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Ravens

Ravens are a genus of large, black birds known for their intelligence and problem-solving abilities.
These omnivorous corvids are found across the Northern Hemisphere, and are often associated with folklore and mythology.
Researchers studying raven behavior and cognition can leverage PubCompare.ai to easily locate relevant protocols from the literature, pre-prints, and patents, and utilize AI-driven comparisons to identify the most effective research approaches.
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Most cited protocols related to «Ravens»

1
Iterative Curation of Metabolic Model
The first draft model based on homology to template models contained gaps due to incorrect annotation in the template models and lacked reactions in parts of metabolism that were unique to P. chrysogenum (Figure 8). Therefore, another draft model was generated from KEGG using the RAVEN Toolbox. An E-value <1e-50 was used as cut off in the gene assignment. This model was used for filling gaps in the draft network and for suggesting metabolic pathways that were not included based on the template models. No reactions were included based solely on presence in the KEGG model, and gene assignments were only included after careful manual validation against the NCBI RefSeq database.
Gaps in the draft metabolic network were identified using the gap finding capabilities of the RAVEN Toolbox (Figure 8). The initial network was rather disconnected and biomass production from glucose was not possible. Firstly, the software was used to identify which metabolites had to be connected in order to produce biomass. This resulted in the addition of some spontaneous reactions from the template models. The second step was to ensure that as many metabolites as possible could be produced. When non-connected metabolites were identified, the KEGG model was queried for candidate reactions which could connect that metabolite. A targeted literature search was then conducted to find evidence for the presence of such a reaction. In only few cases this resulted in the addition of transport reactions based solely on connectivity issues (<2%, see Figure 5). The final step was to use the RAVEN Toolbox to identify reactions that could not carry a flux during growth on any of the available carbon sources. There are, however, sets of reactions that are included in the model even though they cannot currently carry a flux. One such example is the synthesis and loading of tRNA, which is included to allow for possible future extension of the model to cover protein synthesis.
Agren R., Liu L., Shoaie S., Vongsangnak W., Nookaew I, & Nielsen J. (2013). The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum. PLoS Computational Biology, 9(3), e1002980.
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Publication 2013
Anabolism Carbon Genes Glucose Metabolic Networks Metabolism Protein Biosynthesis Ravens Transfer RNA
2
Comprehensive Cognitive Battery Assessment
The Boston participants also completed the Boston cognitive battery, in person, in about 90 minutes, within two years (mean = 18 months) after the BTACT was administered. The Boston cognitive battery includes four factors based on a confirmatory factor analysis (Miller & Lachman, 2000 ) using the cognitive tests which were administered at both Time 1 and Time 2. The factors and their test markers are Short-term Memory [measured with Forward Digit Span, Backward Digit Span (from Wechsler Adult Intelligence Scale, WAIS, Wechsler, 1955 ) and Serial 7s (Folstein et al., 1975 (link))], Verbal Ability (WAIS, Vocabulary, Wechsler, 1955 ), Reasoning [Letter Series (Schaie, 1985 ) and Ravens Advanced Progressive Matrices (Raven, Raven, & Court, 1991 )], and Speed [Letter Comparison (Salthouse, Kausler, & Saults, 1990 ) and Digit Symbol Substitution (Wechsler, 1955 )]. Factor scores for each factor were computed as the mean of the z-scores for the measures in that factor. Additional measures given to a subset of the sample at Time 2 included: the "How Many, What Number" task switching (Cepeda et al., 2001 (link)), trail-making (Reitan, Wolfson, Wedding, Horton, & Webster, 1986 ), letter number sequencing (Wechsler, 1997 ), and logical memory (Wechsler, 1997 ).
Lachman M.E., Agrigoroaei S., Tun P.A, & Weaver S.L. (2013). Monitoring Cognitive Functioning: Psychometric Properties of the Brief Test of Adult Cognition by Telephone (BTACT). Assessment, 21(4), 404-417.
Publication 2013
Adult Cognition Cognitive Testing Fingers Memory Memory, Short-Term Ravens TNFSF10 protein, human Wechsler Scales
3
Maternal Risk Factors Assessment for FASD
Development and behavior were assessed in Tier III with: Tests of the Reception of Grammar (TROG), a measure of verbal IQ (Bishop, 1989 ); Colored Progressive Matrices (Raven, 1981 ) for non-verbal IQ; the WISC-IV Digit-Span Scaled Score (Wechsler, 2003 ) for executive functioning; and the Teacher Report Form for problem behaviors (Achenbach and Rescorla, 2001 ).
