dataesr/scientific-paragraphs-categorization huggingface dataset:
Recently, it has been demonstrated that GPX8 expression increases during epithelial-tomesenchymal transition in several cell lines with evidence that increased GPX8 affects IL-6/JAK-STAT3 signalling via the soluble IL6 receptor. 49 Given that hepatic miR-193a-5p expression is known to exhibit an inverse correlation with serum levels,  37  we therefore postulate that the increase in serum miR-193a-5p we observed in patients with advanced NAFLD is indicative of increased hepatocyte export. This could induce increased GPX8 expression in other hepatic cells as they respond to oxidative stress and undergo further transitions during NAFLD progression. Indeed, we observed a significant positive correlation between serum miR-193a-5p and hepatic GPX8. Nonetheless, further investigations are required to identify the specific cells in which GPX8 acts, as low hepatocyte expression in the single-cell RNA-seq dataset suggests that it may exert its effects elsewhere in the liver.
30 ublished reports on miRNAs in NAFLD are seldom in complete agreement,  50  and as such, one should be mindful of pertinent limitations. Discordance between studies could be caused by sampling and interpretation errors, and so the histological classification of patients' disease might be inconsistent from the outset. Despite being a component of an in vitro diagnostic test for NASH,  19  our sequencing analysis filtered out miR-34a-5p because of low levels, yet subsequent qPCR was in fact able to detect it. Conversely, the strongest signal in NAFLD relative to controls, miR-3687, was not confirmed by qPCR. These examples highlight that sequencing should always be replicated independently using a different methodology to test the strength of the original dataset. This approach combined with use of a replication cohort enabled us to confirm the novel miR-193a-5p signal.
A meta-analysis identified strategies to decrease product-based (PB; CH 4 per unit meat or milk) and absolute (ABS) enteric CH 4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1. 5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies-namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratiodecreased CH 4 per unit meat or milk by on average 12% and increased AP by a median of 17%.
Results and Discussion The meta-analysis included 98 mitigation strategies reported in 430 peer-reviewed journal publications (SI Appendix, Table  S1 ). Mitigation strategies were classified into three main categories: animal and feed management, diet formulation, or rumen manipulation strategies. Of the strategies included,  63   A summary of the studied mitigation strategies is presented in Fig. 1 , and the full list of the studied mitigation strategies and their effects on enteric CH 4 emission and animal performance metrics are presented in SI Appendix, Table  S2  and Dataset S1, respectively. Effective mitigation strategies and their impact on CH 4 I M , CH 4 I G , daily CH 4 , CH 4 Y, as well as their relevance for different systems (feedlot, mixed, and grassland) are presented in Fig.
The cost-effectiveness of this strategy will depend on forage and concentrate costs as well as associated increases in animal production and the price of animal products (meat and milk). Strategies that Decrease Absolute CH 4 Emissions. Rumen manipulation by feeding CH 4 inhibitors effectively decreased CH 4 I M (32%, 21 to 40%, n = 2) without affecting feed intake or milk yield. Of the CH 4 inhibitors, 3-nitrooxypropanol (3-NOP) acts on a key enzyme of the methanogenesis pathway that is used by methanogenesis to produce CH 4  (34). Insufficient data were available in the database to evaluate its effect on weight gain or fiber digestibility in this analysis. However, in recent studies, 3-NOP did not show adverse effects on weight gain of growing beef cattle  (35)  or fiber digestibility in earlylactation dairy cows  (36)  and decreased daily CH 4 emissions throughout a 15-wk experiment  (37).
