dataesr/scientific-paragraphs-categorization huggingface dataset:
Regional Profiling of Aging Primate Synapses Reveals Protein Expression Trends Correlating with Synaptic Vulnerability In conjunction with the Pearson correlation graphs and PCAs, to dissect regionspecific, age-dependent synaptic alterations, we generated network graphs of the regional time course proteomic data, again using BioLayout Express 3D  (Theocharidis et al. , 2009). The software applies unbiased Markov clustering algorithms to the input data and groups proteins displaying similar expression trends. This allows visualization of spatiotemporal profiles promoting the identification of physiological cascades altered within the dataset. Graphs were constructed using regional differentially expressed proteins (altered >20%) through the time course of aging (Figure  1B ), providing 20-30 clusters per region.
Thus, these data represent a physiologically relevant paradigm demonstrating that alterations in the expression of these candidates in the NHP and/or human hippocampus may correlate with selective synaptic vulnerability during aging. Despite presenting several candidates that may have the propensity to alter synaptic morphometry in both NHPs and human patients, it is unlikely that the expression of single proteins solely regulate regional synapse vulnerability during mammalian brain aging. Instead, it is probable that multiple cellular and molecular pathways up-and downstream of the identified candidates converge to regulate age-dependent alterations in synaptic structure and function. By employing Ingenuity Pathway Analysis (IPA) software, we sought to identify common upstream master regulators that may be modulating the expression of the NHP or human patient candidates simultaneously.
Despite presenting distinct candidates that may be associated with NHP (RCN2, CAPZA2, CYC1, OGDH, ROCK2, and CDH2) (Figure  4A ) or human patient (CDC42EP4, STMN3, IGKC, CD99, SLC7A5, APOA1, HCN2, PPT1, FKBP4, HEBP1, and PDAP1) (Figure  4B ) age-dependent hippocampal synaptic vulnerability, upstream analyses highlighted that the identified candidates from both the NHP and the human patient datasets appeared to sit downstream of the microglial cytokine TGF-b1 in the hierarchical cellular signaling cascade (Figures  4C  and 4D ). With further examination, we established that this particular pathway demonstrated conserved activation in both aged NHP and aged human patient hippocampal synapses, promoting concomitant perturbed expression of our identified candidate proteins (Figures  4C  and 4D ).
Data were acquired using Xcalibur software. Raw proteomic data were imported into Progenesis for characterization and analysis of relative ion abundance. 2D representations of MS/MS output were created for each sample and these were aligned to determine similar features (average alignment score > 80%). Following alignment, data were filtered by retention time with features detected below 17 minutes and above 140 minutes (NHP regional analysis of synaptic aging)/below 11. 81 minutes and above 134. 37 minutes (human patient regional analysis of synaptic aging) discarded to correct for elution variability. The runs were grouped according to age and brain region and statistical p values were calculated in Progenesis software. For calculation of statistical power and p values, the per run abundances for each grouped experimental condition were transformed through Arc-Sinh normalization to minimize data variability.
Following this, 1-way ANOVAs were conducted on the data assigned to the pre-defined experimental conditions resulting in the generation of reliable p values associated with each identified peptide. Peptides were filtered by the following criteria: power < 0. 8, fold change > 1. 2, p > 0. 05 and the remaining data were exported from Progenesis for identification of individual peptide sequences using the IPI-Macaca mullata (NHP regional analysis of synaptic aging) and IPI-Homo sapiens (human patient regional analysis of synaptic aging) databases via Mascot Search Engine (V2. 3. 2). Enzyme specificity was set to that of trypsin, allowing for cleavage N-terminal to proline residues and between aspartic acid and proline residues. Other parameters used were as follows. (i) Variable modifications: methionine oxidation, methionine dioxidation, protein N-acetylation, gln / pyro-glu.
