dataesr/scientific-paragraphs-categorization huggingface dataset:
algorithm. Datasets 2 and 3 were obtained from archival accelerometers to simulate the onboard algorithm in desktop PC simulations of the onboard algorithm and traditional analysis methods (Aoki et al. , 2011). Datasets 4 and 5 were obtained from field deployments of the newly developed BD-SRDL running the onboard algorithm on northern elephant seals, allowing us to evaluate onboard glide selection and body density estimation using archived and satellite-transmitted data. Fieldwork on northern elephant seals was conducted at the University of California Natural Reserve System's Año Nuevo Reserve under NMFS (National Marine Fisheries Service) permit No. 14636 and 19,108 issued to D. P. Costa and was approved by the Institutional Animal Care and Use Committee (IACUC) at UCSC. Fieldwork on southern elephant seals was conducted at Kerguelen Island, financially and logistically supported by the IPEV (Institut Paul Emile Victor) under the Antarctic research program 109 (C.
Barbraud) and 1201 (C. Gilbert & C. Guinet) and CNES-TOSCA as part of the SNO-MEMO. 2. 4. 1 | Field deployments of BD-SRDLFor the final evaluation of the onboard algorithm, archived and satellite-transmitted data (datasets 4 and 5; n = 7) were obtained from the BD-SRDLs attached to free-ranging adult female northern elephant seals (Table1). Fieldwork was conducted at Año Nuevo State Park, CA, USA (37°50′N, 122°16′W) during the seals' breeding and moulting season. The 7-month post-moulting foraging migrations occurred from June to January in 2020-2021 (n = 2), and the 2-month post-breeding migrations occurred from February to May in 2021 (n = 5). Each seal was chemically immobilized using a standard protocol (Robinson et al. , 2012)  to attach the data loggers and to collect body mass and morphometric measurements at deployment.
minal speed using three criteria based on Aoki et al. (2011) : a prolonged descent glide (i) has no depths less than 100 m (to minimize the effect of gases in the lungs on buoyancy), (ii) is longer than 40 s, (iii) has maximum swim speed (i. e. terminal speed) and maximum absolute pitch angle within 20 s prior to the end of the prolonged glide (i. e. reached a terminal speed around the end of the prolonged glide). The calculation of terminal speed was conducted on dataset 2, including 1572.
), enabling us to estimate C d,f based on the correlation between empirical terminal speed and theoretical terminal speed. Here, C d,f was simulated with the range of 0. 01-1 to cover the reported values (0. 17-0. 26) estimated in the previous study (Aoki et al. , 2011). The best-fit C d,f was determined per individual of the dataset 2 (Figure3a) by the following six steps: 1. Compile a list of all prolonged glides: list all detected prolonged glides and empirical terminal speed based on the method in the last section, 2. Select a value of C d,f : select and fix a value of C d,f (ranging from 0. 01 to 0. 1, by 0. 01 increments) for the next steps 3 and 4, 3. Simulate ρ seal using Equation (2) with a focal C d,f : at a prolonged glide, identify all 5-s-glides within 4-h windows centred on a start time of the prolonged glide, and calculate averaged ρ seal from the 5-s-glides using the key Equation (2), 4.
Objective: To assess and compare the effectiveness of different exercise modalities in reducing cancer-related fatigue (CRF) in patients with cancer undergoing chemotherapy. Exercise intensities for selected exercise types were also compared. Data Sources: We conducted a search in MEDLINE, Embase, CINAHL, Scopus, SPORTDiscus, and Web of Science from inception to October 15, 2021. Study Selection: Randomized controlled trials concerning the effectiveness of exercise modalities on CRF in patients with cancer undergoing chemotherapy were included. Data Extraction: Study characteristics were extracted using a structured protocol. Methodological quality was assessed employing the PEDro scale and risk of bias was assessed using the Revised Cochrane Risk of Bias Tool for Randomized Trials. The certainty of evidence was assessed based on Grading of Recommendations, Assessment, Development and Evaluation.
