Log In Sign up
The largest database of trusted experimental protocols
> Anatomy > Body Location or Region > Chin

Chin

The chin is the prominent, lower part of the human face located below the mouth.
It is composed of the bony structure of the mandible and overlying soft tissues.
The chin plays an important role in facial aesthetics and can be affected by congenital conditions, trauma, or aging.
Studying the chin and related structures is important for fields such as plastic surgery, orthodontics, and anthropology.
PubCompare.ai can help streamline chin research by quickly locating the best scientific protocols from literature, preprints, and patents using AI-driven comparisons to enhance reproducibility and research accuaracy.

Most cited protocols related to «Chin»

1
Canu Consensus Sequence Generation
Canu generates a consensus sequence for each contig using a modified version of the “pbdagcon” algorithm (Chin et al. 2013 (link)). Briefly, a template sequence is constructed for each contig by splicing reads together from approximate positions based on the best overlap path. This template is accurate within individual reads, as they have previously been error-corrected, but may have indel errors at read boundaries due to inaccuracy in the overlap positions. To correct this, all reads in the contig are aligned to the template sequence in parallel using Myers’ O(ND) algorithm (Myers 1986 ) and added to a DAG. The DAG is then used to call a consensus sequence as in the method described by Chin et al. (2013) (link).
Koren S., Walenz B.P., Berlin K., Miller J.R., Bergman N.H, & Phillippy A.M. (2017). Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Research, 27(5), 722-736.
Full text: Click here
Publication 2017
Chin Consensus Sequence INDEL Mutation
2
Canu's Approach to Correcting Repetitive Reads
Canu uses all-versus-all overlap information to correct individual reads. However, simply computing a consensus representation for each read using all overlaps could result in masking copy-specific repeat variants. Therefore, Canu uses two filtering steps to determine which overlaps should be selected to correct each individual read. The first is a global filter where each read chooses where it will supply correction evidence, and the second is a local filter where each read accepts or rejects the evidence supplied by other reads. This strategy attempts to overcome biases due to sequence quality and repeats. For example, reads with higher than average sequencing quality would tend to dominate the correction, regardless of if they were from the correct repeat copy. To prevent this, each read is only allowed to contribute to the correction of C other reads, where C is the expected read depth. The global filter scores each overlap (overlap_length * identity) and keeps only the C best overlaps for each read, thereby clustering repetitive reads with others likely to have originated from the same copy. When errors are uniformly distributed, we expect that reads are more likely to be grouped with reads from the same repeat copy, as they would have fewer total differences than reads from diverged repeat copies. A small fraction of misassigned reads is tolerable, as they will be outvoted during consensus correction. This strategy was first introduced by PBcR for the hierarchical correction and assembly of single-molecule reads (Koren et al. 2012 (link)). From this list, the local filter then selects the 2C best overlaps to each read for use in correction. The second filter is primarily a computational optimization.
Before computing the corrected sequence, the all-pair overlaps are used to predict the expected length of each read after correction (i.e., accounting for reads with partial or no overlaps). From these estimates, the longest reads up to a user-specified coverage depth are processed for correction. Corrected reads are generated using a modified implementation of the “falcon_sense” algorithm (Chin et al. 2016 (link)), which parallelizes the pairwise alignment step and removes the maximum read length limits. For a given read to be corrected, overlapping reads are aligned to it using Myers’ O(ND) algorithm (Myers 1986 ). A directed acyclic graph (DAG) is created from the alignments, and the highest weight path is followed to generate a corrected sequence (Chin et al. 2016 (link)). Edges with a weight less than four are omitted, which will split the original read when there is insufficient evidence for correction.
Koren S., Walenz B.P., Berlin K., Miller J.R., Bergman N.H, & Phillippy A.M. (2017). Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Research, 27(5), 722-736.
Full text: Click here
Publication 2017
Chin Strains
3
Canu Assembly of Arabidopsis Genome
All TrioCanu assemblies were downloaded from Koren et al. [10 (link)], available at https://obj.umiacs.umd.edu/marbl_publications/triobinning/index.html. The A. thaliana F1 FALCON-Unzip assembly was obtained from Chin et al. [9 (link)]. We generated a Canu assembly to show a typical example of an interim mixed-haplotype assembly that has not been polished or purged of haplotypic duplications. The same Pacific Bioscience reads were used for all three assemblies, obtained from Chin et al. [9 (link)].The Canu assembly was generated with Canu 1.9 release version using the following command:

canu -p canu -d athalF1_notrio genomeSize=130m 'corOutCoverage=100' 'batOptions=-dg 6 -db 6 -dr 1 -ca 500 -cp 50' 'batMemory=200' -pacbio-raw *.fastq.gz

Rhie A., Walenz B.P., Koren S, & Phillippy A.M. (2020). Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biology, 21, 245.
Full text: Click here
Publication 2020
Arabidopsis thalianas Chin Haplotypes
4
Identifying Inherited Haplotype Markers
Appropriate size of k was obtained as k = 18 for the A. thaliana F1 with 130~260 Mbp genome size and k = 21 for NA12878 with 3.2~6.4 Gbp genome size using $MERQURY/best_k.sh.
As parental Illumina sequencing was not available for this F1, the parental genome assemblies from Chin et al. [9 (link)] were used to obtain parental specific k-mers. Each assembly from the inbred Col-0 and Cvi-0 lines were downloaded from:
https://downloads.pacbcloud.com/public/dataset/PhasedDiploidAsmPaperData/FUNZIP-PhasedDiploidAssemblies.tgz.
Meryl databases for the parental strains were built directly with meryl count k=18 output $hap.meryl $hap.fasta for each haplotype assembly.
The parental Illumina whole-genome sequencing sets for NA12878 were downloaded from the high coverage dataset of the 1000 Genomes Project (NA12891 and NA12892) and combined with Illumina Platinum Genomes Project data from PRJEB3381. Illumina whole-genome sequencing of NA12878 was downloaded from PRJEB3381.
All Meryl databases from sequencing read sets were built with $MERQURY/_submit_build.sh. Once the k-mer databases were built, inherited hap-mers were obtained with $MERQURY/trio/hapmers.sh.
Rhie A., Walenz B.P., Koren S, & Phillippy A.M. (2020). Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biology, 21, 245.
Full text: Click here
Publication 2020
Chin Genome GPER protein, human Haplotypes Parent Platinum Strains TRIO protein, human
5
Screening for Obstructive Sleep Apnea in Preoperative Patients
The study was conducted in the preoperative clinics of Toronto Western Hospital and Mount Sinai Hospital, Toronto, Ontario, Canada. Institutional Review Board approvals were obtained from both institutions (MSH: 06-0143-E and 07-0183-E; UHN: 06-0135-AE and 07-0515-AE). Patients aged 18 yr or older, who were ASA I–IV, and were undergoing elective procedures in general surgery, gynaecology, orthopaedics, urology, plastic surgery, ophthalmology, or spinal surgery were included in the screening process and approached for consent by the research assistants for the preoperative polysomnograpy (PSG). Patients who were unwilling or unable to give informed consent or patients who were expected to have abnormal EEG findings (e.g. brain tumour, epilepsy surgery, patients with deep brain stimulator) were excluded.
All the patients were asked to complete the STOP questionnaire.11 (link) Information concerning BMI, age, neck circumference, and gender (Bang) were collected by a research assistant. In the initial 2 yr period of the study, the patients were invited to undergo a laboratory PSG. During the subsequent 2 yr of the study, the patients underwent a portable PSG study at home. The results of the PSG were used to evaluate the various scores of the STOP-Bang questionnaire.
The portable PSG was performed with a level 2 portable sleep device (Embletta X100) which is shown to be a reliable alternative for standard PSG in surgical patients.14 The PSG recordings were performed at the patients’ home. The recording montage consisted of two EEG channels (C3 and C4), electrooculogram (left or right), and chin muscle EMGs. Thoracic and abdominal respiratory effort bands, body position sensors, and pulse oximeter were also used.
The device was attached to patients by a well-trained PSG technician at their home and the overnight recordings were unattended. The patients were advised on how to remove the device which was picked up the next morning from the patients’ home by the same sleep technician. A certified PSG technologist who was blinded to the study information analysed the PSG. The manual scoring was performed using Somnologia Studio 5.0 as the scoring platform. Manual scoring was performed according to the Manual of the American Academy of Sleep Medicine.15 The laboratory PSG was performed overnight and patients went to bed at their usual bedtime. A standard EEG montage consisting of EEG, electrooculogram, submental EMG, and ECG obtained with surface electrodes were used to collect the sleep architectural data. A pulse oximeter measured the oxygen saturation. Additional recordings included the respiratory effort by thoraco-abdominal excursion, respiratory inductive plethysmography, and oronasal airflow.
A certified polysomnographic technologist scored the polysomnographic recordings under the supervision of a sleep physician who assessed and approved the reports. The technologist was blinded to the results of the STOP-Bang questionnaire and other clinical information about the patients. The sleep stages and apnoea–hypopnea index (AHI) were scored according to the American Academy of Sleep Medicine Task Force recommendations.16 (link)The diagnosis of OSA was based on an AHI >5 with fragmented sleep and daytime sleepiness. The severity of OSA with both laboratory and portable PSG was classified based on the AHI values: >5–15 as mild OSA, >15–30 as moderate OSA, and >30 as severe OSA.15 16 (link)
Chung F., Subramanyam R., Liao P., Sasaki E., Shapiro C, & Sun Y. (2012). High STOP-Bang score indicates a high probability of obstructive sleep apnoea. BJA: British Journal of Anaesthesia, 108(5), 768-775.
Publication 2012
Abdomen Apnea Brain Brain Neoplasms Chin Conditioning, Psychology Diagnosis Electrooculograms Epilepsy Ethics Committees, Research Gender Medical Devices Muscle Tissue Neck Operative Surgical Procedures Orthopedic Surgical Procedures Oxygen Saturation Patients Pharmaceutical Preparations Physicians Plastic Surgical Procedures Plethysmography Pulse Rate Respiratory Rate Sleep Sleep Stages Supervision

