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Singer

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Most cited protocols related to «Singer»

1
Disaggregating Between-Person and Within-Person Effects
It is well known that between- and within-person effects can be efficiently and unambiguously disaggregated within the multilevel model using the strategy of person-mean centering. Traditionally, the term centering is used to describe the rescaling of a random variable by deviating the observed values around the variable mean (e.g.,Aiken&West 1991 , pp. 28–48). For example, within the standard fixed-effects regression model, a predictor xi is centered via xi′=xi−x¯ , where x̄ is the observed mean of xi, and xi′ is the mean-deviated rescaling of xi (see, e.g., Cohen et al. 2003 , p. 261). By definition, the mean of a centered variable is equal to zero, and this offers both interpretational and sometimes computational advantages in a number of modeling applications.
However, centering becomes more complex when considering TVCs. This is because multiple repeated measures are nested within each individual, and there are thus two means to consider: the grand mean of the TVC pooling over all time points and all individuals, and each person-specific mean pooling over all time points within individual. There are two ways that we can center the TVC.
First, we can deviate the TVC around the grand mean pooling over all individuals. Here, z¨ti=zti−z¯‥, where z̈ti represents the grand mean centered TVC, zti is the observed TVC, and z̄‥ is the grand mean of zti pooling over all individuals and all time points. In other words, we simply compute the grand mean of the TVC and subtract this from each individual- and time-specific TVC score. Second, we can deviate the TVC around the person-specific mean of the TVC unique to each individual. Here, z˙ti=zti−z¯i, where żti represents the person-mean centered TVC, zti is again the observed TVC, and z̄i is the person-specific mean for individual i. In other words, we subtract just the person-specific mean of the TVC from each of that same person’s time-specific TVC scores. We can use zti, żti, or z̈ti as the level-1 predictor in Equation 8, and each is associated with a potentially different inference with respect to the disaggregation of effects.
Methods exist that allow for the disaggregation of the between-person and within-person effects using zti, żti, or z̈ti (Kreft et al. 1995 , Raudenbush & Bryk 2002 ). However, direct estimates of these effects can be most easily obtained within the multilevel model by incorporating the person-mean centered TVC at level-1 (i.e., żti) and the person-mean at level-2 (i.e., z̄i) (Raudenbush & Bryk 2002 , equation 5.41). Specifically, yti=β0i+β1iz˙ti+rtiβ0i=γ00+γ01z¯i+u0i,β1i=γ10 where all is defined as above. This requires three steps: We first compute the mean of the time-specific TVCs within each individual to obtain z̄i; we then subtract that person-specific mean from each individual’s time-specific TVC values to obtain żti; finally, we use both z̄i and żti as predictors in our multilevel model.
The reduced form equation for this model is yti=(γ00+γ01z¯i+γ10z˙ti)+(u0i+rti), where γ00 is the intercept (or grand mean), γ01 is a direct estimate of the between-person effect, and γ10 is a direct estimate of the within-person effect. Following our earlier hypothetical example, γ01 would capture the relation between average levels of anxiety and average levels of substance use pooling over individuals. In contrast, γ10 would capture the mean relation between a given person’s time-specific deviation in anxiety (relative to the overall level of anxiety) and the individual’s time-specific substance use.
The approach we outline above is currently regarded as best practice for the disaggregation of between-person and within-person effects in multilevel growth models (e.g., Raudenbush & Bryk 2002 , pp. 181-85; Singer & Willett 2003 , pp. 173-77), and there is no question that this is a valid method for accomplishing these goals. As we describe in greater detail below, however, the validity of this approach heavily relies on a set of specific conditions that may or may not be met in practice. Further, we have found that these conditions are rarely, if ever, discussed in either the quantitative or applied literatures. To better define these specific conditions, we next propose a more general framework for defining within-person and between-person effects. This framework both more formally establishes these expressions and allows us to explicate precisely under what conditions standard approaches are and are not valid.
Curran P.J, & Bauer D.J. (2011). The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change. Annual review of psychology, 62, 583-619.
Publication 2010
Anxiety Singer Substance Use
2
Mammalian Expression Plasmid Construction
To construct mammalian expression plasmids, the respective genes of FPs were PCR-amplified as AgeI-NotI fragments and swapped with a gene encoding EGFP in the pEGFP-N1 plasmid (Clontech). IFP2.0-N1 and mIFP-N1 plasmids were acquired from Addgene (#54785 and #54620, respectively).
