dataesr/scientific-paragraphs-categorization huggingface dataset:
A short description of different algorithms is provided, such as default parameters commonly used based in the published literature. For legume species, users simply need to upload their probeset or gene ID list and use checkboxes to select which transcriptomic samples they wish to use to build their GRNs. Output. After calculation, GRN prediction results are saved into the LegumeGRN web server according to the users' account. This feature allows users to store and keep track of their analyses and results. From the ''analysis history'' tab, users can retrieve all their prediction analyses with information related to analysis date, job title and description (optional), the transcriptomic dataset and parameters of predictive algorithms. At this step, users can download the network results and their related annotation information as tab-delimited text files or analyse them using an intuitive web-based GRN viewer to display the prediction results as graphical output.
Case Study 1 One of the DREAM challenges (i. e. , DREAM5) is to reverse engineer gene regulatory networks from gene expression datasets (http://wiki. c2b2. columbia. edu/dream/index. php/D5c4). To validate the performance of our web site, we tested it using three datasets from the DREAM5 in silico network inference challenges. Two are experimental datasets obtained from microorganisms, E. coli and S. cerevisiae. The third one is based on an artificial network, which is a simulation dataset and derived from GeneNetWeaver  [9]. We listed these three datasets in Table  1. Network predictions were evaluated on a subset of known interactions for each organism, or on the known network for the artificial case. Using LegumeGRN, evaluation of these datasets was performed to assess the performances of all six algorithms and their related integrative analysis for GRN combination.
We selected the top 100,000 edges returned from each approach according to their confidence rankings for AUC calculations. The AUPR and AUROC values of each approach are listed in Table  2. From Table  2 , we observed that not a single method achieved best performance from all tests. However, the integrative GRN prediction results always achieved best or second best performance across all three datasets. It suggests that integrative analysis performed more stably than individual GRN prediction algorithms. The AUC scores are consistent with the test results reported in previous study  [9]. We further evaluated the accuracies of directionality prediction using the PLPC algorithm, which is the only one for predicting causal relationships. In Table  3 , we listed the total edges, the true positives (TPs) and the true positives with correct directionalities predicted by PLPC for all three datasets.
Here, we used a = 0. 01 and ord = 8 as the input parameter. In all three cases, the directions for most of the edges can be predicted. Interestingly, ratios between TPs with correct directions and TPs are high. This indicates that PLPC has high chance to identify the correct regulation directionalities if edges are true positive. For the artificial network, we observed 30. 1% precision ( = N TP / (N TP +N FP )) for the directed structure. The results showed that PLPC is a promising approach to discover causal directions when interactions are true. For the E. coli and S. cerevisiae datasets, directionality accuracy is still low in comparison to the artificial dataset, however performances are still reasonable when compared to other algorithms. Note that for these two experimental datasets, not all true regulatory relationships have been identified, thus the false positives may also represent true unknown relations.
Case Study 2 In order to validate our web server on a legume dataset, we performed a co-expression analysis to identify putative target genes in M. truncatula of a well-studied transcription factor, ABSCISIC ACID INSENSITIVE3 (ABI3). ABI3 is known as a master regulator of seed maturation, which controls seed filling mechanisms and preparation for desiccation. This gene has been intensively studied in Arabidopsis and 78 of the ABI3 regulons have been experimentally identified using chromatin-immunoprecipitation (ChIP-chip), array-based transcriptome, quantitative reverse-transcription-PCR (qRT-PCR) and transient promoter activation analyses  [38]. These regulons encode proteins with known domains, which assigned them to various functions such as seed storage proteins, late embryogenesis abundant proteins, stress proteins, beta-glucosidases, or cytochrome P450s.
e. , BLASTX against IMGAG-v3. 5, Swiss-Prot and TAIR9 datasets), we discovered protein domains and putative functions for 33 of them. Out of these 33 genes, 69. 9% (i. e. , 23/33) encode proteins, whose putative functions were shown to be regulated by ABI3 in the plant model Arabidopsis  [38]  (Figure  2 ). Moreover, our predicted network identified two other transcription factors as regulons of MtABI3, FUSCA3 (FUS3) and DELAY OF GERMINATION1 (DOG1). These two genes have not yet been described as direct targets of ABI3 but known to encode genes related to abscisic acid physiology and may act synergistically with ABI3 to regulate an overlapping set of targets  [38, 39, 40].