The mothers of randomly-selected controls were the maternal controls. All maternal risk interviews were administered in the field by experienced, Afrikaans-speaking staff. Multiple items were carefully sequenced to enhance sensitivity and maximize accurate reporting. They covered: general health, reproduction, nutrition, alcohol use, socio-economic status (SES), and physical measurements. Drinking questions followed a timeline, follow-back sequence (Sobel et al., 1988 (link); 2001 (link)), and used vessels methodology pictures tailored to the common, local community alcohol products and drinking practices (Kaskutas and Graves, 2000 (link); 2001 (link); Kaskutas and Kerr, 2008 ). A seven-day, retrospective drinking log of alcohol consumption during the week preceding the interview was embedded into the nutrition questions. Current drinking data establish a baseline understanding of alcohol use and aid accurate calibration of drinking quantity, frequency, and timing (during pregnancy) for subsequent questions regarding alcohol use: 3 months prior to the index pregnancy, during the pregnancy(for each trimester by weekend, by weekdays, and by month) (May et al., 2000 (link), 2005 (link); 2007 (link); 2008a ,b ; Viljoen et al., 2002 (link)). This sequencing minimizes under-reporting (Alvik et al., 2006 (link)). Retrospective reports of alcohol use during pregnancy are considered more accurate for determining prenatal drinking levels than those reported during the prenatal period (Czarnecki et al., 1990 (link); Hannigan et al., 2010 (link)). These methods, sequencing, and contextual frameworks work well, especially when embedded within a dietary inventory (King, 1994 (link)).
Information on maternal risk factors for the index pregnancies was gathered for 377 women. All but 13 mothers of cases and controls were interviewed: 5 (1.3%) had moved, and 8 (2.1%) refused. Some data regarding alcohol consumption during the index pregnancy (17.2% of cases) were obtained via collaterals (usually relatives). Maternal data presented here focus primarily on confirmation of maternal drinking for case diagnosis in the epidemiological study while other maternal risk factors for this community have been reported elsewhere (May et al., 2005 (link); 2008a ,b ). Alcohol use during the index pregnancy was confirmed directly or through collateral sources in 100% of the ARND cases. Nine of the 68 (13.2%) FAS cases, and 6 of the 52 PFAS (17.1%) cases were diagnosed without confirmation of prenatal drinking.
Tobacco use data current and prenatal were also obtained in the interviews. In earlier community trials we determined that each cigarette averaged one gram of tobacco (May et al., 2000 (link); Viljoen et al., 2002 (link)), similar to machine-rolled cigarettes in the U.S.A. (http://www.cancer.gov/cancertopics/factsheet/Tobacco/cigars).
May P.A., Blankenship J., Marais A.S., Gossage J.P., Kalberg W.O., Barnard R., De Vries M., Robinson L.K., Adnams C.M., Buckley D., Manning M., Jones K.L., Parry C., Hoyme H.E, & Seedat S. (2012). APPROACHING THE PREVALENCE OF THE FULL SPECTRUM OF FETAL ALCOHOL SPECTRUM DISORDERS IN A SOUTH AFRICAN POPULATION-BASED STUDY. Alcoholism, clinical and experimental research, 37(5), 818-830.
Publication 2012
Alcohols Blood Vessel Diet Fingers Hypersensitivity Malignant Neoplasms Mothers Physical Examination Pregnancy Problem Behavior Ravens Reproduction TimeLine Tobacco Products Woman
4
Combining MetaCyc and KEGG Models
To capitalize on the complementary information from MetaCyc- and KEGG-based reconstructions, RAVEN 2.0 facilitates combining draft models from both approaches into one unified draft reconstruction (Fig 2). Prior to combining, reactions shared by MetaCyc- and KEGG-based reconstructions are mapped using MetaCyc-provided cross-references to their respective KEGG counterparts (S12 Table). Additional reactions are associated by linkMetaCycKEGGRxns through matching the metabolites, aided by cross-references between MetaCyc and KEGG identifiers (S13 Table). Subsequently, the combineMetaCycKEGGModels function thoroughly queries the two models for identical reactions, discarding the KEGG versions while keeping the corresponding MetaCyc reactions. In the combined model, MetaCyc naming convention is preferentially used such that unique metabolites and reactions from KEGG-based draft model are replaced with their MetaCyc equivalents whenever possible. The combined draft model works as a starting point for additional manual curation, to result in a high-quality reconstruction.