Most recents protocols related to «Chin»

1
Mental Health Service Utilization in Denmark
The Capital Region of Denmark has a population of 1.6 million people. Patients in the Capital Region of Denmark are referred by their general practitioner (GP) or other treatment providers to a central diagnostic and referral centre within the mental health services that yearly assesses 20.000 referrals. About 4000 patients are further evaluated in person and diagnosed by the centre.
Five mental health centres in the region provide treatment packages for first-episode depression and will include participants in the study. The Mental Health Centre Amager and the Copenhagen centre consisting of two clinics located in the City of Copenhagen and treat approximately half of all patients, whereas Ballerup and Glostrup treat approximately a third of patients in the surrounding suburb (Sup. Figure 2). The Psychiatric Centre Northern Zealand treats approximately 16% of patients and is located north of Copenhagen, in a region of intermediate urbanisation with individual municipalities classified as rural.
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
Full text: Click here
Publication 2023
Chin Mental Health Patients Urbanization
2
Identifying Septic Shock Hub Genes
The PPI network were constructed based on the DEGs of septic shock online in the STRING database (http://string-db.org/) (version 11.5) (Szklarczyk et al., 2021 (link)). Specifically, the gene list was input into the multiple protein mode with default parameters. The credibility was set to 0.40. Disconnected nodes were hidden. Then, the output table was input into Cytoscape software. CytoHubba, a Cytoscape plugin, was used to screen potential hub genes by providing 12 topological analysis algorithms (i.e., MCC, DMNC, MNC, Degree, EPC, Bottleneck, Eccentricity, Closeness, Radiality, Betweenness, Stress, and Clustering Coefficient) (Chin et al., 2014 (link)). In this research, genes appearing at least 5 times in the top 10 results of each algorithm were considered as potential hub genes.
Zhao S., Zhu K., Li X., Zhong X., Zhao Y., Le Z., Liu Z., Xiao Y., Lai D., Jiao N, & Shu Q. (2023). Co-expression and interaction network analysis reveals dysregulated neutrophil and T-cell activation as the core mechanism associated with septic shock. Frontiers in Genetics, 14, 1132361.
Full text: Click here
Publication 2023
Chin Genes Proteins Radius Septic Shock
3
Psychophysical Data Collection Setup
Psychophysical data were collected at the Center for Magnetic Resonance Research at the University of Minnesota. Data were collected on an Apple Mac Pro using an Eizo FlexScan SX2462W monitor with a 60 Hz refresh rate (mean luminance = 61.2 cd/m2). A Bits# stimulus processor (Cambridge Research Systems) was connected but not used for this particular experiment. Luminance values were linearized using a PR655 spectrophotometer (Photo Research). Head position was stabilized using an adjustable chin rest positioned at a viewing distance of 70 cm. Stimuli were generated and responses were collected using PsychoPy (Peirce et al., 2019 (link)).
Killebrew K.W., Moser H.R., Grant A.N., Marjańska M., Sponheim S.R, & Schallmo M.P. (2023). Faster bi-stable visual switching in psychosis. medRxiv.
Publication Preprint 2023
Chin Head Magnetic Resonance Imaging
4
Perinatal Depression Prevalence Meta-Analysis