Discussion LegumeGRN provides one-stop services for biologists to predict GRNs using cutting-edge algorithms, who receive results in a userfriendly and intuitive visualization interface. Two case studies demonstrated that this web site is promising to identify the in vivo regulatory genetic networks. Although our web tool is unique in allowing customization of data input, analysis algorithms and result visualization for the biologist with no bioinformatics training, there is still room to improve the performance of the GRN prediction in LegumeGRN. Even though GRN predictions on the artificial dataset (i. e. , in silico dataset from DREAM5) performed well, predictions from microorganism or plant species datasets are less accurate presumably due to the more complex regulatory relationships. From the biological side, several solutions should overcome these problems in future, such as the increase of data resources, identification of cis-regulatory elements on promoter sequences and mutant gene expression data from plants impaired in gene expression.
Supporting Information Figure  S1  The PCA plot for the major organs (flower, leaf, nodule and root) for Medicago dataset. We selected samples from same tissues but carried out by different labs for these four organs and removed control probesets, then, PCA on probesets were performed. The sample names used in PCA are: Flower, Flower 12 wk, Leaf, Leaf GUS-ox, Leaf IRG1 R108, Nod 14 dpi, Nod 14 dpi C, Root, Root A17 control, Root watered 4d. (PDF) For the PLPC algorithm, the numbers of predicted edges, predicted directed edges (in parenthesis), true positives and true edges with correct directionalities (listed in parenthesis) were listed. doi:10. 1371/journal. pone. 0067434. t003.
Until recently, imaging the infant brain was very challenging. Functional Near InfraRed Spectroscopy (fNIRS) is a promising, relatively novel technique, whose use is rapidly expanding. As an emergent field, it is particularly important to share methodological knowledge to ensure replicable and robust results. In this paper, we present a community-augmented database which will facilitate precisely this exchange. We tabulated articles and theses reporting empirical fNIRS research carried out on infants below three years of age along several methodological variables. The resulting spreadsheet has been uploaded in a format allowing individuals to continue adding new results, and download the most recent version of the table. Thus, this database is ideal to carry out systematic reviews. We illustrate its academic utility by focusing on the factors affecting three key variables: infant attrition, the reliability of oxygenated and deoxygenated responses, and signal-to-noise ratios.
However, while the community gathers enough principled evidence, a first approach is to estimate the most appropriate empirical answers on the basis of existing research. In other words, while we may not yet understand why certain parameter combinations 'work better' than others, we might be able to share this knowledge with others, to help them avoid the same errors we have made. Our goal was to pool such indirect information on the performance of specific methodological choices into a database, from which both newcomers and experienced users can draw empirical generalizations that may inform their know-how. To this end, we tabulated studies that had previously been published (see below for details on the inclusion criteria); but we wanted to go beyond a meta-analysis. In such a quickly growing field as infant fNIRS, we deemed it likely that whatever generalization we may draw now might soon become either limited (because it did not take into account additional factors) or incorrect (as new studies may show otherwise).
Therefore, we made use of tools that are currently freely available to create an online resource that can grow through independent contributions in a minimally supervised manner. In this report, we first introduce how we compiled studies into the database (henceforth DBIfNIRS). Thereafter, we explain how consumers of fNIRS literature can download the spreadsheet containing all coded studies, and describe more specifically how producers of infant fNIRS literature can make use of an online form in order to add recently published studies into the database. Then, we provide three examples of how DBIfNIRS can be used to inspire methodological research by experts, and to facilitate methodological decisions by users. The first case study suggests that attrition (i. e. , infant exclusion due to non-completion of the paradigm or poor data quality) remains a considerable roadblock to infant fNIRS research, although some age groups and technical configurations seem more promising than others.
The second case study investigates the highly debated question of whether oxygenated, deoxygenated, and total hemoglobin measures are equally reliable. The third assesses whether study design features pertaining to study duration lead to better results. We then summarize the strengths and weaknesses of this database, which could be ameliorated through more consistent reports in the literature. To this end, we lay out a guideline for infant fNIRS reporting in the final section.
google. com and a form was created. The website nucleates these materials, and it is associated with a blog where users can post comments to alert of inaccuracies and managers can announce changes to the database (such as corrections). As a final check, the website address was provided to 3 experts who have published in the infant fNIRS field, and they were asked to enter their latest paper, with no further instructions provided. This helped hone the interface and instructions. It should be noted at no stage did we assess risk of bias in individual studies. Since the database was intended to be a community resource, assessing bias for previous studies seemed unreasonable. The full PRISMA checklist and diagram are provided as Table  S1  and Figure  S1.
They can also email one of the supervisors, whose email will be clearly stated on the DBIfNIRS website. One can also contribute to DBIfNIRS by entering new studies using the form that is accessible from https://sites. google. com/ site/dbifnirs/. In order to contribute, we strongly recommend to first read through previous entries, as well as the online instructions and the comment forum, so as to ensure that their entry is consistent in criteria with those of previous contributors. To avoid frustration through dropped half-completed entries, producers should also make sure they have all the information they need before they start completing the form. Previous users have estimated it takes between 30 minutes and 2. 5 hours to enter one paper. The database can grow and prosper thanks to the attention and contribution of the community.