Wang H., Marcišauskas S., Sánchez B.J., Domenzain I., Hermansson D., Agren R., Nielsen J, & Kerkhoven E.J. (2018). RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Computational Biology, 14(10), e1006541.
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Publication 2018
Conferences Ravens Reconstructive Surgical Procedures
5
Trycycler Assembler Performance Evaluation
Each of the five Trycycler testers was given the ONT rapid read set for the six genomes used in the real-read tests (all real genomes excluding Serratia marcescens 17-147-1671) and produced one Trycycler assembly (without Medaka or Pilon polishing) for each. The number of input assemblies and which assemblers were used are available in Additional file 4: Tester assemblers. We then compared the assemblies produced by single tools (Flye, Raven, and Miniasm/Minipolish), by Trycycler (from the developer and the five testers), and a hybrid-assembled reference (the developer’s Trycycler+Medaka+Pilon assembly).
For each genome, we clustered the contigs from all assemblies (using Trycycler cluster), and using the developer’s Trycycler assembly as the reference, we classified the genome replicons for each assembly as either present, present with misassemblies, or absent (Additional file 4: Matrix). Each chromosome was rotated to a consistent starting position and a multiple sequence alignment was performed (using Trycycler MSA). We then extracted pairwise distances from the alignment (using the msa_to_distance_matrix.py script, available in Supplementary data) and built a FastME [28 (link)] tree from the distances. The distances were then normalized to the genome size (using the normalise_distance_matrix_to_mbp.py script, available in Supplementary data) to quantify the differences between each assembled chromosome for each of the genomes.
Wick R.R., Judd L.M., Cerdeira L.T., Hawkey J., Méric G., Vezina B., Wyres K.L, & Holt K.E. (2021). Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biology, 22, 266.
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Publication 2021
Chromosomes Genome Hybrids Oryzias latipes Quickset cement Ravens Replicon Sequence Alignment Serratia marcescens Trees

Most recents protocols related to «Ravens»

1
Hybrid Assembly and Validation of Aerobactin-Encoding Plasmids
Isolates that carried aerobactin were considered for long-read sequencing. To capture the diversity of the putative aerobactin-encoding plasmids, 16 isolates were selected for Oxford Nanopore sequencing based on the diversity in STs and AbSTs, 4 of which were from Thailand. DNA was extracted from pure cultures using the GenFind v3 kit (Beckman Coulter Life Sciences) on a Biomek i7 instrument, using the protocol ‘DNA extraction from bacteria using GenFind v3’. Library preparation was done using the SQK-LSK109 ligation sequencing kit. The sequencing was performed on an Oxford Nanopore GridION instrument (Oxford Nanopore Technologies), using a MinION R9.4.1 flow cell. Guppy version 5.0.14 (Oxford Nanopore Technologies) was used for basecalling and demultiplexing, using the super-accuracy basecalling model.
The genomes that were both Illumina and Nanopore sequenced were subjected to hybrid assembly. First, the long reads were quality controlled with NanoPlot [31 (link)] version 1.33.1. Then, Filtlong [32 ] version 0.2.0 was used to discard the lowest 10 % of reads based on length and quality. Unicycler was used to generate hybrid assemblies based on the filtered long reads and untrimmed Illumina reads. If the hybrid assembly failed, Filtlong was run again and set to remove the lowest 20 % of reads.
Genomes that were either incomplete after hybrid assembly or failed to assemble twice were subjected to long-read assembly and consensus analysis using Trycycler [33 (link)] version 0.5.1. Briefly, Trycycler was used to generate 12 subsets of reads, where each set of four subsets were independently assembled using Minipolish [34 (link)] version 0.1.2, Flye [35 (link)] version 2.9 and Raven (https://github.com/lbcb-sci/raven) version 1.6.1. Then, the contigs were clustered, and potential outliers were removed. If a cluster was represented by less than four contigs, a new subset of 24 read sets were generated and assembled as above. The contig clusters were then reconciled and aligned, before a consensus was made. The resulting long-read assembly was polished by using Medaka (Oxford Nanopore Technologies) version 1.4.4 and two rounds of Pilon [36 (link)] version 1.23.