For studies measuring depression at multiple times, one antenatal and one postnatal measurement were included and the measurements taken closest to birth were used, excluding the first 2 weeks postnatally (to allow for the postpartum blues).8 Prevalence estimates were extracted as raw proportions. Pooled estimates were calculated using a random-effects menta-analysis with a Freeman-Tukey double arcsine transformation to stabilize variance. Point estimates of proportions and their 95% CIs were calculated and displayed in forest plots. For pooled estimates, tau2 was used to estimate the between-study variance and the I2 statistic was used to quantify heterogeneity. High-risk populations were determined a priori and examined in subgroup analyses. These included individuals who, during the perinatal period, experienced war or conflict, natural disaster, or the COVID-19 pandemic, and adolescent individuals, individuals with HIV, and individuals who experience intimate partner violence (subcategorized as physical, psychological, sexual, and not specified). Data were extracted as raw proportions from studies reporting perinatal depression prevalence in these subgroups. Potential sources of heterogeneity were investigated with subgroup and sensitivity analyses, including the exclusions of studies with prevalence estimates less than 5% and more than 60%, and those deemed to be at high risk of bias. Meta-regression was used to examine the differences in prevalence estimates between groups.
An adapted version of the Newcastle-Ottawa Scale9 (eAppendix 3 in Supplement 1) was used to assess studies for bias across the domains of selection of participants, comparability of groups, and ascertainment of outcome. Each study was independently assessed for bias by 2 reviewers (A.R.M. and H.G.) and given a score out of 7 (eTable 1 in Supplement 1). Discrepancies were discussed in consultation with a third reviewer (R.H.). Publication bias was examined with funnel plots and Egger test. Stata IC version 17 (StataCorp) was used for the analysis. The study was registered with PROSPERO (CRD42021242901) and reported according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline10 (link) (eAppendix 4 in Supplement 1). Two-sided P values were statistically significant at less than .05.
Roddy Mitchell A., Gordon H., Lindquist A., Walker S.P., Homer C.S., Middleton A., Cluver C.A., Tong S, & Hastie R. (2023). Prevalence of Perinatal Depression in Low- and Middle-Income Countries: A Systematic Review and Meta-analysis. JAMA Psychiatry, 80(5), 425-431.
Full text: Click here
Publication 2023
Adolescent Childbirth Chin COVID 19 Dietary Supplements Forests Genetic Heterogeneity Hypersensitivity Natural Disasters Physical Examination Population at Risk
5
Protein Interaction Network Analysis
A database called Search Tool for the Retrieval of Interacting Genes (STRING) is used to study the functional protein association networks (Szklarczyk et al., 2017 (link)). The filtered DEGs had already been added to the STRING database. All PPI pairs with a cumulative score greater than 0.4 were retrieved. High-degree nodes seem to be essential for maintaining the network’s overall stability. The degree of all nodes was calculated by Cytoscape (v3.6.1) plugin cytoHubba using the MCC algorithm (Chin et al., 2014 (link)), in this experiment, the genes with the top 10 highest MCC score values were considered as hub genes.
Guo J., Feng S., Yu H., Ou B., Jiang D., Zhuang W., Ding C., Chen X., Zhang M., Ling Y., Zeng Y, & Qiu H. (2023). Transcriptomic study of gastrointestinal stromal tumors with liver metastasis. Frontiers in Genetics, 14, 1007135.
Full text: Click here
Publication 2023
Chin Genes MCC Gene Proteins