Biolayout Express 3D Proteomic data was dissected using the complex pattern recognition software, Biolayout Express 3D  (Theocharidis et al. , 2009). The software allows visualization of molecular networks by applying Markov clustering algorithms to raw proteomic data (MCL 2. 2). All graphs were clustered using Pearson correlation r = 0. 95. Clusters of interest indicating age-dependent alterations included those that demonstrated a steady up-or downregulation or a late stage up-or late stage downregulation during the time course of aging. Proteins from clusters with analogous expression profiles underwent a subtractive process -candidates appearing in both the resistant and vulnerable clusters, altered in the same manner, were unlikely to regulate synaptic vulnerability during aging. These candidates were eliminated as modulators of synaptic vulnerability.
Ingenuity Pathway Analysis IPA was performed as previously described  (Wishart et al. , 2007)  with the interaction data limited as follows: direct and indirect interactions; experimentally observed data only; 35 molecules per network; 10 networks per dataset. Prediction activation scores (z-score) were calculated in IPA. The z-score is a statistical measure of the match between an expected relationship direction and the observed protein expression. Positive z-scores indicate activation (orange) and negative z-scores indicate inhibition (blue)  (Savli et al. , 2008).
ACKNOWLEDGMENTS We thank  Chris-Anne Mackenzie  from the  Edinburgh Brain Bank  for coordinating the use of human patient tissue and the  British consulate in San Francisco  for instigating this international collaborative research project. We thank  Samantha Eaton  and  Rachel Kline  from the  Wishart laboratory  for assistance with human sample handling, blotting, and pathway analysis. L. C. G. is supported by a  BBSRC studentship and MRC CiC funding. T. M. W. is funded by the  BBSRC   Institute Strategic Programme  ( ISPG/5 12-17  and ISPG/1  18-22 ) and the  Euan MacDonald Centre for Motor Neurone Disease Research. S. G. K. and H. F. U. are funded by  NIH  ( OD-011092  and  AG-062220 ).
Deposited Data NHP hippocampal synaptic aging time course This paper https://doi. org/10. 7488/ds/2431 NHP occipital cortex synaptic aging time course This paper https://doi. org/10. 7488/ds/2431 Human patient hippocampal synaptic aging time course This paper https://doi. org/10. 7488/ds/2431 Human patient occipital cortex synaptic aging time course This paper https://doi. org/10. 7488/ds/2431 Software and Algorithms Xcalibur Thermo Fisher Cat: OPTON-30382 Mascot V2. 3. 2 Matrix Science http://www. matrixscience. com Progenesis Nonlinear Dynamics http://www. nonlinear. com BioLayout Express 3D (now known as Graphia Pro) Kajeka https://kajeka. com DATA AND SOFTWARE AVAILABILITY All proteomic datasets generated and used by this study are freely available at: https://doi. org/10. 7488/ds/2431 e4 Cell Reports 27, 1018-1026.
Data analysis. To determine if environmental parameters influenced the abundance of plastics at each sampling point, linear regressions were performed between plastic concentration and the parameters local depth, sea state and wind speed, recorded during sampling. To evaluate diversity of plastic colors between the sampling points, we used Shannon's index, and to check if there was dominance of any colors between the points, we used Simpson's index. All statistical analyses were done in R (R Development Core Team, version 3. 5. 0), using the car and vegan packages. Dispersal model. Virtual particles were tracked using the OceanParcels framework  54  in surface velocity fields from the HYCOM + NCODA Global 1/12° Analysis 55 , on which Stokes drift was added from WaveWatchIII  56. At each of the 12 locations, 100 virtual particles were released on the day of sampling, and then tracked back in time for seven years, with output stored daily.
Acknowledgements A. L. d. F. Lacerda received a scholarship and was supported with a funding from the  Program of Academic Excellence (PROEX)  from the  Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES). We thank all  Brazilian Navy crew of the Polar Vessel Almirante Maximiano , as well as investigators involved in the cruise, for their efforts during sampling. This study was conducted within the activities of project  INTERBIOTA , financed by the  National Council for Research and Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico-CNPq  Grant number  407889/2013-2 ) of the  Brazilian Ministry of Science, Technology, Innovation and Communication-MCTIC. CNPq also provided research fellowship to ERS ( PQ 307846/2014-8 ) and  MCP  ( PQ 312470/2018-5 ).
