dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatiotemporal characteristics of features We also analyzed which spatiotemporal features were selected by the decoder for the MATCHED/MISMATCHED decoding in the 96-ms time period after GVS start. The decoder we used is designed to minimize the selected features. Therefore, the selected features may not represent the entirety of brain activity related to prediction errors. On the other hand, the decoder-selected features (especially ones common between participants) are robust to false positives, and hence, we On applying the GVS in the front-back configuration (green data), the decoding performance decreased. Decoding for cue direction, using either all the data since the cue (red data) or data since the GVS start (pink), did not show performance above chance. Decoding performance was similarly low using frequency features between 0.
We did try the decoding using support vector machines [Matlab Support Vector Machine (SVM) toolbox] and iterative SLR (iSLR)  (42) , both of which though gave similar decoding performances. Finally, because our proposed method requires a sensory stimulation when the participant is thinking about a motor action (turning his or her body in our current experiment), an obvious question in this scenario is how to know when a participant is thinking about an action. This in fact is a common question in all BCI setups. Our preliminary investigation shows that our proposed methodology can help both detect whether a participant wants to make a movement (blue plot in Fig. 3 ) and detect which direction he or she wants to turn (black plot in Fig. 3 ). In conclusion, we proposed a new methodology for motor intention decoding, that of using a subliminal sensory stimulator with EEG, using a random stimulation and decoding the prediction error between an intended action and the subliminal sensory stimulation.
Materials The experiments were conducted in a noise-insulated room. We used a commercial EEG recording system (ActiveTwo amplifier system; 64 active sensors, Biosemi) for recoding the brain activity at 512 Hz. A custom-made wireless galvanic vestibular stimulator  (25) (26) (27)  was used in parallel to the EEG recording. Participants were presented with audio cues using stereo speakers to instruct what they should think/imagine (please see Data collection section). The experiment cue presentation and synchronization between the cue presentation, the GVS system, and the EEG recordings were achieved using a Matlab-based program. Protocol Our experiment lasted around 2 hours in total, including time for GVS electrode placement, EEG electrode setup, participant instruction and familiarization, data collection, and subsequent cleanup.
Data collection EEG data were recorded from each participant over six sessions, each lasting about 6 min. Each session consisted of 50 trials, each with a timeline, as shown in Fig. 1C. After an initial rest of 10 s, each trial started with a high-pitched (around 1000 Hz) audio cue (which lasted 1 s) that was presented on either of the two speakers placed on the left or right side of the participant. The participants were instructed to "think about wanting to turn toward the speaker and imagine the feeling corresponding to your body turning accordingly. " This instruction was repeated before every session. To help the participants understand this instruction, a short demonstration was performed in which the participants were asked to close their eyes and feel their body revolving as the experimenter turned the revolving chair (with the participant in it) left and right alternatively.
These trials were used to evaluate if the GVS could help us decode whether the participant is thinking about turning (either left or right). We labeled the CRGR and CLGL trials as "MATCHED" trials, and the CRGL and CLGR trials as "MIS-MATCHED" trials. The cue and GVS timings were optimized with preliminary experiments to minimize experiment time while allowing sufficient time for the participant to start thinking after each cue. The time between the cue and GVS was purposely kept long (>2 s) to enable us to compare the performance of decoding the cue to the decoding of the prediction error. The GVS profile was also tuned with preliminary experiments. Note that the 1-s GVS profile is still not optimal and, as will be shown in the data later, 100 ms of GVS is enough to get appreciable decoding accuracies.
EEG data processing After the preliminary analysis, we chose temporal features from raw EEG data from each of the 64 channels as features. We checked the decoding performances with three decoding algorithms: a recent popular SLR algorithm  (28) , SVM (Matlab toolbox), and the iSLR algorithm  (42). The results were found to be equally good with the three algorithms. We will discuss the SLR results in detail here. The EEG data from the 64 channels of each participant trial were aligned to either of the two alignment points: the cue timing or GVS start. The EEG data were collected in different time periods relative to the alignment point. After removal of the baseline, the time course of the absolute EEG signal from each of the EEG channels (collected at 512 Hz) was used for the decoding. For example, in each trial for decoding data in a 96-ms time period relative to GVS (plotted in black at x = 96 ms in Fig.