Kaspersen H., Franklin-Alming F.V., Hetland M.A., Bernhoff E., Löhr I.H., Jiwakanon J., Urdahl A.M., Leangapichart T, & Sunde M. (2023). Highly conserved composite transposon harbouring aerobactin iuc3 in Klebsiella pneumoniae from pigs. Microbial Genomics, 9(2), mgen000960.
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Publication 2023
aerobactin Cells DNA, Bacterial DNA Library Genome Hybrids Lebistes Ligation Oryziinae Plasmids Ravens
2
Validating the Two Human Figures Test for Emotional Screening
The instrument used was the Two Human Figures test (T2F) of Maganto and Garaigordobil (2009b) . It’s a psychometric proposal for the graphic projective test Draw-A-Person (DAP), from the developmental and projective perspectives giving it greater validity and reliability. The scoring system is based on frequency of items in a human drawing. Which is described for the authors, as a screening instrument to be used in clinical, educational and social settings to identify children with developmental (52 Indicators) and emotional problems (35 Indicators). For the purpose of this study, the drawings were coded only using the 35 Emotional Indicators scale. Participants were asked to draw a person, on a sheet of Din A4 paper which they had been handed previously, along with a pencil and rubber and with no time limit. Once they had finished, they were given a second sheet of paper and is requested to draw a person of the opposite sex. For the youngest children, the instruction was to draw a boy or girl, according with Machover (1953) (link).
Using Spanish samples of 1,222 and 1,623 participants aged 5 to 12, results showed that the instrument was both reliable and valid for to identify developmental and emotional problems (Maganto and Garaigordobil, 2009a ). Regarding the Developmental Indicators scale (52 items), it is based on assigning standardised scores when development indicators are present, and the resulting value is then transformed into percentiles for each age and sex. Two criteria to accept these items were agreed upon: (1) the item was developmental; that is, its frequency in drawing increases as the subjects get older and (2) it correlates with intelligence evaluated by Raven (1995) . To check this, contingency analyses were performed by calculating the Chi-square by ages and age groups for both the male and the female human figure drawings, and Pearson correlations were performed between the scores earned on the T2F and Raven. The results revealed significant correlations (p < 0.05) between the variables, confirming the validity of the test. The Cronbach’s coefficient (0.86) and the Spearman-Brown (0.86) were also calculated and found satisfactory.
Regarding the Emotional Indicators scale (35 items), these Emotional Indicators meet three criteria (Tuset and Fernández, 2017 (link)): (1) they distinguish between clinical and non-clinical groups, (2) they are not developmental, and (3) they are unusual at any age (frequency under 10%). Sixty indicators were initially chosen, but the Chi-squared contingency analysis of Pearson for each of the figures, between the clinical and non-clinical sample, concluded that statistically significant differences were only found in 35 of the emotional indicators. Furthermore, the analyses performed between emotional items and age showed a negative covariation, in that as development advances, the representation of those emotional indicators drops. This enabled us to conclude after what age these items should be considered emotional indicators. Therefore, of the 35 emotional indicators, 23 indicators are common to all ages, 6 indicators are applied from the age of 7 onwards and another 6 indicators from the age of 9 onwards.
These Emotional Indicators, according to the T2F’s authors, need to be interpreted with two complementary aspects taken into account: (1) Number of indicators and (2) Types of emotional indicators present. As for (1) Number of indicators, the assessment is quantitative based on the application of cut-off points according to the percentile, aforementioned (Table 1), which determine whether or not the subject presents emotional problems (75th percentile: points to the possible existence of emotional problems; 85th percentile: considered a high level of probability of the existence of emotional problems; 95th percentile: confirms the existence of emotional problems). The assessment of (2) types of indicators, is qualitative based on the review of literature from experts in the field carried out by Maganto and Garaigordobil (2009b) . Moreover, the authors also point out that some emotional indicators have particular clinical-emotional relevance. Within the 23 indicators common to all ages, these indicators are the following: (1) Bizarre, unreal, grotesque or monster figure, (14) Genitals or sexual characteristics, (19) No eyes, (20) No mouth, and (21) No body. And the six indicators applied from the age of 7, include the following: (25) Leaning figure.