Materials and Methods Literature Search and Classification of Mitigation Strategies. The database for this meta-analysis was compiled using data obtained by searching the databases of the Commonwealth Agricultural Bureau International (CABI), the EBSCO Discovery Service, and the Web of Science. Publications from 1964 through 2016 were searched using CABI and EBSCO Discovery Service with the search terms "rumen" AND "methane" and an additional four searches were completed in the EBSCO Discovery Service using the term "rumen" in combination with "methane," "energy partitioning," "energy metabolism," or "energy balance. " Publications from 2017 through 2018 were searched using CABI and Web of Science databases. Seven searches were conducted with the search term "methane" in combination with "beef," "cattle," "dairy," "goat," "sheep," "rumen," or "ruminant" and three searches with the search term "rumen" in combination with "energy balance," "energy metabolism," or "energy partitioning.
" Publications listed in an independently developed database supported by the AnimalChange project, MitiGate  (17) , were merged with the database created in the current analysis. The abstracts of the publications found in the search were reviewed, and based on the abstract content, publications were selected for further consideration if they included in vivo measurement of enteric CH 4 emissions, a clearly defined treatment and control, and multiple replications (at least four or more animals in continuous design experiments, cross-over design experiments, and so forth). Publications were excluded if they were not from peer-reviewed literature or if they were not in English, French, German, Spanish, or Portuguese. Furthermore, publications were excluded if they were based on inappropriate study design (i.
e. , experimental period ≤10 d) or measurement technique [e. g. , the "sniffer technique" that is based on CH 4 -to-CO 2 ratio of exhaled breath  (70, 71) ]. The completed database consisted of 650 publications. From these, only the publications that had a treatment that could be assigned to one of three main mitigation categories, as described below, and reported statistical variance for at least one of the CH 4 emissions emission metrics (e. g. , least significant difference, relative standard deviation, or SEM) were included in the final analysis. WebPlot-Digitizer (https://automeris. io/WebPlotDigitizer/; accessed 30 October 2019) was used to determine absolute values for a total of nine metrics in seven publications where data were reported as figures. The data were classified into three main mitigation categories: 1) animal and feed management, 2) diet formulation, and 3) rumen manipulation, each of which was then further classified into up to five subcategories (SI Appendix, Table  S2 ).
Digestibility (of fiber) metrics included only apparent digestibility of neutral detergent fiber. Where multiple treatments of a common treatment type were present within an experiment, the treatment means were averaged, and their respective errors pooled, so that each experiment produced a single "treatment" and "control" pair of response means and SDs. The final dataset analyzed in the present study included data from 430 peerreviewed publications, of which 66% were of cattle, 31% of small ruminants (sheep and goats), and 3% of other ruminant species (buffalo, deer, and yak). The complete list of references used in the current analysis is given in SI Appendix, Table  S1  and the database can be found on https://www. datacommons. psu. edu under the link https://www. datacommons. psu. edu/commonswizard/MetadataDisplay.
aspx?Dataset=6333 and the DOI 10. 26208/6em7-k817. The majority of the publications reported daily CH 4 emissions (92%), feed intake (84%), and CH 4 Y (71%), but less than half of the publications reported weight gain for all animal types (growing, lactating, and other adult animals) (49%), Y m (48%), fiber digestibility (41%), milk yield (29%), CH 4 I M (21%), or CH 4 I G (7%) (SI Appendix, Fig. S5 ). The final analysis only included weight gain data for growing animals (106 publications), which led to the exclusion of the weight gain data of half of the publications (104 publications) that reported weight gain data for lactating and other adult animals. Statistical Analysis. A mixed-model meta-analysis weighted by inverse variance was carried out considering treatment mean comparisons within the publications as a random effect.
Computations were carried out using Comprehensive Meta-Analysis (V. 3. 3. 070; Biostat). All analyses were adjusted for multiple comparisons using a step-down Bonferroni procedure to reduce the risk of type I error (74) (SAS, v9. 4; SAS Institute). The effect of a mitigation strategy was considered significant for adjusted P < 0. 05 and 0. 05 ≤ adjusted P ≤ 0. 10 was considered as a trend.