For protein tagging and labelling of intracellular structures study, miRFPs were amplified, digested with restriction enzymes and then swapped with mTagBFP2 either as C- (for α-tubulin and clathrin) or N-terminal fusions (for keratin, α-actinin, LifeAct, EB3, myosin, vimentin, clathrin, LAMP1, zyxin, H2B and mitochondrial signal) as previously described50 (link). C-terminal fusions (SGGGG)n linker was increased to 30 amino acids. N-terminal fusions linker length was left unchanged.
To create an IκBα reporter plasmid (CMV-IκBα-miRFP703), we used a CMV-IκBα-FLuc plasmid kindly provided by S. Achilefu and D. Piwnica-Worms. A FLuc gene was replaced with one of the miRFP genes. Kozak sequence was deleted in the CMV-IκBα-miRFP703 and CMV-miRFP control plasmids.
miSplit670 and miSplit709 reporter plasmids, which are pC4-RHE-PAS, pC4EN-F1-mGAF670 and pC4EN-F1-mGAF709, were constructed from an iSplit plasmids25 (link) by swapping either PAS or GAF domains. A linker -ggggsggggs- was left unchanged. Where appropriate, an NLS sequence in the pC4EN-F1 plasmid was deleted by site-directed mutagenesis.
For mRNA labelling, a CMV-PAS-MCP plasmid was constructed as follows. PAS-ggggsggggs- without STOP codon was amplified as a single fragment and inserted into the C1 vector backbone using AgeI and KpnI sites, MCP was amplified from an ubc-nls-ha-MCP-VenusN-nls-ha-PCP-VenusC plasmid (Addgene, #52985) and inserted at KpnI and BamHI sites. The cmv-PCP-mGAF670 and cmv-PCP-mGAF709 plasmids were constructed as follows. A PCP without STOP codon was amplified from an ubc-nls-ha-MCP-VenusN-nls-ha-PCP-VenusC plasmid and then inserted into the C1 vector backbone using AgeI and EcoRI restriction sites. A -ggggsggggs-miGAF was amplified as a single fragment and inserted using EcoRI and KpnI sites. A phage-cmv-cfp-12xMBS-PBS was obtained by swapping a 12xMBS-PBS fragment from a Pcr4-12xMBS-PBS (Addgene, #52984) with 24xMS2 in a phage-cmv-cfp-24xms2 plasmid (Addgene, #40651). An ubc-nls-ha-MCP-VenusN-nls-ha-PCP-VenusC, a phage-cmv-cfp-24xMS2 and a Pcr4-12xMBS-PBS plasmids were gifts from B. Wu and R. Singer.
Plasmids encoding several green-red Fucci cell cycle reporters were provided by A. Miyawaki. The mKO2 and mAG genes fused with hCdt1(30–120), hCdt1(1/100), hGem(1/110) and hGem(1/60) sequences in the pCSII-EF-MCS plasmids were swapped with the miRFP709 or miRFP670v1 genes.
Shcherbakova D.M., Baloban M., Emelyanov A.V., Brenowitz M., Guo P, & Verkhusha V.V. (2016). Bright monomeric near-infrared fluorescent proteins as tags and biosensors for multiscale imaging. Nature Communications, 7, 12405.
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Publication 2016
Actinin alpha, NF-KappaB Inhibitor alpha-Tubulin Amino Acids Bacteriophages Cell Cycle Clathrin Cloning Vectors Codon, Terminator Cytokeratin Deoxyribonuclease EcoRI DNA Restriction Enzymes Genes Gifts Helminths lysosomal-associated membrane protein 1, human Mammals Mitochondria Mutagenesis, Site-Directed Myosin ATPase Plasmids Proteins Protoplasm RNA, Messenger Singer Vertebral Column Vimentin ZYX protein, human
3
Systematic Essentiality Analysis of S. pombe Genes
General growth conditions and media were used as described by Moreno et al41 (link). Essentiality was determined by a microscopic observation of colony-forming ability of spores on YES (Yeast extract medium supplemented with adenine, leucine, uracil and histidine at 250 mg/l) at 25°C and 32°C. The spores were derived from corresponding heterozygous diploid deletion strains transformed with the pON177 plasmid42 (link) using a modified version of the PLATE method43 (link). About 5% of the heterozygous deletion diploids could not be transformed using this high throughput method and these were repeated using a standard transformation protocol40 (link). Briefly, 4 batches each of 48 heterozygous diploid strains were patched on to YE (yeast extract medium supplemented with leucine and uracil at 250 mg/l)+G418 agar plates in two 96-well microtitre plates (each strain is represented four times) and left to grow for 2~3 days at 32°C. Cells were inoculated into 200 μl YE+G418 and left to grow into stationary phase. The cells were harvested and transformed with pON17742 (link), plated on minimal agar + leucine (250 mg/l) and incubated for a week at 32°C.