Data plots Decoding was performed for every time period for every participant. The across-participant decoder performance was plotted as box plots in Fig. 2. The red box plot at any abscissa point shows the decoding performance (with the cue direction as label) with EEG features from the time period between the cue and the abscissa point. The black and pink box plots at any abscissa point are decoding performances, with MATCH/MISMATCH and cue direction labels, respectively, but using data from time periods between the GVS start time (which is labeled as zero in Fig. 2 ) and the abscissa point. The inset histograms show the participant ensemble decoding performance in the 20 × 7 participants = 140 tests, with data from each time period. The data from each participant are shown in a different color.
Acknowledgments Funding: G. G. was partially supported by the  Japan Science and Technology Agency (JST) Exploratory Research for Advanced Technology (ERATO), Japan  (grant number  JPMJER1701 ). Y. K. and Y. A. were supported by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  grants  17H05903  and  26112004. Part of this work was supported by  JSPS KAKENHI  (grant number  15K01849 ) and  JST PRESTO (Precursory Research for Embryonic Science and Technology)  (grant number  JPMJPR17JA ). Author contributions: G. G. developed the concept. G. G. , Y. K. , H. A. , and E. Y. developed the experimental design. G. G. , K. N. , S. S. , and A. M. T. collected the data. G. G. , N. K. , N. Y. , and Y. K. analyzed the data. G. G. , N. Y. , and Y. K. wrote the paper.
Anemia is a common complication for patients with chronic kidney disease (CKD) and is associated with cardiovascular comorbidities and reduced quality of life. The incidence of anemia increases as kidney function declines and affects approximately 32% of Japanese patients with stage 3-5 CKD. This review examined the current literature on anemia in CKD patients in Japan to provide an overview of the burden of anemia in CKD. Medline, Embase, and Igaku Chuo Zasshi databases were searched to identify relevant manuscripts and abstracts published from 2004 onward. The population included CKD patients with anemia, and the outcomes of interest were epidemiology, economic, humanistic, and treatment patterns. Observational studies, database analysis, and economic evaluation studies were included in the analysis.
Information sources The following databases were searched to identify relevant studies: Medline (Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MED-LINE Daily and Ovid MEDLINE, Ovid MEDLINE 1946 to Present), Embase (via Ovid 1974 to Present), and Igaku Chuo Zasshi (ICHUSHI: Japan's largest medical-literature database established in 1903). Databases were searched for references dated from 2004 onward to align with the availability of the first published JSDT Guideline for Renal Anemia in Chronic Hemodialysis (HD); the date of the last search was January 24, 2017.
Eligibility The search was carried out according to a predefined protocol with key words and queries based on the PICO(S) criteria (P: population, I: intervention, C: comparator, and O: outcomes). The population included CKD patients with anemia, with no limitations on age, stage of CKD, or dialysis status. No specific criteria for intervention or for comparator were applied. Outcomes of interest included epidemiology, economic, humanistic, and treatment patterns. Study design criteria included observational studies, database analyses, and economic evaluations (except modelling analyses that were not primary studies). No language restriction was applied. Details of the selection criteria are reported in Table  1.
Study selection After all the identified references were imported into a joint database and duplicate references were removed, a review of titles and abstracts was conducted by two independent reviewers to identify publications that met the inclusion criteria. All references not excluded based on title or abstract had the full text reviewed by two independent reviewers. Titles, abstracts, and all full-text articles identified for review could be excluded for any of the following reasons: "not relevant population"; "not relevant country/setting"; "not relevant treatment (except erythropoiesis stimulating agent [ESA])"; "not relevant outcome"; "not relevant study design"; "not primary sources (all types of review articles and modelling study)"; "duplicate. ".
