP. SAN2, 18A. PNP. SANT, 18B. PNP. SAN1, and 19A. PNP. SANT.
Data analysis All statistical analyses were performed with the R software v. 4. 0. 2 (R Core Team, 2020). Our data are available from Dryad Digital Repository https://doi. org/10. 5061/dryad. 866t1 g1sq. The mean number of buds damaged was compared between maturity classes (young, intermediate and old buds) using a Wald test applied on a GLMM including the individual plant as random factor (distribution: Poisson, link function: log; R package lme4:  Bates et al. , 2015 and car: Fox & Weisberg, 2011). Here and in the following experiments, pairwise comparisons of estimated marginal means (EMMeans) were systematically performed using the emmeans package  (Lenth, 2019)  and p-values adjusted using the false discovery rate correction  (Benjamini & Hochberg, 1995).
Based on this passive transfer study, a 50% micro-plaque reduction neutralization test titer of ≥ 150 was determined as a surrogate of protection, which was supported by analysis of samples from a seroepidemiological study. In conclusion, considering the unfeasibility of an efficacy trial due to the unpredictability and explosive, rapidly moving nature of chikungunya outbreaks, the definition of a surrogate of protection for VLA1553 is an important step toward vaccine licensure to reduce the medical burden caused by chikungunya. JCI Insight 2022;7  (14) :e160173 https://doi. org/10. 1172/jci. insight. 160173 Subsequently, VLA1553 was subject to a phase I dose-escalation study (NCT03382964; https://clinicaltrials. gov/ct2/show/NCT03382964) involving 120 healthy volunteers from 18 to 45 years of age  (17).
Determination of a threshold titer as a surrogate of protection against WT CHIKV viremia. In order to determine a surrogate of protection using the passive transfer of human sera in a NHP CHIKV challenge model, we considered as evidence of protection the lack of CHIKV RNA detection in the blood. CHIKV RNA was detected by a sensitive qPCR assay with a LLOD of 60 CHIKV RNA copies/mL (5 copies/reaction) and a lower limit of quantification (LLOQ) of 500 CHIKV RNA copies/mL (42 copies/reaction). Based on the data obtained in the NHP CHIKV challenge model (Figure  3  and Table  2 ), the threshold titer for protection was set at a μPRNT 50 titer of ≥ 150 (Figure  3 , solid red vertical line) at which none of the animals had CHIKV RNA that was detectable by qPCR. Evaluating all NHPs of the study, 13 animals had a μPRNT 50 titer ≥ 50 after VLA1553 phase I serum transfer and prior to WT CHIKV challenge (d0).
JCI Insight 2022;7(14):e160173 https://doi. org/10. 1172/jci. insight. 160173 We determined the surrogate of protection based on data from 2 independent sources of evidence, the NHP passive transfer study, and data using sera from a seroepidemiology study; this alleviates concerns about relying on the results of a single animal experiment or only on seroepidemiological data. While data derived from humans in prospective seroepidemiological studies may have certain limitations, such as surveillance and testing methods to identify clinical cases of CHIK disease or the detection of baseline CHIKV-neutralizing antibody titers caused by recent CHIK infection, the NHP passive transfer model also has limitations. Nevertheless, the applied cynomolgus macaque (M. fascicularis) NHP model is considered as the most relevant model for CHIK disease, since the immune and pathological responses of NHPs to CHIKV are similar to those seen in human infections  (31, 47).