Although humans cooperate universally, there is variability across individuals, times and cultures in the amount of resources people invest in cooperative activities. The origins of such variability are not known but recent work highlights that variations in environmental harshness may play a key role. A growing body of experimental work in evolutionary psychology suggests that humans adapt to their specific environment by calibrating their lifehistory strategy. In this paper, we apply structural equation models to test the association between current and childhood environmental harshness, life-history strategy and adult cooperation in two large-scale datasets (the World Values Survey and the European Values Study). The present study replicates existing research linking a harsher environment (both in adulthood and in childhood) with a modulated reproduction-maintenance trade-off and extends these findings to the domain of collective actions.
First, large-scale datasets with representative samples of respondents are used. Second, we exploit indicators of life-history strategies which come as close as possible to testing the reproduction-maintenance trade-off (i. e. , respondents' health status, respondents' number of children, respondents' age at their first child's birth,  Ellis et al. , 2009). Third, variables reflecting investment in actual collective actions -i. e. behaviors that involve concrete costs for the agent and have benefits for others -were used as proxies for cooperation rather than self-reported attitudes. In this paper, we thus test the relationship between current and childhood environmental harshness and people's propensity to invest in collective actions (e. g. , volunteering, activism, etc. ). We further test the hypothesis that variability in cooperative behavior is mediated by differences in life-history strategy.
Our specific hypotheses were that (1) current environmental harshness is associated with lower investment in collective actions, and this effect is mediated by variations in individuals' reproduction-maintenance trade-off; (2) childhood environmental harshness is associated with lower investment in collective Study. We used the World Values Survey to test hypothesis 1 concerning current environmental harshness. The advantage of this dataset is that it includes non-European countries and in particular poorer countries. We used the European Values Study to test hypothesis 2 concerning childhood environmental harshness. Using two independent datasets allowed us to externally replicate our results and to test the robustness of the association between harshness and cooperation. In addition, we simulated internal replication by randomly dividing each individual dataset into two subsamples of similar size: a discovery sample and a replication sample.
Study 1: Current harshness and collective action in the World Values Survey We tested the association between current environmental harshness, life-history strategy and collective action by applying structural equation modeling on the World Values Survey dataset  (World Values Survey Longitudinal Data File 1981 -2014 (World 1981 -2014 ), 2015). The World Values Survey is an independent large-scale sociological survey exploring people's values and beliefs across countries and times. The World Values Survey has relied on social scientists since 1981 to collect data from large representative samples of the populations from almost 100 countries. In this study, we used the fourth wave of the survey for which data was collected between 1999 and 2004. We focused on the fourth wave because it includes the same collective action variables as the ones included in the European Values Study, which we used in the second study reported in this paper.
Survey. However, some respondents could not be included in our analyses. First, one of the variables used to model life-history strategy is only relevant for respondents with children, which led us to focus on the 41. 799 respondents who report having children. Second, we removed countries for which the cooperation variables had not been filled out, which reduced the dataset to 25. 670 respondents living in 27 different countries. We randomly split this sample in a discovery subsample (12. 962 respondents) and a replication subsample (12. 708 respondents). In both subsamples, we excluded participants with too many empty cells (more than two standard deviations from their respective subsample mean) which led to 12. 083 respondents for the discovery sample and 11. 888 respondents for the replication sample.
Conclusions Our prediction was that people who currently live in harsher environments invest less in collective action and that this effect is mediated by modulations of the reproductionmaintenance trade-off. Data from the World Values Survey confirm this hypothesis and show that the association between current environmental harshness and adult involvement in collective action is mediated by life-history strategies. This result is compatible with at least two (not mutually exclusive) interpretations. One interpretation is that lower involvement in collective action is the result of a flexible behavioral adjustment in response to short-term variations in participants' resource level. Indeed, humans routinely adjust their investments in collective action in response to short-term changes in their environment: they cooperate less if they feel stressed  (Riis-Vestergaard et al.