Data extraction process Data extraction was conducted using Excel software within four separate data grid sheets containing data specific to the given purpose. Sheets storing information about the publication and study were also developed. Information concerning each publication and study included ID number, first author and year of publication, language of publication, type of publication, title, sponsor, objective, study design, study location, follow-up period, enrollment, population, inclusion/exclusion criteria, details of study treatment, CKD classification, results availability (epidemiology, economic, humanistic, treatment patterns), key conclusions, limitations, and patient characteristics (sample size, mean age [SD], mean Hb [SD], mean eGFR [SD], sex, treatment with ESA, dialysis, CKD stage, cardiovascular disease, and diabetes).
Data planned within the purpose included: epidemiology data (outcomes [mortality, incidence and prevalence of anemia in CKD patients]) and results; economic burden data (outcomes [cost, dosage, medical resource use, non-medical resource use]) and results; humanistic data (outcomes and results); and treatment patterns data (outcomes [ESA responsiveness and dialysis duration]) and results. For all studies, one reviewer completed a quality assessment as recommended by the guidance from the Centre for Reviews and Dissemination from the University of York  (12).
Study selection The literature search in the selected databases resulted in the retrieval of 1148 references (Fig. 1 ); three additional references were identified through a literature review publication (n = 1) and a hand search (n = 2). After 119 were identified as duplicates and removed, the remaining 1032 references were included in the title and abstract screening. A total of 940 records were excluded based on selection criteria, and 92 were selected for full-text analysis. The final analysis comprised 50 references (full-text articles, n = 47; conference abstracts, n = 3). Epidemiological outcomes were reported in 25 records, economic outcomes in 37, humanistic outcomes in one, and treatment patterns outcomes in 26.
Study design The most frequent study design among the identified studies was retrospective observational study (n = 22), followed by prospective observational study (n = 17), cross-sectional study (n = 10), and database study (n = 1). Economic outcome was the most frequently evaluated outcome (n = 19) among retrospective observational studies, whereas epidemiological outcome (n = 14) was the most frequent among prospective observational studies. Both epidemiological (n = 6) and economic outcomes (n = 6) were the most frequently evaluated types of outcome among cross-sectional studies. The only database study identified reported prevalence of anemia in CKD patients. Table  3  reports the number of studies identified for each study design type and outcome. More than half of the studies included a single center (n = 29), whereas a third were multicentric (n = 16).
22 per 100 person-years) than those who were iron deficient but not anemic (5. 13 per 100 person-years)  (15). One study reported that the rate of cardiovascular-related mortality among HD patients who were treated with rHuEPO was 10. 06%, and that the mortality rate did not significantly differ among those who were not treated with rHuEPO  (34). Another study examined mortality data of anemic patients whose dialysis status was not reported, and evaluated in-hospital death among patients treated with percutaneous coronary intervention for acute myocardial infarction. The authors found that the risk of cardiac-related death was higher in patients with anemia compared with those without anemia (odds ratio, 2. 085)  (40). Cardiovascular and/or cerebrovascular events associated with anemia were reported in four studies.
0% in Korea (4) and 51. 5% in China  (64) ; prevalence rates in the US had a greater variation and were reported to be between 15. 4% and 46%  (5, 65). Data on mortality were reported in 10 studies, most of which did not indicate the specific CKD stage. Among HD patients, higher mortality was associated with ESA resistance, lower Hb levels, and elevated or fluctuating ferritin levels among patients treated with ESA, and with lower Hb and Hct levels among patients not treated with ESA. This is consistent with conclusions from a 2013 review article on mortality risk associated with anemia management in CKD. The authors noted that poor response to ESA treatment was a substantial challenge in anemia management and increased the risk of poor outcomes  (66). Information on cardiovascular-related mortality was limited to only one study  (34).