General overview On July 20, 2012, DBIfNIRS included 76 articles or theses, reporting results from 3557 infants distributed into 109 entries (which are defined on the basis of infant groups; see previous section for details). An additional 8 articles had been shortlisted but were discarded because they contained no fNIRS data (e. g. , reviews). For simplicity, we provide all numbers as percentages of entries. By far, the most common focus of interest was language (25%), followed by processing of auditory (13%), visual (12%) and face stimuli (11%). Other types of stimulation which made up at least 3% of the sample were motor, music, social, and pain. About 3% of the studies did not contain any external stimulation, as they focused on correlations across channels tapping different brain areas or the relationship between neural events recorded through EEG and fNIRS.
Thus, current expanses towards higher densities are being done at the expense of higher attrition rates. Furthermore, it should be noted that the attrition rate remains considerable even for studies using fewer than 19 optodes (median 36%; interquartile range 25%-52%). In other words, even with lighter headgears, more than a quarter of infants are typically excluded. Finally, in these data there was no indication of a reduction of attrition rate over the past 14 years. Our database design included two more factors whose effect on attrition we had hoped to investigate. One of them related to study design, and involved the compromise of longer and more repetitive stimuli presentation to get better signal-to-noise ratios and higher attrition rates due precisely to this study design feature. We will breach those findings after introducing our results on the hemodynamic response in the next section.
However, long and repetitive blocks are likely to result in restless infants, and they may result in weaker average activation levels due to habituation. Therefore, when designing a study, it would be important to have a rough approximation of what is a good compromise between these two goals. We calculated a number of measures that would be relevant to this question, including a proxy for study duration or amount of data as the duration of block plus baseline multiplied by the minimum number of blocks. In terms of data quality, we did the sum of oxy-Hb and deoxy-Hb patterns; a 2 would indicate a study with a typical hemodynamic response, a 22 one with an inverted response. Our hope was that we would find a minimum duration of stimulation, a minimum interstimulus interval, and a minimum number of blocks that virtually ensured a normal hemodynamic response, with as low an attrition rate as possible.
Unfortunately, we saw no clear patterns in the current DBIfNIRS one way or another. This may have to do with the difficulty of defining stimulation and baseline in cases where 'blocks' consist of a train of stimulation; or it may be due to the crude measures of signal-to-noise ratio used (a ternary division for each of oxy-Hb and deoxy-Hb). In addition, some data would need to be reported more consistently, such as how many blocks infants had to complete in order to be included (reported in about 60% of the entries). This is one area in which the field as a whole needs to contribute, by becoming more consistent in terms of the variables reported. In section 5, we lay out guidelines as to how this could be done.
Conclusions In this paper, we presented the database DBIfNIRS, and assessed its utility by presenting the factors relating to three variables: infant attrition rate, the reliability of oxygenated and deoxygenated responses, and signal-to-noise ratios. Analyses of the first two variables illustrated the strengths of DBIfNIRS, while the signal-to-noise ratio analysis illustrated the limitations of the database. We found that the attrition rates of 2-to 4-month-olds are usually higher than newborns or 5-to 8-month-olds, suggesting that at certain points of development infants are ideally still and alert to facilitate fNIRS testing. Analyses of attrition rate in relation to the number of optodes suggested that infant attrition rate may also depend on the lightness of headgear. In studies where headsets had more than 20 optodes, the attrition rates were inordinately high, such that less than half of the infants could be included in the final analyses.
It should be noted that the attrition rates of studies with fewer than 19 optodes were also considerable: In most studies, more than a quarter of infants tested were excluded. In our case study of reliability of different hemoglobin measures, we found that most of the erratic behaviors of oxy-Hb and deoxy-Hb were reported with high lower-end wavelengths of about 770-780 nm, and possibly with powers above 1. 5 mW per physical source. As for signal-to-noise ratios, no clear patterns emerged from the inspection of available data, partially due to inconsistencies in data reporting and the difficulty of deciding on how to measure 'signal' and 'noise' in published research. The results of our case studies underlined some relevant variables that could affect data quality. Therefore, analyses of published studies using DBIfNIRS could provide some insights to help fNIRS researchers make methodological choices when designing future studies.
doi:10. 1371/journal. pone. 0058906. t002 but potentially also in terms of contents or research themes. Inputting one's study in the DBIfNIRS, and seeing others' studies, would provide timely information on who's working in one's field, and facilitate cooperation. Another advantage of the DBIfNIRS is that if producers of infant fNIRS literature enter their own data, the research community might be able to access information which is not reported in their papers. Finally, by virtue of being a community resource, the database does not rely on one or a few individuals micro-managing it. Instead, DBIfNIRS can grow with the help of many, while remaining trustworthy thanks to the supervision of a few expert volunteers. Whilst there are numerous positive features in DBIfNIRS, there are also some limitations.