Search strategy We searched for scientific articles in the MEDLINE (PubMed), Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, SPORTDiscus, and Web of Science databases from inception to October 15, 2021. Databases were reviewed prior to statistical analysis to identify additional articles published up to January 15, 2022. We manually checked the reference sections of included studies, checked studies included in reviews related to the topic and contacted authors for additional information where necessary. We also use the Connected papers website, which displays articles according to their similarity (based on co-citation and bibliographic linking). 22  The search was adapted and performed in Google Scholar. The search strategy is shown in Supplemental Appendix A.
Selection criteria The identified references were exported to the Rayyan QCRI software, used to remove duplicates, and perform the screening process in 2 phases. 23  In the first phase, we assessed the relevance of the studies in relation to the study questions and objectives using information from the study title, abstract, and keywords. The full text was reviewed in the absence of consensus or sufficient information. In the second phase, we assessed the full text of each study for compliance with the inclusion criteria. The article selection process was conducted by 2 independent researchers (A. H. G. and C. V. R. ) and differences were resolved by consensus moderated by a third researcher (F. C. M. ). 24  Data extraction and efficacy measures Two independent researchers (A. H. G. and M.
26  Statistical results related to the effect of exercise training on CRF were extracted from the post-intervention and/or follow-up assessment. All the numeric data were converted to mean and SD. If necessary, CI and SE were converted in SD using the formulas recommended by the Cochrane Handbook for Systematic Reviews of Interventions version 6. 2: SD=x(N)*(upper limitlower limit)/3. 92 and SD=x(N)*SE, respectively. 27  The Plot Digitizer software was employed to estimate outcome results when only figures were available (http://plotdigitizer. sourceforge. net). Studies that referred to the same sample of patients with cancer were considered only once, in order to reduce bias in the results.
Data synthesis and statistical analysis The short-term effect of different exercise modalities on CRF was analyzed with a frequentist network meta-analysis (CVR) using RStudio software version 1. 4. 1717, which is based on the R software version 4. 1. 1  [36] [37] [38]  and different packages. [38] [39] [40] [41] [42] [43] [44] [45]  R scripts and raw data used for the analysis are available in the Open Science Framework repository (https://osf. io/eyfnq/?view_only=7ca5e b0a4f9c4a40888cbe084f8fd2a0). The follow-up results were analyzed and synthesized qualitatively. Network meta-analysis allows to infer an estimation of a comparison's effect size based on direct evidence from observed comparisons in the different primary studies and indirect evidence from the inference of comparisons that have not been actually studied (The unfamiliar reader should consult Harrer et al's guide  38  to understand our analyses).
Cancer-related fatigue network meta-analysis Forty-two studies were included in the short-term network metaanalysis for a total of 18 exercise interventions and 58 comparisons. [55] [56] [57] 60, [61] [62] [63] [64] [65] [66] [67] [69] [70] [71] [72] [73] [74] [75] [76] 57, [77] [78] [79] [80] [81] [82] [83] [84] [86] [87] [88] [89] [90] [91] [92] [93] [94] [95] [98] [99] [100] [101]  The study by Yeh and Chung, 2016 was excluded from the network meta-analysis because it had a statistically significant difference at baseline,  97  so, the post-intervention is biased. It is not recommended to calculate the within-group SMD without individual participants' data  102.
Donors can therefore be selected based on objective phenotypic measurements, rather than a dichotomous diagnosis. Where possible, non-psychiatric medical history should also be collected. It has now been demonstrated that hiPSCs show epigenetic memory to some extent, and morbidity as well as medical treatment could therefore potentially alter cellular morphology and function  (Bar-Nur et al. , 2011; Efrat, 2020). However, it should be noted that with the development of more accurate models of epigenetic age, there is an Box 1 Summary of Key Recommendations 1) Where possible, generation/use of patient-specific hiPSC lines with detailed demographic, clinical, medical, diagnostic and genetic data (incl. polygenic risk scores), along with familial history and non-clinical measures to provide context to sources of variation between donor lines.