A paucity of humanistic data was also reported in a similar burden of illness review of US studies  (67). Of 16 articles that reported QoL data, only one directly measured QoL associated with anemia in patients with CKD not on dialysis using the Short Form-36. The authors noted that a need exists for a health-related QoL instrument that is specific to anemia. Several studies reported treatment patterns that included ESA responsiveness, dialysis patterns, and supplementary treatment patterns. DA and epoetin beta were the most evaluated ESA for responsiveness, while responsiveness for epoetin alfa was reported in only two studies. ESA responsiveness was generally low, and varied widely depending on the specific ESA treatment, dialysis status, and definition of responsiveness; the highest rate of patients achieving Hb target was 62.
g. excessive, high, poor, etc. ); time to dialysis; dialysis duration; IV iron use; use of iron replacement therapy; use of "ferrous citrate" Study Design • Observational studies • Database analysis • Economic evaluation Not the study type of interest (e. g. general review  † , SLR  † , letter, commentary, editorials, notes, opinions, case studies, PK studies, CTs); modelling study  † ; RCTs.
Statically analysis All statistical analyses were performed using the Graph Pad Prism software, version 6. Data were analyzed using one-way analysis of variance (ANOVA) followed by Tukey's HSD as the post hoc test and the two-way ANOVA test followed by the post hoc Bonferroni test was also performed. Results were presented as mean ± S. D. of triplicate samples. The minimal level of significance chosen was p < 0. 001.
Background Chronic lung allograft dysfunction (CLAD) is the major factor limiting long term survival following lung transplantation (LTx). The International Society of Heart and Lung Transplantation (ISHLT) evaluates the prevalence of bronchiolitis obliterans syndrome (BOS) at 50% at 5 years post transplantation  [1]. Previously known as "chronic rejection", the term CLAD was coined in 2010 in order to encompass two distinct identities: BOS and restrictive allograft syndrome (RAS)  [2, 3]. The underlying physiopathological mechanisms leading to the development of CLAD are yet to be fully understood  [4]. However, several risk factors have been identified that have a significant association with the development of BOS, among which: primary graft dysfunction, acute cellular or humoral rejection, lymphocytic bronchiolitis, gastro-esophageal reflux, viral, fungal or bacterial pneumonitis or bronchial infection  [5].
Sample acquisition and analysis Sputum samples were collected when clinically indicated. BAL and BA were obtained during a bronchoscopy, following current guidelines  [25]. Samples were cultured on 4 different media: MacConkey agar (Biomérieux, Marcy l'Etoile, France), chocolate agar (Biomérieux), 5% sheep blood enriched Columbia agar with nalidix acid and colistin (ANC Columbia) (Biomérieux) and Cepacia agar (BD, Franklin Lakes, New Jersey, United States). Bacterial colonies were identified using MALDI-TOF MS as previously described  [29]. Antimicrobial susceptibility testing was performed according the European Committee on Antimicrobial Susceptibility Testing (EUCAST) recommendation. MALDI-TOF was performed on all samples at the time of collection. Bacterial spectra obtained by MALDI-TOF MS for each strain were recovered, classified by species and used to build specific dendrogram (Bruker Biotyper 3 software) for each species.
Selection signatures We looked for genomic signatures of positive selection in the two lines using the FLK  [15]  and hapFLK  [16]  methods. These methods consider genotype data from multiple populations and detect regions where genetic differentiation between these populations is too large to result from a neutral evolution model. To do so, they proceed in three steps. First, they estimate the inbreeding coefficient in the different sampled populations, which quantifies the amount of drift that has been accumulated in these populations since their divergence from a common ancestral population. They do it based on genome-wide data, assuming that most loci genomewide have evolved neutrally, so that the few loci that have evolved under selection have little influence on this estimation. Second, for each genotyped locus, they compute a p-value measuring how likely it is to reach the level of genetic differentiation observed at this locus, under a pure drift model with the inbreeding values estimated previously.