For a 100% adoption rate of one PB strategy, the identified average (average of all mitigation potentials of strategies applicable to a production system), minimum and maximum (strategies with lowest and highest mitigation potential applicable to a production system, respectively) reductions of CH 4 I for a production system were used to adjust the CH 4 I reported by GLEAM (4) for red meat and dairy protein for each of the projected years. When there were no data for the CH 4 I G reduction potential of a strategy, it was assumed that the minimum reduction potential was 0%, the maximum reduction potential was the one identified for CH 4 I M , and the average reduction potential was the average of the minimum and maximum reduction potential. For a 100% adoption rate of one ABS strategy, the identified average, minimum, and maximum reductions of daily CH 4 emission for a production system were used to adjust the projected annual CH 4 emissions for all red meat and dairy protein of a given productions system of the projected years.
For a 100% adoption rate of one PB and one ABS strategy, the reduction for the adoption of one PB strategy was first projected, and afterward, the adoption of one ABS strategy was projected. Similar calculations were done for the other three assumed adoption rates (75%, 50%, and 25%). Data Availability. Data have been deposited in Penn State Data Commons (http://doi. org/10. 26208/6em7-k817). ACKNOWLEDGMENTS. We thank the GLOBAL NETWORK project for generating part of the database. The GLOBAL NETWORK project (https://globalresearchalliance. org/research/livestock/collaborative-activities/global-research-project/; accessed 20 June 2020) was a multinational initiative funded by the Joint Programming Initiative on Food Security, Agriculture, and Climate Change and was coordinated by the Feed and Nutrition Network (https://globalresearchalliance.
org/research/ livestock/networks/feed-nutrition-network/; accessed 20 June 2020) within the Livestock Research Group of the Global Research Alliance on Agricultural GHG (https://globalresearchalliance. org; accessed 20 June 2020). We thank MitiGate, which was part of the AnimalChange project funded by the EU under Grant Agreement FP7-266018 for sharing their database with us (http://mitigate. ibers. aber.
ac. uk/, accessed 1 July 2017). Part of C. A. , A. N. H. , and S. C. M. 's time in the early stages of this project was funded by the Kravis Scientific Research Fund (New York) and a gift from Sue and Steve Mandel to the Environmental Defense Fund. Another part of C. A. 's work on this project was supported by the National Program for Scientific Research and Advanced Studies -PROCIENCIA within the framework of the "Project for the Improvement and Expansion of the Services of the National.
System of Science, Technology and Technological Innovation"  (Contract No. 016-2019 ) and by the  German Federal Ministry for Economic Cooperation and Development (issued through Deutsche Gesellschaft f€ ur Internationale Zusammenarbei)  through the research " Programme of Climate Smart Livestock" (Programme 2017. 0119. 2 ). Part of A. N. H. 's work was funded by the  US Department of Agriculture (Washington, DC) National Institute of Food and Agriculture Federal Appropriations  under Project  PEN 04539  and  Accession  no. 1000803. E. K. was supported by the  Sesnon Endowed Chair Fund  of the  University of California, Davis.
Our integration includes advanced audio playback capabilities with multiple loudspeakers, an adaptive procedure, compatibility with standard I/Os and their synchronization through implementation of the Lab Streaming Layer protocol. We exemplify the capabilities of this extended OpenSesame platform with an implementation of the three-alternative forced choice amplitude modulation detection test and discuss reliability and performance. The new features are available free of charge from GitHub: https:// github. com/ elus-om/ BRM_ OMEXP.
, 2017; Alhanbali, Dawes, Millman, & Munro, 2019; Seifi Ala et al. , 2020; Fiedler et al. , 2021; Zekveld, Koelewijn, & Kramer, 2018; Wiggins, Anderson, Kitterick, & Hartley, 2016; Mackersie & Cones, 2011). Experimental research paradigms in auditory science are still generally custom-built, and labor-intensive. Indeed, the design and implementation of auditory experiments commonly requires for instance the a priori design and recording of sound material in a sound studio, the pre-mixing of various tracks in specialized software, followed by the implementation of the experimental workflow with the control of playback timing, the specification of the sequence and order of audio playback, etc. The implementation of this experimental workflow is generally carried out with programmatic tools such as MATLAB or Python.
