dataesr/scientific-paragraphs-categorization huggingface dataset:
Androgen Depletion Upregulates The Phb1 Expression And Promotes Its Nucleus-cytoplasm Translocation To elucidate the mechanism by which PHB1 promotes castration resistance in PCa, we rst determined whether the AR signaling pathway regulates PHB1 expression. Androgen-starved LNCaP cells were stimulated with DHT for indicated times (0\6\12\24\48 h) or concentrations (0\0. 01\0. 1\1\10 nM), respectively. Western blot and qRT-PCR results showed that DHT treatment inhibited the expression of PHB1 in a time/concentration-dependent manner (Fig. 3A ), indicating that PHB1 is an androgen suppressive gene, which is consistent with Bevan's results  [24]. Furthermore, the GSE59986  [37]  dataset analysis revealed that PHB1 mRNA expression in LNCaP cells was upregulated under chronic androgen depletion.
Acknowledgements The authors thank support from the LLB (S. P. ) for beamtime at 4F2. The  Chevreul Institute  ( FR 2638 ),  Region Hauts-de-France , and  FEDER  are acknowledged for funding the X-ray diffractometers and the PPMS magnetometer. Houria Kabbour  is thanked for her in contribution in the preliminary investigation on BFPO. Laurence Burylo  and  Nora Djellal  at the  UCCS  are also acknowledged for their experimental contribution.
Methods The data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). ADNI began in 2004 as a public-private partnership under the leadership of Dr. Michael W. Weiner. The primary goal of ADNI has been to detect AD dementia at the earliest possible stage (pre-dementia) and identify ways to track the disease progression. To that end, data from magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers as well as clinical and neuropsychological assessments have been collected to test if they can be combined to measure the progression of the various stages of the AD continuum. The initial ve-year study (ADNI-1) was extended by two years in 2009 by a Grand Opportunities grant (ADNI-GO), and in 2011 and 2016 by further competitive renewals of the ADNI-1 grant (ADNI-2, and ADNI-3, respectively).
In addition to ADNI general inclusion and group classi cation criteria, we applied for this study two additional speci c criteria. The rst one was to be native English speakers (excluded N = 33). The second criterion was consistency between total AmNART scores and single item-level data on this test, when available, in the ADNI database (excluded N = 52). Of the original 2097 and after all considerations, 2012 participants remained, of which 681 HC, 104 SCD, 290 EMCI, 589 LMCI and 348 AD. Demographics of this nal sample are provided in the result section.
Epsilon square was used as a measure for effect size (ε 2 ; Okada, 2013). Stein's formula was used to calculate adjusted R 2  (Stevens, 2002). To assess the involvement of each psycholinguistic variable extracted from the AmNART (as presented in 2. 2. 1) on irregular word reading, we analyzed single-item accuracy with a generalized logistic mixedeffects model using the lme4 package  (Bates et al. , 2015). This analysis was conducted on a subsample of participants who had single item-level AmNART data available, as opposed to only having total AmNART score available (195 HC, 323 LMCI, 156 AD). Single-item accuracy was predicted by length, lexical frequency, orthographic neighborhood, bigram frequencies by position, age of acquisition, concreteness, number of senses, semantic neighborhood density, number of syllables, number of phonemes and phonological neighborhood as xed effects, with by-item and by-subject random intercepts as random effects.
, 2000. The same can be said for languages that incorporate phonograms or ideograms (e. g. , Chinese, Japanese, Korean or Vietnamese) which could be more context-dependent or invoke greater imageability in reading. Third, item-level analyses were conducted on a subsample of participants for which item-level AmNART data (as opposed to only total AmNART score) was available (195 HC, 323 LMCI, 156 AD). Additionally, 20 words with missing values in age of acquisition, objective lexical frequency, concreteness, and/or phonological neighborhood had to be excluded from item-level analyses. Fourth, the use of the BNT as unique semantic test has certain limits, as picture naming involves distinct cognitive processes that are not limited to semantics. Involved are visual analysis of the picture, recognition of the stimulus as familiar, activation of the semantic representation of the object via the semantic system, a lexical-semantic process which directs selection and retrieval of semantic information in a task appropriate way, modality-independent lexical access to the phonological word form of the object, that is to say the speech sounds used in the word; and the motor programming and articulation required for saying the word (DeLeon et al.