Study 2: Childhood harshness and collective action in the European Values Study We tested the association between childhood environmental harshness, life-history strategy and adult investment in collective actions by applying structural equation modeling on the European Values Study dataset  (European Values Study Longitudinal Data File 1981 -2008 (EVS 1981 -2008 ), 2015). The European Values Study is an independent large-scale sociological survey exploring people's values and beliefs across countries and times. The European Values Study has relied on social scientists since 1981 to collect data from large representative samples of the populations from 47 European countries. In this study, we used the fourth wave of the survey for which data was collected between 2008 and 2010. We focused on the fourth wave because it includes the same collective action variables as the World Values Survey, which gives us the opportunity to replicate our initial findings on an independent sample.
This study was supported by the  Institut d' Etudes Cognitives  ( ANR-10-IDEX-0001-02  FrontCog), the  Institut national de la santé et de la recherche médicale (INSERM) , an  ERC consolidator grant  (no. 8657 ) and an " Action Incitative " funding from the  École Normale Supérieure. The pre-registrations and R scripts used in this study are available through the  Open Science Framework :.
Results Probe design using CATCH. To design probe sets, CATCH accepts any collection of sequences that a user seeks to target. This typically represents all known genomic diversity of one or more species. CATCH designs a set of sequences for oligonucleotide probes using a model for determining whether a probe hybridizes to a region of target sequence (Methods and Supplementary Fig. 1a ); the probes designed by CATCH include guarantees concerning the capture of input diversity under this model. CATCH searches for an optimal probe set given a desired number of oligonucleotides to output, which might be determined by factors such as cost or synthesis constraints. The input to CATCH is one or more datasets, each composed of sequences of any length, that need not be aligned to one another. In this study, each dataset consists of genomes from one species, or closely related taxa, that we seek to target.
CATCH incorporates various parameters that govern hybridization (Supplementary Fig. 1b ), such as sequence complementarity between probe and target, and accepts different values for each dataset (Supplementary Fig. 1c ). This allows, for example, more diverse datasets to be assigned less stringent conditions than others. Assume we have a function s(d, θ d ) that gives a probe set for a single dataset d using hybridization parameters θ d , and let S({θ d }) represent the union of s(d, θ d ) across all datasets d where {θ d } is the collection of parameters across all datasets. CATCH calculates S({θ d }), or the final probe set, by minimizing a loss function over {θ d } while ensuring that the number of probes in S({θ d }) falls within the specified number of oligonucleotides (Fig. 1a ). The key to determining the final probe set is then to find an optimal probe set s(d, θ d ) for each input dataset.
Also, a user can design probe sets for capturing only a specified fraction of each target genome and, relatedly, for targeting regions of the genome that distinguish similar but distinct subtypes. CATCH also offers an option to blacklist sequences, for example, highly abundant ribosomal RNA sequences, so that output probes are unlikely to capture them. CATCH can use locality-sensitive hashing  27, 28  , if desired, to reduce the number of candidate probes that are explored, improving runtime and memory usage on especially large numbers of input sequences. We implemented CATCH in a Python package that is publicly available at https://github. com/ broadinstitute/catch. Probe sets to capture viral diversity. We used CATCH to design a probe set that targets all viral species reported to infect humans (V ALL ), which could be used to achieve more sensitive metagenomic sequencing of viruses from human samples.
V ALL encompasses 356 species (86 genera, 31 families), and we designed it using genomes available from NCBI GenBank  29, 30  (Supplementary Table  1 ). We constrained the number of probes to 350,000, significantly fewer than the number used in studies with comparable goals  18, 19  , reducing the cost of synthesizing probes that target diversity across hundreds of viral species. The design output by CATCH contained 349,998 probes (Fig. 1c ). This design represents comprehensive coverage of the input sequence diversity under conservative choices of parameter values, for example, tolerating few mismatches between probe and target sequences (Fig. 1d ). To compare the performance of V ALL against probe sets with lower complexity, we separately designed three focused probe sets for commonly co-circulating viral infections: measles and mumps viruses (V MM ; 6,219 probes), Zika and chikungunya viruses (V ZC ; 6,171 probes), and a panel of 23 species (16 genera, 12 families) circulating in West Africa (V WAFR ; 44,995 probes) (Supplementary Fig.