Here, we present a platform-based experiment builder offering the audio control features commonly sought by auditory scientists. We build on the open-source OpenSesame platform  (Mathôt, Schreij, & Theeuwes, 2012)  to streamline the implementation of behavioral and cognitive hearing science experiments that include multi-channel audio stimuli while interfacing with various synchronous I/Os through the Lab Streaming Layer protocol. Mathôt et al. give an extensive account of the main solutions available for cognitive science experiment building  (Mathôt et al. , 2012). We summarize and elaborate on their findings with CHS applications in mind. Of the available experiment-building platforms, few are based on a commonly used scripting language, a feature that means that the specification of highly specific elements of computation or logic that are not readily available is not easily accessible.
Cognitive and behavioral experiment builders Since then, Peirce et al. introduced PsychoPy2 building on PsychoPy  (Peirce et al. , 2019). One of the important additions to the original PsychoPy package is the option of using two kinds of interfaces, one for scripting in Python, and the other for constructing experiments graphically and the addition of the ioHub system for asynchronous control of various pieces of hardware, mainly conceived for eyetracking purposes. Mathôt et al. introduced  OpenSesame, an experimentbuilding platform for cognitive science with extensive functionality, written in Python, compatible with PsychoPy, allowing scripting and available as open-source software under the GPLv3 license. OpenSesame is a graphical experiment builder, made of a collection of so-called "plugins" that are to be understood as modular functional blocks.
Plugins are available for displaying text, images, or videos on screen, collecting user-input, recording data from different hardware peripherals, and even offering basic functionality for playing back audio stimuli, all as part of a user-centric laboratory experiment. OpenSesame proposes a little-code user interface, which can be particularly useful for non-technical users who do not know scripting/coding but are keen to design their own experimental paradigms. OpenSesame will also appeal to code-skilled users, who will be able to specify advanced functionality using Python. OpenSesame offers to run implemented experiments using a simplified version of its interface or "runner" version.
If an input track has more than one channel then they are summed into one. Moreover, a set of plugins was implemented to allow the recording of synchronous input data streams from various devices provided they implement the Lab Streaming Layer (LSL) protocol,  (Kothe & et al. 2019a). LSL is a system enabling the unified collection of time-series measurements in research experiments. LSL handles networking, time-synchronization, (near-) real-time access to these data streams as well as the centralized collection, viewing, and disk recording of the data carried by these streams  (Kothe & et al. 2019b ). The list of plugins added to OpenSesame to constitute OMEXP are summarized below and described in detail in the following section. The new plugins are available in GitHub (https:// github. com/ elus-om/ BRM_ OMEXP) and they can be pip-installed inside OpenSesame following the installation instructions.
LSL plugins The Lab Streaming Layer protocol provides an abstraction layer for the network-based real-time acquisition of timeseries data coming from peripheral applications also implementing LSL. LSL relies on a core liblsl library  (Kothe & et al. 2019b ), which can be handled in Python using the pylsl Python package  (Kothe, 2019). LSL is based on the use of Stream Outlet, Stream Inlet, and Resolver classes. Stream Outlet makes the time-series data streams available on the LSL network. Hardware manufacturers interested in making the data their equipment generates available on the LSL network need to implement an App (using LSL terminology) that overwrites Stream Outlet. The Resolver collects and interprets the different Stream Outlets. It queries based on the name, type, or some other value field to list the available Stream Outlets on the LSL network that match the request.
The Audio Mixer plugin is central to the experiment. Sketchpads plugins will allow the presentation of the red rectangles, and the Adaptive next plugin serves to change [depth], the depth of the modulation of the target signal (the Adaptive Init plugin must be used once in the initialization phase of the experiment). An inline script plugin is used to generate the audio stimuli before performing the main trial sequence is executed. OpenSesame gives the possibility to display instructions to the test participant using the Sketchpads plugin. This 3-AFC AMDT version was implemented for an automated validation purpose, therefore little to no instructions were included. A condition Loop is used nests the overall main trial Sequence so that trials of modulation depth detection are run for different pure-tone and modulation frequencies (the conditions).