Acknowledgements Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  AbbVie ,  Alzheimer's Association ;  Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc. ;  Biogen ;  Bristol-Myers Squibb Company ;  CereSpir, Inc. ;  Cogstate ;  Eisai Inc. ;  Elan Pharmaceuticals, Inc. ;  Eli Lilly  and  Company ;  EuroImmun ;  F. Hoffmann-La Roche Ltd  and its a liated company  Genentech, Inc. ;  Fujirebio ;  GE Healthcare ;  IXICO Ltd.
Availability of data and materials Data analysed in this study were acquired from the ADNI database (http://adni. loni. usc. edu). All ADNI data are shared without embargo through the Laboratory of Neuro Imaging and Data Archive. Interested scientists may obtain access to ADNI imaging, clinical, genomic, and biomarker data for the purposes of scienti c investigation, teaching, or planning clinical research studies. Access is contingent on adherence to the ADNI Data Use Agreement and the publications' policies. For further information, please refer to the ADNI website (http://adni. loni. usc. edu/data-samples/access-data/).
Here we present hydrological and vegetation paleo-data extracted from 28 sites in West Africa from 5 • S to 19 • N and the past1000/PMIP4 IPSL-CM6A-LR climate model simulations covering the 850-1850 CE period to document the environmental and climatic changes that occurred during the Little Ice Age (LIA). The comparison between paleo-data and model simulations shows a clear contrast between the area spanning the Sahel and the savannah in the north, characterized by widespread drought, and the equatorial sites in the south, where humid conditions prevailed. Particular attention was paid to the Sahel, whose climatic evolution was characterized by a progressive drying trend between 1250 and 1850 CE. Three major features are highlighted: (1) the detection of two early warning signals around 1170 and 1240 CE preceding the onset of the LIA drying trend; (2) a tipping point at 1800-1850 CE characterized by a rainfall drop and an environmental degradation in the Sahel; and (3) a succession of drying events punctuating the LIA, the major one of which was dated to around 1600 CE.
Pollen data -In the open landscapes of the Sahara, Sahel and savannah zones, vegetation changes were of minor amplitude except at sites where gallery forests were previously well developed. It is in the westernmost part of the Sahel that the most profound changes in vegetation cover are recorded: in the Niaye area (Mboro) and in the Senegal River delta (St Louis), the degradation of the landscape originated ca. 1300 CE and accelerated ca. 1600 CE to a maximum reached ca. 1850 CE (Fig. 4f ). A discrete vegetation recovery is then recorded in the 19th century. In contrast, sites from the central Sahel (Oursi and Jikariya) remained relatively stable throughout the last millennium in spite of a slight degradation recorded at Oursi ca. 1050 CE. North of the Sahel (Yoa), the aridification of the desert landscape accelerated from the 19th century onward.
Hydrology Three major regions can be recognized from the paleohydrological records: the Sahel and savannah zones, with a drying trend; the center of the Congo Basin, which exhibits an opposite trend of increasing humidity; and the boundary between the dry and humid domains defined by the equatorial sectors closest to the coast or in mountains, where an alternation of wet and dry phases is recorded. Two paleorecords differ from this general picture: that of Lake Chad, https://doi. org/10. 5194/cp-19-277-2023 Clim. Past, 19, 277-292, 2023 where a period of flooding is recorded ca 1600 CE and that of the Diorom Boumak, where the LIA is entirely characterized by a wet period (Fig. 4 ). As evoked above, the multiple origins of the data and the complex hydrological system of Lake Chad may have introduced a bias into the hydrological record and may explain (at least partly) the difference with the other Sahelian archives.
50 years can be observed between the onset of a drought phase and the response of the vegetation. -The ultimate environmental threshold is recorded ca. 1800 CE (event 6). It resulted in the widespread lowering of lake levels, the massive contribution of dust to the ocean and the irreversible destruction of forest galleries in the western Sahel in response to an abrupt drop in rainfall ca. 1800 CE, already observed by  Carré et al. (2019)   2010 ) that may induce a delay between the climate signal and its effects on ecosystems. The uncertainty associated with the ages, whether it comes from the data or from the modeling, can also play a role by increasing or reducing this response time.