To begin with, it is almost impossible to avoid errors when inputting manually such large amounts of information. In the immediate future, this problem is addressed by ensuring a minimal supervision, to be done by experts who volunteer to manage the database for two years. In the longer term, however, it would be ideal that harvesting these data were done automatically from papers producing standardized reports, such as those outlined below. A second problem is overtesting, which affects not only DBIfNIRS, but any other meta-analytic resource. In this paper, we have concluded that e. g. optode number affected attrition rate, a difference that was highly significant. However, if everyone does t-test after t-test on the same database, significance values become more difficult to interpret. Thus, it should be clear from the start that this database is a first approach, a 'discovery sample' of sorts, and should be complemented with more principled analyses and ad hoc hypothesis testing.
A final potential problem is that the focus of the field changes; as new variables become important, other variables in the database could fade into the background. Our system of supervision allows DBIfNIRS to stay abreast of the field, as major structural changes can be implemented if needed. A major contribution of DBIfNIRS is that it outlines several aspects of our data that cannot be a part of the database because they need to be more consistently reported. We propose a set of strongly recommended and additional reporting guidelines, shown on Table  2. Following a set of guidelines such as these would increase comparability across studies, and facilitate the detection of more or less favorable settings. While this is desirable, standardization is most likely to be achieved if it is based on items that are widely agreed upon within the community.
-Turbulence: the turbulent dynamics and thermodynamics of the sub-cloud layer, cloud environment, and cloud edges including the entrainment process are investigated. Furthermore, the turbulent in-cloud mixing and its influence on the cloud microphysical properties are quantified by determining the governing timescales and dimensionless numbers. This article is intended as a general introduction and overview of the CARRIBA-project, as well as an illustration of unique aspects of the data set collected, for those interested in using the data, or considering similar measurement campaigns. After a classification of the weather situation observed during the two CARRIBA campaigns with emphasis on the cloud situation and the thermodynamic structure of the lower troposphere, a case study based on two days with nearly identical stratification, dynamic, and thermodynamic conditions is presented.
The ACTOS payload During the two CARRIBA field campaigns, helicopter-borne measurements were performed with ACTOS  (Siebert et al. , 2006a)  which is an autonomous measurement payload carried by means of a 140 m long kevlar cable as external cargo. The long rope and a typical true airspeed of 20 m s -1 of the helicopter avoid any measurable influence of the rotor downwash. The overall length of ACTOS (Fig. 3 ) is about 5 m and the total weight is limited to 200 kg. The main body consists of five 19 standard racks in which most of the sensor electronics, power supplies, and data acquisition systems are located. The sensors themselves are located on a carbon fibre outrigger on the front of ACTOS so as to avoid possible aerodynamic influences. A passive tail unit keeps ACTOS aligned in the mean flow direction.
6d ). The mean q profiles measured with ACTOS, as well as with the RAMAN lidar, are 1 to 2 g kg -1 moister compared to the OP ECMWF profiles during both CARRIBA WET and CARRIBA DRY , especially in the SCL during April 2011 (Fig. 5a ). The data assimilation performed at ECMWF is based on radiosondes which are regularly launched from the Barbados airport. However, the location from where they are launched, together with the prevailing wind direction might cause a dry bias in the measured profiles. Because the AC-TOS profiles are derived from highly accurate fast dew point mirror measurements performed over the open ocean, and the RAMAN lidar is also located directly at the coastline, landocean differences, especially in the SCL, may play a role in explaining these differences. Because the mean ACTOS profiles are based on only one profile per flight and the water vapor field is highly variable in both space and time, one should keep in mind that ACTOS profiles likely do not represent the mean conditions during these months.
In situ data at Ragged Point and from ACTOS do well agree with the lidar observations. The slightly higher concentrations observed by ACTOS result from particles smaller than 80 nm. Again, aerosol which originated from biomass burning was partly influencing the situation over Barbados. During clean periods, for example around 21 November, often increased number concentrations of Aitken-mode particles (< 80 nm) were observed which were attributed to freshly produced particles. For CARRIBA DRY , we can divide the campaign into three major periods in terms of aerosol load. The first half of CARRIBA DRY was influenced by Saharan dust layers in a height up to 3 km height indicated by an increased volume depolarization ratio. This period was interrupted by a comparable clean period (8 to 12 April) which coincides with higher cloudiness but no precipitation.