Statistical analyses Detection of selection signatures We looked for genomic signatures of positive selection using the FLK  [15]  and hapFLK  [16]  methods, both implemented in the hapFLK software (https://forge-dga. jouy. inra. fr/projects/hapflk). These methods consider genotype data from multiple populations and detect regions where genetic differentiation between these populations is not consistent with a neutral evolution model. First, based on the matrix of observed genome-wide pairwise Reynold's distances between populations, they estimate a population tree, using a Neighbour-Joining algorithm, and summarize this tree by a kinship matrix quantifying the amount of drift accumulated in each population since their divergence from a common ancestral population. Then, they scan the genome for loci where genetic differentiation between populations is significantly larger than expected under this pure drift model.
At each generation (G1 to G5), 3 populations were compared: the pHu + line, the pHuline, and the G0 population. G0 was defined as outgroup when building the population tree and computing the kinship matrix (option -outgroup). Data from G0 were also used when computing genetic differentiation at each locus (option -keep-outgroup). As a result, estimated allele frequencies in the ancestral population were always very close to the allele frequencies observed in G0 (p0), so the allele frequency differences computed within FLK (the terms (pi-p)^2 mentioned above) were almost equal to the allele frequency differences (pi-p0)^2 observed along each selected line. HapFLK makes use of the local clustering approach of Scheet and Stephens  [30]  to estimate haplotype diversity. This approach assumes that for each position on the genome, local haplotypes can be divided into K main clusters, where each cluster may include several similar haplotypes.
We inferred haplotype clusters using 50 Expectation-Maximisation (EM) runs (option -nfit). Following Fariello et al. [16, 20] , we assumed that hapFLK statistic was normally distributed under neutral evolution, and estimated the parameters of this normal distribution by fitting genome wide hapFLK values with a robust linear regression approach. HapFLK p-values at each SNP were computed from this estimated distribution. To identify selected regions, we used a q-value threshold of 0. 05, therefore controlling false discovery rate at the 5% level  [31]. For FLK analyses, p-values were directly obtained from the hapFLK software (they can easily be computed because the distribution of the FLK statistics under neutral evolution was shown to follow a chi-squared distribution with known degrees of freedom).
Computation of linkage disequilibrium The linkage disequilibrium measure r2 was computed for all SNP pairs distant by less than 500 kb and with minor allele frequency greater than 0. 1, using the -r2 command of the Plink software, version v1. 90b3. 34 (https://www. cog-genomics. org/plink2). This computation was performed using genotyped animals from generation 0. For a given category of regions (for instance those listed in Table  1 ), all SNP pairs belonging to this category were collected and the average r2 among these pairs was estimated.
Acknowledgements We thank  LABOGENA laboratory  for genotyping analyses. We thank the staff of the poultry breeding facilities (PEAT,  INRA ,  37380 ,  Nouzilly, France)  and of the  Avian Research Unit (URA, INRA ,  37380 ,  Nouzilly, France ) for their technical assistance. We thank  Dr. Nabeel Alnahhas  for his helpful suggestions on the manuscript.
Determination of the type 1 pilus rod structure To characterize FimA polymers that form the type 1 pilus rod, we solved the cryo-EM structure of native type 1 pili appearing in a preparation of recombinantly expressed Type IV pili (T4P) from the E. coli K12 strain BW25113. As shown in Figure  1A , three types of filaments could be separated by eye: T4P, flagellar filaments, and a third class that were thicker and more rigid than T4P but thinner than the flagellar filaments. Sequencing the fimA PCR product showed that the encoded amino acid sequence was identical to the FimA protein from BW25113 (GenBank AIN34588. 1) and MG1655 (GenBank NP_418734. 1). There was no possibility of cross-contamination of the FimA and T4P filament images, as each has a very different helical symmetry. Averaged power spectra from FimA filaments (Figure  1 -figure supplement 1) showed no trace of the Type IV pilus spectrum (Figure  1 figure supplement 1).