Top products related to «Chin»

1
Matlab by MathWorks
Sourced in United States, United Kingdom, Germany, Canada, Japan, Sweden, Austria, Morocco, Switzerland, Australia, Belgium, Italy, Netherlands, China, France, Denmark, Norway, Hungary, Malaysia, Israel, Finland, Spain
MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
2
Eyelink 1000 by SR Research
Sourced in Canada
The EyeLink 1000 is a high-performance eye tracker that provides precise and accurate eye movement data. It is capable of recording monocular or binocular eye position at sampling rates up to 2000 Hz. The system uses infrared illumination and video-based eye tracking technology to capture eye movements. The EyeLink 1000 is designed for use in a variety of research applications, including cognitive science, psychology, and human-computer interaction studies.
3
Eyelink 1000 plus by SR Research
Sourced in Canada, United States
The EyeLink 1000 Plus is a high-speed, video-based eye tracker that provides accurate and reliable eye movement data. It is designed for a wide range of applications, including research, usability testing, and clinical studies. The system uses infrared illumination and a digital video camera to track the user's eye and record its position over time.
4
Eyelink 1000 eye tracker by SR Research
Sourced in Canada
The Eyelink 1000 is a high-performance eye tracker manufactured by SR Research. It is capable of recording eye movements with high spatial and temporal resolution. The device uses infrared video-based technology to track the user's gaze and provide accurate data on eye position and pupil size.
5
Eyelink 1000 system by SR Research
Sourced in Canada
The EyeLink 1000 system is a high-performance eye tracking device designed for research applications. It provides accurate, real-time data on eye movements and gaze position. The system uses advanced optical and digital technologies to capture and analyze eye behavior. It is a versatile tool used in various fields of study, including psychology, neuroscience, and human-computer interaction.
6
Eyelink 2 by SR Research
Sourced in Canada
The EyeLink II is a high-performance eye-tracking system designed for precise and reliable monitoring of eye movements. It is capable of measuring the position and movement of the eyes with a high degree of accuracy and temporal resolution. The system is widely used in various research fields, including cognitive psychology, neuroscience, and human-computer interaction.
7
Cytohubba by Cytoscape
CytoHubba is a Cytoscape plugin that provides a suite of network analysis tools to identify key nodes and interactions within a biological network. The plugin offers centrality analysis, hub detection, and bottleneck identification functionalities to help users understand the topological and functional significance of network components.
8
Experiment builder software by SR Research
Sourced in Canada
Experiment Builder is a software suite designed for researchers to create and control experimental paradigms. The software provides a user-friendly interface for designing and configuring experiments, with the ability to integrate with various hardware and data collection systems.
9
Experiment builder by SR Research
Sourced in Canada, United States
Experiment Builder is a software tool designed to facilitate the creation and execution of experiments. It provides a graphical user interface for designing experimental protocols, managing data collection, and analyzing results. The core function of Experiment Builder is to enable researchers to efficiently plan, conduct, and analyze their experiments.

More about "Chin"