Nested in the condition Loop, see Fig. 3 , the main trial Sequence will be run several times by presenting an AM tone (at a specific frequency set by the Condition loop) in random order among the three audio stimuli of each trial. This is done thanks to the trial Loop. The trial Loop randomizes the association between the target audio stimulus with one of the rectangular shape displayed on screen for each given trial, i. e. , whether the AM tone will be related to the first, second, or third rectangular shape. For each iteration in the trial Loop, the audio stimuli are generated (at the given carrier frequency with a given modulation depth) using the inline script plugin. The Python script used to generate the audio stimuli is illustrated in Fig. 4. The Audio Mixer (Fig. 5 ) is then responsible for playing the three previously generated audio stimuli (signal1, signal2, and signal3 in Fig.
Discussion The implementation of experimental paradigms in cognitive hearing science is generally complex and time-consuming. Building on the open-source OpenSesame platform, we propose a set of OpenSesame plugins that can help in streamlining the implementation of experiments involving advanced audio playback capabilities synchronously with visual stimuli and multimodal data acquisition from various physiological monitoring pieces of equipment. Implemented features include: advanced audio playback capabilities with multiple loudspeakers at various sound pressure levels and tracks, as well as control over timing, audio level calibration, a state-of-the-art adaptive procedure, and compatibility with standard inputs/outputs (I/Os) using LSL. These new plugins have been validated behaviorally including our adaptive routine plugin which we tested as part of an implementation of a 3-AFC ADMT.
Importantly, we have characterized the timing performance of our audio playback module and demonstrated a very consistent sub-10-ms jitter which we are confident is sufficient for the general usability of OMEXP for many CHS experimental paradigms including those incorporating pupillometry or fNIRS. The time constant of pupil response to auditory stimuli for instance lies in the 100-ms range  (Winn, Wendt, Koelewijn, & Kuchinsky, 2018). We must acknowledge that an average jitter in the 10-ms range does not allow for low-latency repeated objective measurements Setting aside this limitation, we could therefore envisage OMEXP to help with the homogenization of experiment design and data collection format in the cognitive hearing science, aiding researchers to better plan experiments and synthesize knowledge across experiments and sites (for instance, enabling meta-analysis and multi-centered studies)  (Heinrich & Knight, 2020).
The expression of AMH and to a lesser extent NUP107 is slightly increased in DSD EBs compared to control EBs 46,XX, but without statistical significance. The NUP107 variant (exon 12) is present at a low frequency in the gnomAD database (2. 122 10 -5 ) and represents a potential variant of interest. A rare homozygous missense variant in exon 12 of the NUP107 gene (c. 1063C>T, p. R355C) has been reported in two sisters affected with primary amenorrhea and hypergonadotropic hypogonadism  37. However, the results do not support a disruption of NUP107 gene expression at least at PGCs level. AMH plays a critical role in the normal sexual differentiation and can be useful in the initial evaluation of DSD suspicion in childhood. But AMH expression in the diagnosis of DSD at adult age has not been to date investigated.
The following probes were used, in accordance with the manufacturer's recommendations: centromeric probes specific for chromosomes 18, X and Y (Vysis) and SRY probes [The probe mix also contains control probes for the X centromere (DXZ1), and for chromosome Y (DYZ1, the heterochromatic block at Yq12)] (Cytocell). Images were created with the CytoVision 7. 0 Software, Leica Biosystems. Genomic DNA was isolated from both patient's peripheral blood and from derived iPSCs, with the Maxwell® 16 Blood DNA Purification Kit (Promega, Biotech). The concentration and quality of the extracted DNA were evaluated with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies). Genomic imbalances from patient 1 and 2 were analyzed by oligonucleotide based array-comparative genomic hybridization analysis (array-CGH).