dataesr/scientific-paragraphs-categorization huggingface dataset:
We report here a novel HLA-B*44 allele, now named HLA-B*44:324:02 that carries one nucleotide substitution in exon 3 when compared to the HLA-B*44:324:01 allele, identified in a volunteer bone marrow donor. The HLA typing was performed using Next Generation Sequencing (AllType NGS, One Lambda, Canoga Park, CA) on the Ion S5 system platform (ThermoFisher Scientific, Waltham, MA),  1  from exons 1 to 7. The reads were analyzed using the TypeStream Visual Software version 3. 0 (One Lambda). This donor was found to   1 ). This nucleotide change was confirmed using other NGS reagents provided by GenDX NGSgo-MX6-1 (Utrecht, Netherlands) run on the Illumina MiSeq system (San Diego, CA) and analyzed with the NGSEngine software (GenDX, version 2. 26). We were very confident in the phasing as the sample displayed a mean read length of 313 base pairs over all the loci, the mismatched C base was attributed 198 times to the new HLA-B*44 allele and can be only attributed to this allele because it was possible to discriminate from the associated HLA-B*07:02:01:01 allele by virtue of seven variant positions each distant by less than 100 base pairs.
HLA typing by Luminex reverse sequencespecific oligonucleotide (SSO) was performed (One Lambda Labtype XR, Canoga Park, CA). 3 With this assay (lot 007, catalog RSSOX1B_007_04), the most likely HLA-typing of the donor was B*07:EENNR, 44: EGAFV (most likely allele B*07:02, 44:03, respectively) without any bead modification. Indeed the IPD-IMGT/ HLA Database 3. 51. 0 release describes many alleles displaying a TAC in codon 99 (as HLA-B*44:03) and few other HLA-B alleles displaying an ACC sequence in codon 283 (as HLA-B*44:324:01), explaining why the manufacturer did not include probes targeting this codon. The coding nucleotide sequence of the new allele has been submitted to the GenBank database (Accession No. OQ378320) and to the IPD-IMGT/HLA Database (Submission No. HWS10065195). The name B*44:324:02 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in February 2023.
have a new HLA-B*44 allele and was consequently typed A*24:02, 29:02; B*07:02:01, 44:324:02; C*07:02, 16:01; DRB1*07:01, 15:01; DRB4*01:01; DRB5*01:01; DQA1*01:02, 02:01; DQB1*02:02, 06:02; DPA1*01:03, 02:01; DPB1*04:01, 11:01. Using the IPD-IMGT/HLA Database, 2 nucleotide sequence alignment with HLA-B alleles shows that this new allele has one nucleotide change from B*44:324:01 in codon 99 in exon 3 where T ! C, not resulting in a coding change (TAT ! TAC, Figure.
To ensure the proper functioning of the market, it is an efficient and challenging topic to enhance the identification of excessive market corner risks. The most formidable challenge associated with this subject matter lies in the presence of consistent market corner patterns and characteristics, despite the absence of readily accessible labeled datasets  [23] [24] [25] [26] [27] [28]. Motivated by the above issues, we propose in this article a hybrid anomaly detection approach based on the combination of Local Outlier Factor (LOF), Support Vector Machine (SVM), and Random Forest (RF) algorithms. First, the outliers are separated and marked to solve the no-label problem due to abnormal prices and volumes. Then, based on the market behavior prior to the expiration day, SVM & RF are conducted to identify whether there will be a market corner risk on the expiration day.
Data Description This article focuses on the price anomalies caused by market corners and is not intended to discuss the reasons behind the trend or the manipulation initiated by any institution or individual. The data used comes from the data related to the market and price Investing 1. The downloaded data includes daily trading data from April 1983 to October 2020, containing opening price(open), closing price(close), highest price(high), lowest price(low), change rate(rate), and trading volume(volume).