Transformants were inoculated into minimal media lacking nitrogen and left for 2~3 days at 25°C to induce sporulation. The asci were treated with helicase (Bio Sepra) diluted 1 in 250 to eliminate vegetative cells, washed with water and the haploid spores were plated on YES agar at 25°C and 32°C. Essentiality was determined by a microscopic observation of the germinating spores on plates after 1 and 2 days before replica plating to YES+100 μg/ml G418 to confirm that the deletion phenotype was associated with G418 resistance. Essential genes were further analysed by tetrad analysis. Briefly, cells harbouring pON177 were left to germinate for 4~5 days on minimal plates. Using a Singer MSM microscope, spores were dissected on YES plates for 4~5 days at 30°C. Viable colonies were patched onto YES plates+100 μg/ml G418 to confirm that viability was linked to G418 sensitivity. For details see Supplemental Methods 1. Whilst analyzing gene dispensability, we found that a subset of the deletion collection harboured a recessive temperature sensitive mutation unrelated to the gene deletion. This ts mutation was removed from the entire non-essential haploid deletion library after sporulation of the diploid heterozygous deletion strains of non-essential genes. There were originally 416 of the 1,260 essential heterozygous deletion diploid strains that harboured the ts mutation. Of these 416 strains, 364 have been remade and the remaining 52 are currently being remade (see Supplementary Table 1 Column U for the list of heterozygous diploid strains that still contain the ts mutation).
Kim D.U., Hayles J., Kim D., Wood V., Park H.O., Won M., Yoo H.S., Duhig T., Nam M., Palmer G., Han S., Jeffery L., Baek S.T., Lee H., Shim Y.S., Lee M., Kim L., Heo K.S., Noh E.J., Lee A.R., Jang Y.J., Chung K.S., Choi S.J., Park J.Y., Park Y., Kim H.M., Park S.K., Park H.J., Kang E.J., Kim H.B., Kang H.S., Park H.M., Kim K., Song K., Song K.B., Nurse P, & Hoe K.L. (2010). Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe. Nature biotechnology, 28(6), 617-623.
Publication 2010
Adenine Agar antibiotic G 418 Cells Deletion Mutation Diploidy DNA Helicases DNA Library Gene Deletion Genes Genes, Essential Growth Disorders Heterozygote Histidine Hypersensitivity Leucine Microscopy Mutation Nitrogen Phenotype Saccharomyces cerevisiae Singer Spores Strains Uracil
4
Hierarchical Linear Modeling for Symptom Change Predictors
Our primary statistical analyses investigated the association between possible prognostic and prescriptive predictors of symptom change across the 16-week study. Continuous data from the HRSD were analyzed using hierarchical linear models (HLM, also known as random regression models or growth curve models) that adjusted for the repeated measures with nested random effects. Using this approach, each subject’s growth curve and HRSD score at the end of treatment is estimated from a collection of patient-specific parameters. These are treated as having been randomly sampled from a population of individuals, and an unstructured covariance structure was assumed in order to model the correlation between the patient-specific intercepts and slopes. All available data were used, rendering this data analytic strategy a full intent-to-treat analysis. Two baseline scores were obtained for each participant, allowing us to covary each patient’s initial baseline depression severity score and to maintain a full intent-to-treat approach, even for individuals who dropped out before the first day of treatment. For all models, full maximum likelihood estimation procedures were used, and the degrees of freedom for hypothesis tests were estimated with the Kenward-Roger approximation (Kenward & Roger, 1997 (link)). All analyses were performed using SAS Version 9.1 PROC MIXED (SAS Institute Inc, Cary, NC).