Data availability. Pollen data are available on the African Pollen Database website: https://africanpollendatabase. ipsl. fr (African  Pollen Database, 2023). The other paleo-data are from the literature. The IPSL-CM6A-LR model data and pre-processed model and proxy data sets used in this study are available at https://doi. org/10. 5281/zenodo. 7003853 (Lézine et al. , 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-19-277-2023-supplement.
Paired-end short-read sequence data generated on the Illumina MiSeq was processed using the VSEARCH package  [20]. De-multiplexed paired-end sequences were first passed through cutadapt for primer removal  [21]  and then merged prior to sequence quality filtering, followed by error correction  [22] , chimera checking  [23] , and clustering of sequences to Amplicon sequence variants (ASVs)  [24]. Taxonomic classification of bacterial ASVs was done using the IDTAXA algorithm implemented in the DECIPHER R package against the SILVA SSU r132 training set  [25]. Regression analysis of ASVs and their relationship to δ 15 N was performed using the mixOmics R package  [26]  using Sparse Partial Least Squares (sPLS)  [27]. In the initial analysis we used a stepwise process to first build a basic sPLS model that was further optimised and tuned.
The tuning involves selecting the number of components that best explains the variance in the data and the number of features (ASVs) that maximise the correlation between the predicted and actual components. The number of features was limited to a maximum of 100 ASVs per component. Tuning involved crossvalidation in which the data was randomly split into 10 subgroups to train sub models to predict the accuracy of these models. Selected ASV's were transformed using Centered logratio (CLR) and visualised using ggplot2  [28]. The sequences obtained in this paper have been deposited in the European Nucleotide Archive (https://www. ebi. ac. uk/) under the accession number PRJEB63213.
Statistical analyses Data from experiment #1 and #2 were analysed as a univariate analysis of variance using the GLM procedure of SPSS (IBM, version 21. 0), with the bottle as the experimental unit. In experiment #1, the bacterial strain, source of N, level of N and their interactions were considered as fixed effects for the analyses. In experiment #2, the level of N, source of N and their interaction were considered as fixed effects. Linear (L), quadratic (Q) and cubic (C) components of the response to incremental levels of nitrogen were evaluated using polynomial contrasts. Effects were considered significant at P � 0. 05. When significant differences were detected, differences among means were tested by pairwise comparisons (LSD test).
| Case definition The primary congenital anomaly reported in this study is undifferentiated CLP. This includes all CLP cases including isolated CLP (no other orofacial anomalies identified), CLP associated with multiple congenital anomalies, and CLP associated with syndromes. Isolated cleft palate and isolated cleft lip have been reported separately to this data set and are, therefore, not included here. Cases included all identified conceptions, resulting in an individual with CLP, regardless of outcome. Where data were available, subclassifications including isolated CLP, CLP associated with multiple congenital anomalies, and CLP associated with syndromes are reported as mutually exclusive categories. Keeping with accepted terminology, birth prevalence is used in this article to describe the point prevalence of CLP in discrete populations included in ICBDSR programs  (Mason, Kirby, Sever, & Langlois, 2005).
Mason et al. (2005)  suggested using total births alone as the denominator, but those data were not available in this study, so a slightly modified equation was used. Birth prevalence was calculated as follows: The programs are a mix of population and hospitalbased registries. Raw data were provided in the form of MS Excel documents with cases classified by pattern into the following categories: isolated CLP, CLP associated with multiple congenital anomalies, CLP associated with syndromes, and CLP unclassified. No program returned data for CLP unclassified and, therefore, analysis is focused on the other categories only. Further to this, all programs reported a "Total CLP" value that combines all the aforementioned subclassifications. Data sets included varying amounts of data on LB, stillbirths (SB) and ETOPFA for each of the CLP subclassifications.