After that, the data we use is the closing price(close) and trading volume(volume) of all trading days. Examples of real data used in the model are shown in Tab. 1 A pseudo label corresponding to the market corner can be generated based on the anomaly detection results. The closing date for trading in the current delivery month is the third business day before the 25th day of the previous month. Based on the design mechanism of the settlement price, the settlement date is not fixed, so we programmed all the predicted points in time to be the settlement date based on the actual situation in the model. The pseudo-code is shown in 1. The occurrence of market corners is not a temporary intention but needs to be arranged in advance. Moreover, the event of abnormal prices and trading volume is not accidental, but the factors of planning and layout in advance, and these activities will eventually be reflected in the market trading behavior.
Therefore, the data we use are the market change data generated by trading activities on all trading days before the settlement date of each month. The label we used in fitting is the first step, divided into abnormal for '1' and nonabnormal for '0'. The examples of real data used in the model are shown in Table  2. In summary, we first detect market corner risk by the characteristics and generate pseudo-labels based on the identification results. Then, since the market corner cannot be a random, spontaneous action but must be prepared for, we classify them based on the market behavior and the labels generated in the first step.
Data Processing As mentioned in 4. 1, the data selected in this article are the real transaction data of WTI. We use the median interpolation method to fill in the missing value for the lost value of trading volume in the data. Then, to update the original data more quickly, we use a linear function to transform the original data into the range of [0-1] and normalize the data. The normalization formula is as follows. Xnorm = X -X min Xmax -X min (8) In this method, the original data is scaled equivalently. Based on the data set, we use the data between the trading day of each month and the trading day of each month to predict whether there will be any abnormality in our trading day. Among them, the data between two trading days is the training data value, whether the trading day is abnormal is tagged, taking 0 and 1.
Feature Selection As mentioned in Section 4. 2, in the first step of the anomaly detection algorithm, we select the features of daily settlement price (close) and volume (vol). Next, we take the month as the unit and select the market trading data of all trading days before the settlement date, including opening price(open), closing price(close), highest price(high), lowest price(low), change rate(rate) and trading volume(vol) to train the classifier. After data processing and feature selection, the final modeling data examples in step1 are shown in Tab. 3, and data examples in step2 are shown in Table  4.
Parameter Sensitivity According to the results in step1, we have obtained the label corresponding to the sample. Besides, according to the definition of the problem, we take the abnormal results of the first step on settlement day as the training label and the market data of the trading day before the settlement day of each month as the training characteristics, so that SVM and RF are conducted to this dataset. In the model training, we use the ratio of training set: test set as 85%:15 % to train the model. We need to adjust the model parameters to obtain a better training model.
Scalability Despite the current design of the model has achieved excellent performance, it can only detect the abnormal price on the settlement day one day in advance in practical application. If only one day is left, there is likely no time to prevent or adjust. On the settlement day, it often occurs that the contract cannot be traded in time because there is no counter offer. Therefore, we attempt to use the model to forecast seven trading days in advance and remove the data near the settlement date, and examples of data are shown in Tab. 6. We applied the processed dataset to fit the model, adjusted the parameters, and tested it in the validation set. The performance is shown in Table  7. The ROC curve on the verification is shown in Fig. 9. In the forecast performed, the accuracy and F-score of both SVM and RF declined since some samples were removed near the settlement date.
In particular, the research on the connection between the definition of price manipulation and the composition of labels is worthy of reference for further investigation. There is still room for improvement in this study, and listed as follows. 1. Consider using the dataset with finer time granularity. Since this article solely has daily data, it adopts the approximate value that fails to match the settlement mechanism perfectly, 2. Consider updating other models to improve the model's efficiency, 3. Consider applying the scheme in other financial market fields.
The electron current density was ≈63 pA cm -2. The electron-selected area diffraction patterns could not be obtained reliably due to a partial loss of the structural integrity of the samples imaged with a high-energy electron beam. To quantify the elemental atomic compositions and for stoichiometric assessments, we used a Bruker AXS TEM software package. SEM images and 2D elemental chemical maps were collected with the help of a Jeol JSM-7900F microscope operating at a topview mode and a low accelerating bias of ≈5 keV. The PL and PLE spectroscopic measurements were done by exciting samples with a spectrally filtered light generated by a Xenon lamp (optical power density of ≈5 mW cm -2 ). All emission spectra were collected with an Olympus 51X microscope and dispersed by a second double-grating monochromator.