To identify relevant predictors, we were guided by the approach advocated by Kraemer and colleagues (Kraemer et al., 2002 (link)). Within this framework, the interaction between treatment condition and the predictor of interest is examined. If the term representing this interaction is significantly related to outcome, the predictor is considered to be prescriptive, as it indicates differential effects of the treatments depending on the value of the variable in question. If the interaction term is not significant but the lower order term representing the effect of the variable is significant, then the predictor is considered to be prognostic. In such a case, outcome depends on the level of this predictor independent of the treatment that was received. Using this general framework, we constructed the HLMs to assess simultaneously whether a variable was prognostic or prescriptive. To determine whether a variable was prognostic, we examined the effect of the predictor at the intercept (centered to represent estimated endpoint scores at 16 weeks) and on the linear slope estimates (represented in the model by the predictor-by-time interaction). Prognostic predictors were required to be associated with both of these outcomes (intercepts and slopes) at the p < .05 level. In order to determine whether the variable was prescriptive, we investigated the predictor-by-treatment interaction effect at the intercept as well for linear slope estimates. Prescriptive predictors were required to be associated with both of these outcomes (intercepts and slopes) at p < .05.
Given the potentially large number of statistical tests implied by the number of baseline variables to be examined, analyzing each separately would be expected to increase the likelihood that we would find significant relationships purely by chance. One typical solution to this dilemma is the imposition of a correction factor, such as the Bonferroni, which would raise the threshold required for declaring statistical significance. Rothman (1990) (link), on the other hand, argues that the use of such correction factors is not only misguided given the fundamental task of empirical science, but might inadvertently lead to a greater number of errors of inference, albeit errors in the opposite direction. That is, such corrections might render significance tests to be so strict as to reject real relationships between variables that would have been worth exploring had they been examined separately. In an attempt to maintain a balance between these two competing concerns, we employed the following approach.
First, we calculated separate models for each of the aforementioned predictor domains. For each domain, a larger prediction model containing all of the potential predictors in that domain was compared to a smaller, nested model. This smaller, simple model contained the covariates implemented in the original (DeRubeis et al., 2005 (link)) manuscript (main effects of baseline HRSD, site, and treatment, as well as the site by treatment interaction). The fit of the prediction model was compared to that of the simple model by means of likelihood ratio tests between the models’ deviance statistics (Singer & Willett, 2003 ). Only in cases where the prediction model proved statistically superior to the simple model at alpha ≤ .05 were the specific effects of the individual predictors explored. Because the deviance statistic requires that both the simple and predication models contain all of the same individuals, missing data from any of the potential predictors could be problematic as it would result in the list-wise deletion of individuals with missing data from one model but not the other. Across most predictors, the rate of missing data was below 9%. The rates of missing data were slightly higher for some of the family history of mental illness predictors, the highest being 18% for the “family history of suicide attempts” variable. All missing values were imputed using multiple regression models containing site, gender, and age as predictors.
We employed a step-wise procedure within each of the five domains. Step 1 of the procedure for a given domain was the test of whether the model that included all variables from the domain was significant. In Step 2, we retained only those predictors associated in Step 1 with significance values of p < .20; in Step 3, we retained only those from Step 2 where p < .10. Finally, in Step 4, we retained only those predictors from Step 3 with a significance value of p < .05. As in previous work of this nature (Hybels, Blazer, & Steffens, 2005 (link)), once all predictor variables were identified, they were entered into a full model containing all significant predictors so that we could ascertain the effects of each variable while simultaneously controlling for each of the other significant predictors. For all models reported below, continuous variables were centered at the grand mean, and dichotomous variables were set to −½ and ½ (Kraemer & Blasey, 2004 (link)).
Fournier J.C., DeRubeis R.J., Shelton R.C., Hollon S.D., Amsterdam J.D, & Gallop R. (2009). Prediction of Response to Medication and Cognitive Therapy in the Treatment of Moderate to Severe Depression. Journal of consulting and clinical psychology, 77(4), 775-787.
Publication 2009
Blazer Deletion Mutation factor A Gender Mental Disorders Patients Singer Suicide Attempt
5
Multilevel Analysis of Neighborhood Food Density
We calculated adjusted mean BMI for each quintile of retail density for the three food categories using cross-sectional, multilevel modeling (Diez Roux 2000 (link)) with the Proc Mixed procedure (Singer 1998 ) in SAS (version 9; SAS Institute Inc., Cary, NC). Because each of the neighborhood-level measures was generated for each individual’s address, we treated the neighborhood variables as level 1 variables. We expected intercor-relations among individuals, reflecting similarity among those living in proximity to each other, to exist across a geographic scale larger than the half-mile buffers. To account for this, we estimated our multilevel models with community district as a level 2 clustering factor. New York City’s 59 community districts correspond to named areas such as the Upper West Side and Chinatown. Although we measured no predictive variables at level 2, the use of this nested data structure allowed for valid estimation of standard errors. We adjusted analyses for individual and neighborhood sociodemographic characteristics and then for the five neighborhood walkability measures. We evaluated the five walkability measures as possible confounders individually and in combination. We mutually adjusted all analyses for quintiles of each of the three food categories.