The data set from each program was individually copied and pasted into a large Excel "master" file, which was of a format suitable for analysis in R. The data were inspected primarily through the use of the aggregate command and ggplot2 to produce summary statistic tables and graphs. Where possible, the data were further checked for obvious errors (e. g. , extremely low or high prevalence or mathematical errors such as more deaths than reported cases in a given year). Not all programs provided data for the entire observation period; therefore, data provided were averaged for the period that each program provided results. For example, when calculating prevalence, while the number of years returned varied, the denominator in all cases was the total number of LB plus SB reported by that program for all the years they returned data, and the numerator was the total cases observed during that same period.
| RESULTS Data from 22 programs amounting to 23 separate data sets were included in the analysis. This included a total number of 23,523,031 births and 15,103 CLP cases. Table  1  provides a description (location, type of registry, area covered, ascertainment period, stillbirth definition, whether ETOPFA is permitted, and prenatal screening services) of ICBDSR programs providing data and included in this study. A description of the follow-up method for LB for each program is presented in Table  2. Table  3  presents descriptive statistics including pregnancy outcomes for each of the included programs (total number of births, total number of CLP cases, prevalence per 10,000 births, percentage of LB among CLP cases, percentage of SB among CLP cases, and percentage of ETOPFA among CLP cases) for the observation period 1974-2014.
any obvious mathematical errors present in the data? 2. Are any of the reported values implausibly high or low? 3. Were any essential data missing? Data validation errors 4. Has the data entry template been edited from its original form to change labels or insertthere at least some data in all columns in the dataset? 8. Are zero values explicitly marked as such instead of just being left blank? 9. Are there enough reported cases to draw meaningful conclusions? entry columns been used in a way that is consistent with the column Are there multiple competing versions of the same dataset without indication of the most F I G U R E 3 Quality indicators used by authors to assess data quality of data sets and inform inclusion criteria.
Results We included 3,572 unrelated European ancestry patients with PD from 5 observational cohorts and 1 drug trial. The average AAO was 62. 6 years (SD = 9. 83), and 63% of participants were male. We found an average increase in the total MDS-UPDRS III score of 2. 3 points/year. We identified an association between PD axial motor progression and variation at the GJA5 locus at 1q12 (β = -0. 25, SE = 0. 04, p = 3. 4e -10 ). Exploration of the regulation of gene expression in the region (cis-expression quantitative trait loci [eQTL] analysis) showed that the lead variant was associated with expression of ACP6, a lysophosphatidic acid phosphatase that regulates mitochondrial lipid biosynthesis (cis-eQTL p-values in blood and brain RNA expression data sets: <10 -14 in eQTLGen and 10 -7 in PsychEncode).
Study Design and Quality Control at the Sample and Genetic Level We studied 6 observational and interventional longitudinal PD cohorts with either genotyping or whole genome sequencing (WGS) data available, totaling 4,971 patients (eTable 1, links. lww. com/NXG/A624). We selected cohorts which included longitudinal MDS-UPDRS part III assessments from the MDS-UPDRS  11  and applied quality control (QC) at the clinical data level (eFigure 1). To study the motor progression of PD, we derived limb and axial phenotypes from the MDS-UPDRS part III scale based on previously accepted definitions. 13  In addition, we used the MDS-UPDRS III total score as an overall measure of PD motor state (eTable 2). We included data up to 36 months from the baseline visit, within longitudinal observational and therapeutic cohorts, as we had high rates of data completion up to 36 months.
We quantified the power of LMMs to investigate the effect of SNPs in changes in the motor signs of PD (eMethods). To run the disease progression model, we used lmerTest R package (v. 3. 1-3; RRID:SCR_015656) and the Satterthwaite approach to approximate degrees of freedom and derive p-values and fitted them using restricted maximum likelihood (REML), as it produces acceptable type-1 error rates. 17  In addition, we used SCEBE 18 algorithm (v. 0. 1. 0) with REML and lme4 R package (v. 1. 1-30; RRID:SCR_015654) to decrease the computational expense of adding unexplained variability at the slope level in the disease severity model (eMethods). We validated SCEBE in 2 separate cohorts (eFigure 4). All tests were two-tailed. We used METAL software (version released on the 25/03/2011; RRID:SCR_002013) for meta-analysis of genomewide association summary statistics, using a fixed effects model weighted by β coefficients and the inverse of the standard errors.