Experimental Results and Analysis A characteristic XRD spectrum is presented in Figure  1  and consists of an array of individual peaks in confirmation that the films remain polycrystalline. Using X'Pert High Score software, three distinct crystalline phases were detected and assigned to Zn According to a top-view SEM image of the substrate-attached structures, Figure  2  a, the film comprises aggregates of highly flattened automorphic and prismatic crystals with the lateral dimensions ranging from ≈10 to 100 μm. The prismatic morphology of the crystals appears very similar to that of the hopeite reported by Milnes. [20]  The elemental compositions of Zn 3 (PO 4 )   Composition-wise, the prismatic phase is found to be made from ≈55%-60% of [Zn+O] and ≈40%-45% of [2P + 5O] elemental stochiometric combinations.
Background Although Acute Kidney Injury (AKI) incidence is increasing worldwide, data investigating its cost are lacking. This population-wide study aimed to describe the characteristics and costs of hospital stays with, and without AKI, and to estimate the AKI-associated increases in costs and length of stay (LOS) in three subgroups (major open visceral surgery (MOV), cardiovascular surgery with extracorporeal circulation (CVEC), and sepsis). Methods All hospital stays that occurred in France in 2018 were included. Stay and patient characteristics were collected in the French hospital discharge database and described. Medical conditions were identified using the 10 th International Classification of Diseases and the medical acts classification. In each subgroup, the adjusted increase in cost and LOS associated with AKI was estimated using a generalized linear model with gamma distribution and a log link function.
Study design and ethics This study is a retrospective cross-sectional populationwide database study, using the French hospital discharge database (Programme de Médicalisation des Supports d'Information, PMSI). PMSI is hosted and controlled by the technical agency for information on hospital care (Agence Technique de l'Information sur l'Hospitalisation, ATIH). According to French law, data were collected via the platform of the secure data access center (Centre d'Accès Sécurisé aux Données, CASD) after a declaration to the national institute of health data (Institut National des Données de Santé, INDS) through the reference methodology 006 (MR-006), n°2214295. Because all patient-level data in the PMSI database are anonymized, institutional review board, ethics approval, and informed consent at an individual patient level were not required.
Data source The PMSI contains a discharge summary for each single stay that occurs in each public or private hospital in France (approximately 3,000 hospitals in 2018). Each stay is identified by a main diagnosis (the reason for hospital admission) and several associated diagnoses (medical comorbidities of the patient or associated medical conditions and complications that occurred during the same hospital stay). All diagnoses are registered in the PMSI for billing purposes, according to the International Classification of Diseases, 10 th revision (ICD-10) codes. The PMSI also contains all medical procedures performed during the stay, encoded according to the French medical classification of clinical procedures (Classification Commune des Actes Médicaux, CCAM). According to the codes entered in the hospital stay resume (including codes for AKI), each stay is classified within a Diagnosis Related Group (DRG) of patients that shares common characteristics and have similar resources use.
Statistical analysis The statistical unit for analysis was the hospital stay. The characteristics of AKI and no-AKI stays were described using mean and standard deviation (SD) or median and interquartile range (IQR) for continuous variables, and frequencies and percentages for categorical variables. Because the whole population was included without sampling, statistical tests were not relevant to compare populations. Missing data were not replaced. To estimate the increase in cost and LOS associated with AKI in the three subgroups, a case-control analysis was performed. Control stays (no-AKI stays) were matched to AKI stays with a 1:3 ratio (i. e. , one AKI stay was matched to 3 no-AKI stays in each subgroup). Matching was considered acceptable beyond 90% of patients matched. If less than 90% of patients were matched, a lower ratio was used (1:2 then 1:1).