The GDPR also includes the principle of data minimisation meaning that personal data shall be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed. Biological samples and data are collected with the donor's consent for medical research and product development for healthcare and health benefiting purposes in Biobanks. When processing of personal data is based on consent the donor has the right to revoke it at any time. Consent must be obtained before information about a data subject can be collected and processed. For donors of tissue that will be used to generate hiPSCs, consent must be informed and explicit for donation of the biological sample for reprogramming and for the collection and processing of personal data and genetic material that will be used to characterise the cell line.
Improving data reproducibility and robustness The novelty and inherently heterogeneous nature of hiPSC technology has complicated basic experimental planning, and there is currently a lack of consensus regarding the minimum standard required for robust hiPSC study design, particularly regarding factors such as the minimum number of biological replicates. Current practice is to use multiple clones from the same donor, which is also entrenched into the guidelines of several journals (e. g. Stem Cell Reports)  (McNeill et al. , 2020). However, the inclusion of more than one clone per donor in hiPSC transcriptomic datasets has been shown to result in a significant increase in the detection of spurious differentially expressed genes, suggesting that the hiPSC field may currently be reporting a high number of false positive results (Germain and Testa, 2017).
These findings may highlight a need to widen access to larger numbers of hiPSC lines, for example through centralised resources such as the previously mentioned Biobanks/stem cell repositories. An alternative approach has recently been used while studying 22q11. 2 deletion syndrome  (Nehme et al. , 2022). In this study, the authors performed a pilot experiment using 2 hiPSC lines from either carriers of the 22q11. 2 chromosomal deletion, or non-carriers. Based on RNA-sequencing data generated from all 4 lines at 3 distinct time points of differentiation, the authors performed a power calculation to determine that a sample size of >20 control and donor lines were needed in order to detect transcriptomic changes  (Nehme et al. , 2022). This approach highlights how a data driven approach could be utilised in order to provide insight into what would be an appropriately power study for a given phenotype or assay.
This is a mixed-models approach which treats the individual as a random-effect variable. The approach was found to reduce the false discovery rate whilst offering increased sensitivity, leading the authors to recommend the use of this model (with a more stringent threshold) when analysing datasets including more than one clone per donor. However, it should be noted that the use of only one clone per donor still showed superior specificity. Secondly,  Volpato et al. (2018)  attempted to control for noise using a factor-based analysis method called remove unwanted variation (RUV)  (Volpato et al. , 2018) , which aims to remove technical variation whilst retaining variation associated with a biological covariate of interest  (Risso et al. , 2014). When applied to transcriptomic and proteomic datasets, two individual hiPSC lines could be separated, whereas previously they could not.
Publishing edited hiPSCs as laboratory resources and in stem cell repositories could largely contribute to the overall quality of edited hiPSCs and ensure that they follow internationally recognized standards. Lastly, with regards to controlling noise caused by technical variance, we recommend that large datasets are adjusted using RUV to help identify biological signals. For a summary of recommendations regarding minimum number of replicates and use of appropriate statistical methods, please see Table  1  and Table 2  respectively. 4. Best practice when performing neuropharmacological studies relevant for neuropsychiatric disorders hiPSC-derived neural cellswe used this term to encompass hiPSCderived neurons (excitatory and inhibitory) and glial cells (astrocytes, microglia and oligodendrocytes), grown either in 2D or 3Doffer a translationally relevant model for assessing the effects of pharmacological agents.
, 2018; Collo et al. , 2018). These effects were observed in vitro at doses estimated to be in the same range as the clinically effective concentration, further suggesting a potential translational value. Moreover, this study provided information regarding the possible effective therapeutic dose to be used in the human studies with HNK. These studies, together with other preclinical data, supported the rationale for the current clinical development of HNK (https://clinicaltrials. gov/ct2/show/NCT04711005). It is however important to note that even though phenotypic and molecular changes by a compound of interest can overlap with gold standard drugs, this is not necessarily the case for their exact pharmacodynamics and pharmacokinetics. These characteristics can greatly affect the safety, efficacy and thereby also the translational relevance of the compound of interest.