We calculated separate prevalence ratios for overweight and obesity compared with normal weight for increasing quintiles of retail food density categories using Poisson regression with robust variance estimates (Spiegelman and Hertzmark 2005 (link)). We used community district as a clustering variable to correct the standard errors for intercorrelations among individuals across larger areas of the city and to generate robust SE estimates.
Rundle A., Neckerman K.M., Freeman L., Lovasi G.S., Purciel M., Quinn J., Richards C., Sircar N, & Weiss C. (2008). Neighborhood Food Environment and Walkability Predict Obesity in New York City. Environmental Health Perspectives, 117(3), 442-447.
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Publication 2008
Buffers Food Food Analysis Obesity Singer

Most recents protocols related to «Singer»

1
Transcriptomic Analysis of Septic Shock
The included transcriptome data were downloaded from gene expression omnibus (GEO) databases (http://www.ncbi.nlm.nih.gov/geo/) (Barrett et al., 2013 (link)). Only peripheral blood samples collected within 24 h of diagnosis or ICU admission were included. The RNA sequencing data of 91 adult samples (including 19 septic shock, 20 sepsis, 12 uncomplicated infection and 40 healthy controls) in the GSE154918 dataset, which were pre-processed using the DESeq2 package by the contributors (Love et al., 2014 (link); Herwanto et al., 2021 (link)), were used as discovery dataset to explore genes, modules and mechanisms associated with septic shock. Additionally, the array data and survival information of 479 adult sepsis samples with a 28-day cumulative death rate about 23.80% in the GSE65682 dataset were read in R language to determine the prognostic significance of interested genes in sepsis patients. The gene expression profiles of GSE65682 were background-subtracted and normalized by a robust multi-array average algorithm using the affy package. The row count matrix of 345 adult sepsis samples including 52 dead and 293 survival samples in the GSE185263 dataset was downloaded to validate survival significance of the hub gene.
Clinical blood laboratory examinations data of sepsis and septic shock patients were extracted from the MIMIC-IV (version 2.0) database in the physionet (https://physionet.org/content/mimiciv/2.0/) for the further validation (Goldberger et al., 2000 (link); Johnson et al., 2022 (link)). One of the authors who has finished the required Collaborative Institutional Training Initiative examination (Certification number 53459610 for Zhao) can access the database. The adult ICU stay samples meeting the sepsis-3 definition at the first day of ICU admission were included (Singer et al., 2016 (link)). The patients’ parameters including absolute neutrophil count, absolute CD3 count (i.e., T cell count), absolute CD4 count and absolute CD8 count from blood specimens and survival data were extracted for further analysis. Specifically, we extracted the max values of neutrophil counts of each ICU stay within 6 h before ICU admission and 24 h after; while the chart time requirements of the other three items were limited to 6 h before ICU admission and 48 h after, concerning their more time costs waiting for the reports. In our study, the data about neutrophil counts of 8250 ICU stays containing 40.5% septic shock samples and with a 28-day cumulative mortality rate (CMR) about 22.3% were extracted. However, among them only 69 had the time-limited data about CD3 counts and 68 had desirable CD4 counts and CD8 counts due to their less clinical applications. More details were shown in Supplementary Table S1. The code used for data extraction can be available on GitHub (https://github.com/MIT-LCP/mimic-iv).
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.
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Publication 2023
Adult BLOOD CD4+ Cell Counts Clinical Laboratory Services Diagnosis Genes Infection Love Neutrophil Patients Physical Examination Septicemia Septic Shock Singer Transcriptome
2
Assessing Motion Sickness and Immersive Tendencies
By way of sampling information, participants completed questionnaires including sociodemographic and their general utilization of new technologies. Participants were also asked how often they use a smartphone, a computer, virtual reality, and play video games. In addition, they were asked to indicate how familiar they are with virtual reality on a 10-point scale ranging from 1 = “strongly disagree” to 10 = “strongly agree”.
The Motion Sickness Susceptibility Questionnaire (MSSQ; Golding 1998 (link)) assesses the level of motion sickness experienced during various transportation-related activities and other activities. It contains 9 categories of possible experiences that elicit motion sickness (e.g., cars, buses, swings, funfairs). Each item is rated on a 5-point scale ranging from 1 = “never” to 5 = “always”.