Study population The 2018 PMSI database contains 26,917,832 stays, of which 9,458,453 (35. 1%) were admissions for specific procedures and health care (including radiotherapy and chemotherapy sessions, rehabilitation, and dialysis sessions). All of these PMSI stays were included in the main analysis and a code for AKI was present in 415,067 stays (1. 54%). An explicit code for sepsis was present in 421,543 (1. 6%) stays, a code for CVEC in 40,084 (0. 1%) stays, and a code for MOV in 166,831 (0. 6%) stays. An AKI code was associated with 83,553 (19. 8%), 7,165 (17. 9%), and 15,387 (9. 2%) of the sepsis, CVEC, and MOV stays respectively. The study population flow-chart is reported in Fig. 1.
2% when excluding repeated stays for medical procedures (358,057 AKI stays out of 17,459,379 stays). Although a similar incidence of AKI has been previously reported in studies conducted retrospectively from large databases, it contrasts with the higher incidence of AKI reported when prospective methodologies are used  [24]. This suggests an under declaration of AKI by clinicians in the billing charts and highlights the need to increase the efforts towards recognizing and coding AKI more systematically as it largely impacts costs, LOS, and outcomes. Concerning subpopulations, AKI incidence in surgical stays was similar to the results observed in a recent epidemiological study  [14]. For sepsis, we identified a higher number of stays compared with two recent studies using the same discharge database  [21, 25].
Finally, in the PMSI was filled with updated ICD-10 codes, using an improved and easier classification of AKI, probably reducing the misclassifications of AKI compared to the previously used ICD-9 codes. Nevertheless, the present study has certain limitations. First, the severity of AKI and its cause cannot be determined using the PMSI. Because dates cannot be entered along with ICD-10 codes, causal relationship between diseases cannot be assessed from this database. For example, if a stay is associated with an AKI and a sepsis code, it is impossible to know what disease occurred first or if they occurred at the same time. Second, the quality (exhaustivity and exactitude) of the codes entered may be questioned. The correlation between AKI codes in databases used for billing purposes, such as the PMSI, and clinically confirmed AKI is largely debated.
Observations In order to trace embedded star formation, we use the 21 μm emission map observed with MIRI on board JWST as a part of PHANGS-JWST survey. This data was obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. 35  This mid-infrared wavelength has been widely used as a tracer of embedded star formation, because a substantial fraction of the emission, especially that with compact morphology, originates from dust excitation by radiation from surrounding massive stars and empirically exhibits a correlation with tracers of massive star formation  (Kennicutt & Evans 2012; Galliano et al. 2018; Hassani et al. 2022; Leroy et al. 2022; Thilker et al. 2023). In particular, using four initial targets,  Hassani et al. (2022)  have found that 90% of compact 21 μm sources are associated with H II regions detected in extinction corrected Hα maps from MUSE.
A composite three-color image of the CO, 21 μm, and Hα emission maps is also provided, where the spatial small-scale decorrelation of these tracers is illustrated by the color variations. The Hα emission map is from PHANGS-Hα (Preliminary version; A. Razza et al. 2022, in preparation)  observed using the Wide Field Imager instrument at the MPG-ESO 2. 2 m telescope at the La Silla Observatory. We use the 12 CO(J = 2-1) transition (CO hereafter) from PHANGS-ALMA as a tracer of molecular gas. A detailed description of the full sample and data reduction can be found in  Leroy et al. (2021b Leroy et al. ( , 2021a)). The observations were carried out with the 12 m array, as well as with the 7 m and total power antennas of the Atacama Large Millimeter/submillimeter Array (ALMA). We use the moment-0 map at the native resolution reduced with an inclusive signal masking scheme with high completeness (the "broad" mask; see  Leroy et al.
(2022)  are shown in the plane spanned by the galaxy stellar mass (M *,global ) and the galaxy-wide SFR (SFR global ). As a proof of concept, we have measured the timescales of the embedded and heavily obscured phases of star formation in one of the JWST target galaxies, NGC 628. No trend is found between the duration of the embedded phase (here denoted by t fb,MIR because the figure combines measurements from Spitzer at 24 μm and from JWST at 21 μm) and the galaxy properties (e. g. , mass, SFR, offset from the main sequence), but NGC 628 represents an important extension of the parameter space shown here. Our results highlight the power of JWST by demonstrating that the quality of the data enables the embedded phase of star formation to be systematically characterized for a galaxy located at 9.