Primary studies are possible to perform using hiPSCs and can provide a relatively quick overview and selection of potential effective therapeutic agents in absence of already available therapeutic agents with a desired effect. However the interpretation of the huge datasets would be challenging. Differentiating neurons can react to new compound treatment in many ways simultaneously (e. g. changes in neurite outgrowth combined with electrophysiological activity), and these changes might have opposite directional effects. Therefore, net effects would be difficult to model. However, the development of high throughput screening assays for intermediate cell types could be a potential solution. For example,  Readhead et al. (2018)  employed the use of hiPSC-derived neuronal progenitor cells from SCZ patients to perform high throughput pharmacological screening, thereby avoiding the methodological hurdles of neuronal differentiation, and were able to identify drugs that could reverse post-mortem SZ-associated transcriptomic signatures.
SKS was supported by an  IZKF Würzburg  grant  N440. RVM was supported by a  NARSAD Young Investigator grant  from the  Brain and Behaviour Research Foundation  (# 30587 ) and  Postdoc Plus  funding from the  Graduate School of Life Sciences, University of Würzburg. GCZ was supported by a grant from the  DFG  (Project No. 413657723 ;  Clinician Scientist-Program  UNION CVD). FR was supported by a grant from the  DFG  ( Clinician Scientist Program UNION-CVD ). JR was supported by the  TKP2021-EGA-25   Thematic Excellence Grant  of the  Hungarian Government. This work was also supported by the  Netherlands Organ-on-Chip Initiative (NOCI) , an  NWO Gravitation  project funded by the  Ministry of Education, Culture and Science of the government of the Netherlands  ( 024. 003. 001 ; to F. M. S.
Results Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described (APOE, TMEM106B, and CHI3L1). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers.
Specifically, we identified associations between the injury/inflammation component (PC3) and rare variants in IFFO1, DTNB, NLRC3, and SLC22A10, as well as between the non-AD synaptic functioning component (PC5) and rare variants in GABBR2 and CASZ  [3]. Interestingly, rare variant associations with AD risk were simultaneously reported for the DTNBlocus in an independent project utilizing whole-genome sequencing in AD families and case-control datasets  [11]. In this study, we aimed to extend the previous, rarevariant analyses to investigate the role of common variants on the PCA-defined CSF biomarker profiles. While previous GWAS in the field have screened for commonvariant associations with single biomarkers  [9, [12] [13] [14] [15] , to our knowledge no GWAS combining these CSF biomarkers in a multivariate framework has been performed to date.
More details on the recruitment and phenotype ascertainment protocols used in the EMIF-AD dataset can be found in Bos et al. [4]. To increase the generalizability of effect estimates and to increase power to detect new associations, we performed all analyses jointly with equivalent CSF biomarker and SNP genotype data from the Alzheimer's Disease Neuroimaging Initiative (ADNI)  [5]. Data used in the preparation of this article were obtained from adni. loni. usc. edu. ADNI was launched in 2003 as a publicprivate partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD.
The current study utilized two participant selection paradigms for analysis: first, we selected participants for whom observations for at least 4 out of the 5 biomarkers were available. In total, this yielded 1158 participants to construct and examine the biomarker PC scores (Additional file 1: Table  S1 ). Second, we only included participants with available SNP array data, who were unrelated and of European ancestry. This reduced the sample size to 973 participants (Additional file 1: Table  S2 , see also  Hong et al. for detailed selection methods  [13] ). Overall, both EMIF-AD and ADNI were comparable datasets of elderly participants, with a mean age at ascertainment of 69 and 75 years, respectively (Additional file 1: Table  S1 ). The distributions of diagnostic status were similar in both datasets as well, with approximately half of the sample diagnosed with MCI, while 25% presented either no cognitive impairment or with a diagnosis of AD (Additional file 1: Table  S1 ).