Ballús E., Comelles M.C., Pasto M.T, & Benedico P. (2023). Children’s drawings as a projective tool to explore and prevent experiences of mistreatment and/or sexual abuse. Frontiers in Psychology, 14, 1002864.
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Publication 2023
Age Groups Child Child Development Emotions Euphoria Eye Females Genitalia Hispanic or Latino Homo sapiens Human Body Males Oral Cavity Projective Techniques Psychometrics Ravens Rubber Woman
3
Assessing Children's Non-Verbal Intelligence
Raven’s Colored Progressive Matrices (Raven and Court, 1998 ) was administered to assess children’s non-verbal intelligence.
Luo L, & Su I.F. (2023). Meta-linguistic awareness skills in Chinese-speaking children with hyperlexia: A single-case study. Frontiers in Psychology, 14, 1049775.
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Publication 2023
Child Ravens
4
Longitudinal Cognitive Development Assessment
We conducted linear growth models (Singer & Willett, 2003 ) to address our RQ1. More specifically, we examined whether children’s assessment scores, including Raven, Corsi, mental rotation, and Panamath tasks, changed across three time points over six months. Given the small sample size, we only used unconditional growth models without covariates. HLM 7.03 software program (Raudenbush & Bryk, 2002 ; Raudenbush & Congdon, 2021 ) was used for conducting linear growth models. The growth model is defined as follows: Level-1(intra-individuallevel):Yti=π0i+π1iTime+eti Level-2(inter-individuallevel):π0i=β00+r0i;π1i=β10+r1i where Yti denotes the outcome at time t for child i; π0i is the intercept of the growth trajectory for child i; π1i is the growth rate for child i; time denotes the indicator of data collection waves (Time = 1, 2, 3); β00 is the average of level-1 intercepts; β10 is the average of level-1 growth rates; eti is the level-1 residuals; and r0i and r1i are level-2 residuals for the intercept and growth rate, separately.
Thematic analysis (Braun & Clarke, 2013 ) was used for analyzing the interview data, which intended to address our RQ2. We followed a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. The audio interview data were transcribed to text first and then were analyzed by NVivo 12 software program (QSR International Pty Ltd., 2020 ). For the interview conducted in Mandarin, the Chinese transcript was first translated into English by a transcription software (https://otter.ai/); two RAs independently checked and corrected errors in the translation and then went over all the translations with a senior researcher, who had English translation training, to discuss the consistency and accuracy of the translations. If there were any conflicts, the senior researcher resolved the issue with two RAs together.
Eye-tracking data were analyzed to address our RQ3, which were coded by iMotion software (https://imotions.com/eye-tracking/) and analyzed via data visualization using ggplot2 R package (Wickham, 2016 ). Given the small sample size from this subset of data, we did not conduct any statistical analysis. We used line chart to visualize the eye-tracking measures over time and compared all the measures between two locations.
Liu Y., Odic D., Tang X., Ma A., Laricheva M., Chen G., Wu S., Niu M., Guo Y, & Milner-Bolotin M. (2023). Effects of Robotics Education on Young Children’s Cognitive Development: a Pilot Study with Eye-Tracking. Journal of Science Education and Technology, 32(3), 295-308.
Publication 2023
Child Chinese Otters Ravens Singer Transcription, Genetic
5
Assessing General Intelligence with Raven's Matrices
Raven Progressive Matrices tests (Raven & Raven, 2003 ) were designed to assess general intelligence and abstract reasoning. In these tests, children were asked to look at a geometric pattern with a missing piece and fill in this missing piece by choosing one out of six options (Fig. 1). A total of 24 images were presented to children in an increasing order of difficulty. Previous studies demonstrated satisfactory reliability and validity, with a test–retest reliability of 0.85 and moderately high correlations ranging from 0.54 to 0.86 between the Raven tests and Binet and Wechsler Intelligence scales (Raven & Raven, 2003 ).

An example of Raven Progressive Matrices Tests

Liu Y., Odic D., Tang X., Ma A., Laricheva M., Chen G., Wu S., Niu M., Guo Y, & Milner-Bolotin M. (2023). Effects of Robotics Education on Young Children’s Cognitive Development: a Pilot Study with Eye-Tracking. Journal of Science Education and Technology, 32(3), 295-308.
Publication 2023
Child Ravens Wechsler Scales

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