Fine-Mapping and Functional Annotation We performed a conditional and a stepwise model selection procedure to determine whether there were independently associated SNPs in each locus of interest. To nominate causal variants, we applied fine-mapping. To further understand regulatory mechanisms in nominated loci, we mapped each locus against (1) cell type-specific and "bulk" genome enhancer marks, (2) enhancer-transcription start site (TSS) interaction marks from FANTOM5, and (3) brain cell type-specific transcriptional regulatory marks and distal enhancer-promoter interactions. 21, 22  To determine whether causal variants could be linked to the motor phenotypes by dysregulation of gene expression, we performed colocalization against cis-expression quantitative trait loci (eQTL) data sets. [23] [24] [25]  Software and packages to conduct the analyses and access the data are described in eMethods.
We also explored eQTLs data sets through the FUMA platform. We found that many of the GWAS-significant SNPs within the GJA5 locus were significant cis-eQTLs for ACP6, in PsychEncode, and eQTLGen. In particular, we found that the lead variant was a significant eQTL in PsychEncode, and eQTLGen, and also rs12037169, the proxy significant variant found in all cohorts, was a significant cis-eQTL in eQTLGen (eTable 7, links. lww. com/NXG/A624). We then performed a colocalization analysis to evaluate whether there was colocalization between the GWAS axial progression results and eQTL GWAS for gene expression at the GJA5 locus (eMethods). We used cis-eQTL data from eQTLGen and Metabrain cortex tissue cis-eQTLs data sets and performed a colocalization test for any gene within ±1 Mb from the GJA5 lead SNP.
For LINC00511, we found an anchored chromatin loop from the GWAS LD block in LINC00511 to a region where the neuronal SOX9 active promoter is found, suggesting that mutations in this distal regulatory region may alter SOX9 expression in neurons specifically (eFigure 10). We explored eQTL databases from FUMA. We found the lead variant in MAD1L1 as well as the fine-mapped nominated causal variant to be a significant cis-eQTL in BIOS and eQTLGen (eTable 8, links. lww. com/NXG/A624). We then performed a colocalization analysis to evaluate whether there is a shared causal variant between the 2 traits (eMethods). We did not find direct colocalization evidence for any gene within ±1 Mb from the GWAS lead SNPs. There was no cis-eQTL data available for SOX9. In the MAD1L1 locus, the posterior probability H3 (PPH3) (association with both phenotypic and expression traits, but distinct causal variants) was the highest (PPH3 in MAD1L1: eQTLGen = 0.
Discussion To understand the biology of motor progression in PD, we performed a large well-powered GWAS of PD motor progression. We have found 1 haplotype block at the GJA5 locus that is significantly associated with axial PD motor progression. This association was consistently replicated across individual cohorts included in our motor progression GWAS metaanalysis and was replicated in an analysis of H/Y supporting our findings. Further exploration of the GWAS significant signals in eQTL databases suggests that the GWAS hits may control the expression of ACP6, an enzyme that regulates lipid metabolism in mitochondria. 30  Changes in ACP6 concentrations are found in Gaucher disease (GD), although there is no clear link between ACP6 levels in and GD progression. ACP6 has a high astrocyte specificity,  31  and mitochondrial dysfunction has been widely associated with PD etiology.
A separate GWAS metaanalysis assessing the PD genetic contribution to the disease motor severity and subsequent functional annotation identified MAD1L1 and SOX9 as candidate genes associated with PD axial motor severity. Nevertheless, these potential associations did not reach genome-wide significance, and further analysis in distinct PD cohorts is needed for validation. Strengths of our study include the large sample size and replication of our results across cohorts and across different measures of axial motor progression. Potential limitations of our identification of ACP6 as the relevant gene at the GJA5 locus include the lack of colocalization between the phenotype and expression GWAS, although these analyses are currently limited by the sample size of eQTL data sets and the lack of cellspecific gene expression data.