Scenarios for evaluation In the base case analysis, we simulated seven priority screening algorithms as identified by the Guidelines Development Group: primary VIA, primary HPV (no triage but with VIA to determine treatment eligibility), primary cytology with HPV triage in case of finding ASC-US cytology, primary HPV with HPV 16/18 triage, primary HPV with VIA triage, primary HPV with cytology triage and primary HPV with colposcopy triage (Table  1 ). These priority algorithms were selected by the Guidelines Development Group for consideration due to the availability of quality data on test sensitivity and specificity as well as evaluations of the potential feasibility, costs and acceptability of possible implementation. Notably, detailed management for each of these screening scenarios, including downstream management for women in follow-up, at colposcopy and after pre-cancer treatment, is described by the WHO online  36.
Model calibration and validation outcomes Details of the model parametrization process and calibrated model variables presented here have been adapted and reproduced from a previous publication  13  and the open-access doctoral thesis from which this study forms a chapter  62. The input parameters are specified primarily using empirical data; however, some parameters, if unobservable or informed by survey data, were found through calibration using MATLAB's (R2018b) inbuilt nonlinear least squares solver for data fitting called 'lsqnonlin', which used a trust region reflective algorithm option. The calibrated inputs included sex-specific, age-specific and activity-group-specific volumes of high-risk sexual contacts per timestep, the degree of age-assortative sexual mixing, annual fluctuations in population-level risk-aversive behavior and the relative per-sex-act probability of HIV acquisition for females compared to males.
Calibrated model outcomes were additionally similar to age-specific cervical cancer incidence and mortality rates for 2018 as reported by the International Agency for Research on Cancer 66 and are reported in Fig. 4  of Hall et al. 13. Following the calibration procedure, the model was validated against independent data (that is, not used in model training). For HIV-specific outcomes, validation data included sex-specific and age-specific HIV prevalence and the sex-specific age distribution of age at AIDS diagnosis. The model fit to these data appears as Fig. 5  in Hall et al. 13  , and the underlying validation data were sourced from the Tanzanian Ministry of Finance. Validation data for HPV and cervical cancer-related outcomes included age-specific HPV prevalence (Fig. 6  in Hall et al. 13  ) and the prevalence of high-grade squamous intra-epithelial lesion (HSIL), considered equivalent to a diagnosed CIN 2/3 prevalence among HIV - versus HIV + women (Fig.
Acknowledgements We acknowledge the contribution of the  WHO Guidelines Development Group for Screening and Treatment to Prevent Cervical Cancer , which helped formulate inputs to the modeled evaluation and provided critical feedback on our results. The development of the Guidelines Development Group was done in accordance with the WHO's Handbook for Guideline Development (https://www. who. int/publications/i/item/9789241548960), which includes a requirement for representation from all WHO regions that will be impacted by the guidelines. The Guidelines Development Group included scientists, healthcare providers, implementers, ministry of health representatives, systematic reviewers, program implementation experts, survivors and representatives from special interest groups and civil society and also included members from all five WHO regions (AFRO,  SEARO ,  WPRO , EURO and  EMRO) , many from LMICs.
The new Sixth Assessment Report (AR6;  Adler et al. , 2022)  undoubtedly shows that the total aerosol ERF is negative. Compared to previous assessments, the ERF due to aerosol-cloud interactions has increased, but the ERF due to aerosol-radiation interactions has decreased. The total aerosol ERF of 1750-2019 is less certain with a magnitude of -1. 1 W m -2 , mainly due to recent changes in emissions. For this reason, it is of paramount importance to quantitatively assess how the optical and microphysical properties have changed in the past decades. The uncertainties inherent in current aerosol emission databases are particularly pronounced for absorbing aerosols such as biomass burning and dust, which is a focus of the present research. Some literature  (Cohen and Wang, 2014; Qin et al. , 2023)  suggests that the emission values of these absorbing aerosols  (Wang et al.