Genotyping, imputation, and quality control (QC) SNP genotypes were determined using the Infinium Global Screening Array (GSA; Illumina, Inc. , USA) at the Institute of Clinical Molecular Biology (UKSH, Campus-Kiel) in EMIF-AD and using Illumina's Omni 2. 5 M or Human610-Quad arrays in ADNI. Autosomal SNPs in both GWAS datasets were processed with the same computational workflow  [13] , including the imputation of untyped variants with MiniMac 3 using the HRC 1. 1 reference panel  [24]. Here, we only analyzed common SNPs (MAF ≥ 0. 01 per study) with sufficient imputation quality (R 2 > 0. 30) and SNPs within HWE (p < 5 × 10 -6 ). To check for cryptic relatedness, we used the KING-robust kinship estimator implemented in PLINK (v2. 0) with a cutoff of 0. 025, which excludes family relationships down to the fourth degree.
Statistical analysis CSF biomarker PCA, sex differences, and AD associations The analysis work-flow is summarized in Additional file 2: Fig. S1. First, we computed five PCs across all participants with sufficient biomarker information. PCs were defined as described previously  [3]  and assigned to specific functional domains, as described in the introduction. The PCA was performed on the phenotype level and PCs were constructed independent of genotype information. Briefly, we first applied a rank-based inverse normal transformation within both studies to decrease extreme skewness of the observed biomarker levels and to z-score standardize their scale across studies  [29]. We used the missMDA package to determine the optimum number of components and account for missing data  [30]. Specifically, we applied leave-one-out cross-validation, removing one observation at a time and predicting it by a PCA model fitted to the rest of the dataset.
The model resulting in the smallest mean square error of prediction contained five components (PC1-PC5). We then imputed missing values to avoid excessive sample size losses and potential participation biases. This was achieved using a regularized iterative PCA method with five components, as implemented in missMDA. Finally, we performed a PCA with varimax rotation (an orthogonal rotation) and extracted five PC scores with the psych package  [31]. All analyses were performed in R 4. 0. 3  [32]  We applied the same PC loadings to the Knight-ADRC for replication purposes of results related to PC4 and PC5. We have not previously explored to which degree the component structure differs between sexes, or whether the resulting PCs show sex-dependent associations with dementia symptoms. We first repeated PCA in both males and females and compared loadings.
GWAS and meta-analyses We performed four sets of GWAS: main-effect GWAS analysis (both sexes), male-only GWAS, female-only GWAS, and sex interaction analyses. Within each analysis group, we performed five GWAS, one for each biomarker PC, separately for EMIF-AD and ADNI. For all GWAS, separate linear regression models were run in PLINK  [35]  to all autosomal and X-linked SNPs passing QC. For the X-chromosomal analyses genotype, dosage for hemizygous males was coded as 2, to reflect the same dosage as homozygous females  [36]. The biomarker PC scores were treated as outcome, imputed SNP dosage (0-2 numbers of effect allele) as main predictor. In addition, we included sex and five PCs reflecting genetic ancestry as additional covariates in the regression models. Analyses of the ADNI dataset were additionally corrected for genotyping array.
Functional characterization and related phenotypes SNPs were annotated to genes using default settings of FUMA, which relies on positional and functional information. Given the broad association signal between the chromosomal region encompassing GRIN2D and non-AD synaptic functioning (PC5), we additionally performed fine-mapping analyses to identify the most likely causal genes at this locus. To this end, we used a TWAS fine-mapping approach  [43]  as implemented in FOCUS 0. 802 (https:// github. com/ bogda nlab/ focus). Briefly, we examined the association between predicted gene expression at the GRIN2D locus with non-AD synaptic functioning (PC5). The GRIN2D locus was defined as encompassing chr19:48382575-48466980 (GRCh38/ hg38), starting 10 kb downstream of KDELR1 and 10 kb upstream of KCNJ14to cover all lead variants within the locus.