These eight viruses together reflect a range of typical viral titers in biological samples, including ones that have extremely low levels, such as ZIKV  6, 7. The samples encompass a range of source materials: plasma, serum, buccal swabs, urine, avian swabs, and mosquito pools. We performed capture on these libraries and sequenced them both before and after capture. To compare enrichment of viral content across sequencing runs, we downsampled raw read data from each sample to the same number of reads (200,000) before further analysis. Downsampling to correct for differences in sequencing depth, rather than the more common use of a normalized count such as reads per million, is useful for two reasons. First, it allows us to compare our ability to assemble genomes (for example, due to capture) in samples that were sequenced to different depths.
See Supplementary Note 2 for details regarding parameter choices. Previous approaches for targeting viral diversity use clustering in probe set design. The shaded regions around each line are 95% pointwise confidence bands calculated across randomly sampled input genomes. c, Number of probes designed by CATCH for each dataset (of 296 datasets in total) among all 349,998 probes in the V ALL probe set. Species incorporated in our sample testing are labeled. d, Values of the two parameters selected by CATCH for each dataset in the design of V ALL : number of mismatches to tolerate in hybridization and length of the target fragment (in nucleotides) on each side of the hybridized region assumed to be captured along with the hybridized region (cover extension). The label and size of each bubble indicate the number of datasets that were assigned a particular combination of values.
Discussion CATCH condenses highly diverse target sequence data into a small number of oligonucleotides, enabling more efficient and sensitive sequencing that is only biased by the extent of known diversity. We show that capture with probe sets designed by CATCH improves viral genome detection and recovery while accurately preserving sample complexity. These probe sets have also helped us to assemble genomes of low-titer viruses in other patient samples: V ZC for suspected ZIKV cases  6  and V ALL for improving rapid detection of Powassan virus in a clinical case  39. The probe sets we have designed with CATCH, and more broadly capture with comprehensive probe designs, improve the accessibility of metagenomic sequencing in resource-limited settings through smaller-capacity platforms. For example, in West Africa we are using the V ALL probe set to characterize LASV and other viruses in patients with undiagnosed fevers by sequencing on a MiSeq (Illumina).
We represent a set of taxa and its target sequences with a dataset d, with its own parameters θ d. Let {θ d } be the collection of θ d across all d. We wish to find S({θ d }), the union of s(d, θ d ) across all datasets d. CATCH finds this by solving a constrained nonlinear optimization problem ∑ θ θ θ =. | | ≤ θ L S N { }* arg min ( ) s t ({ }) d d d d { } d The constraint N on the number of probes in the union is specified by the user; this is the number of probes to synthesize and might be determined on the basis of synthesis cost and/or array size. CATCH solves this using the barrier method with a logarithmic barrier function. By default, we use the following loss function for each d θ β β = + L w m e ( ) ( ) d d d d 1 2 2 2 where m d gives a number of mismatches to tolerate in hybridization and e d gives a cover extension, as defined above.
w d allows a relative weighting of datasets, for example, if one should have more stringent assumptions about hybridization and thus more probes. β 1 , β 2 , and the set of {w d }s can be specified by the user. The user can also choose to generalize the search to a different set of parameters ∑ θ β θ = L w ( ) d d i i di 2 where θ di is the value of the ith parameter for d and β i is a specified coefficient for that parameter. In practice, we have used the default loss function above, with It is possible that CATCH cannot find a choice of {θ d } such that |S({θ d })| ≤ N. This might be the case, for example, if the grid of θ d values over which a user precomputes s(d, θ d ) has too small a range to satisfy the constraint. That is, one or more of the parameter values may need to be relaxed (across one or more datasets) to obtain ≤ N probes.