The Immersive Tendencies Questionnaire (ITQ; Witmer and Singer 1998 (link)) assesses one’s tendency to shut out external distractions in order to focus on different tasks in daily life. The French version (Robillard et al. 2002 ) contains 18 items where participants rate their level of agreement on a 7-point scale. Four dimensions are derived: focus (i.e., the tendency to maintain focus on current activities), involvement (i.e., the tendency to become involved in activities), emotion (i.e., the ease of feeling intense emotions evoked by the activity), and game (i.e., the tendency to play video games).
Della Libera C., Simon J., Larøi F., Quertemont E, & Wagener A. (2023). Using 360-degree immersive videos to assess multiple transdiagnostic symptoms: A study focusing on fear of negative evaluation, paranoid thoughts, negative automatic thoughts, and craving. Virtual Reality.
Publication 2023
Emotions Menopause Motion Sickness Singer Submersion Susceptibility, Disease
3
Validation of Vocal Dose and Effort Algorithm
To confirm the accuracy of the algorithm for determining vocal dose and effort, four singers (soprano, alto, tenor, and baritone) completed a set of between 10 and 11 tasks while recording with the MA device, each lasting for 1 min with 1 min of rest in between. A handy video recorder (Q8, Zoom) for all participants simultaneously recorded acoustic audio samples. The tasks included normal speaking, speaking over 60 dB of ambient noise, whispered speaking, moderately loud singing (low to mid range), moderately loud singing (mid to high range), very loud singing (low to mid range), very loud singing (mid to high range), singing without vibrato (low to mid range), staccato arpeggios throughout the vocal range, and strained speaking. Each participant selected one excerpt to use for the low- to mid-range tasks and another piece to use for the mid- to high-range tasks. For the speaking exercises, participants read from “Practicing Vocal Music Efficiently and Effectively: Applying ‘Deliberate Practice to a New Piece of Music’” by Ruth Rainero (21 ). The strained speaking task was completed only by the alto participant.
Jeong H., Yoo J.Y., Ouyang W., Greane A.L., Wiebe A.J., Huang I., Lee Y.J., Lee J.Y., Kim J., Ni X., Kim S., Huynh H.L., Zhong I., Chin Y.X., Gu J., Johnson A.M., Brancaccio T, & Rogers J.A. (2023). Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2219394120.
Publication 2023
Acoustics DB 60 Medical Devices Singer
4
Singing vs. Speaking: Machine Learning Analysis
Sixteen singers, ranging in age from 20 to 61 y, wore an MA device adhered to the sternum, just below the sternal notch. The participants sang six different vocal exercises through their full vocal range (hums, glides, legato scales, arpeggios, staccato scales, and monotones with varied dynamics) followed by a song of their choice for 4 min. The participants then read from the first chapter of the book Grit: The Power of Passion and Perseverance for 10 min (20 ). These samples served as the training set for the development of machine learning algorithms, capable of distinguishing singing from speaking with 91% accuracy.
Jeong H., Yoo J.Y., Ouyang W., Greane A.L., Wiebe A.J., Huang I., Lee Y.J., Lee J.Y., Kim J., Ni X., Kim S., Huynh H.L., Zhong I., Chin Y.X., Gu J., Johnson A.M., Brancaccio T, & Rogers J.A. (2023). Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2219394120.
Publication 2023
Medical Devices Singer Sternum
5
Vocal Biomarker Acquisition Protocol
The studies were approved by the Institutional Review Boards of Northwestern University (STU00207900). During data collection, the device was mounted on the SN of singers. All singers consented to procedures and provided written consent form for images.
Jeong H., Yoo J.Y., Ouyang W., Greane A.L., Wiebe A.J., Huang I., Lee Y.J., Lee J.Y., Kim J., Ni X., Kim S., Huynh H.L., Zhong I., Chin Y.X., Gu J., Johnson A.M., Brancaccio T, & Rogers J.A. (2023). Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2219394120.
Publication 2023
Ethics Committees, Research Medical Devices Singer

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4
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Sourced in United Kingdom
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9
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10
96 well replicator by Singer Instruments
The 96-well replicator is a laboratory tool designed to transfer liquid samples from one 96-well microplate to another. It features a set of 96 pins or tips that can simultaneously aspirate and dispense small volumes of liquid, enabling efficient and accurate parallel transfer of samples across multiple plates.

More about "Singer"

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By integrating PubCompare.ai's cutting-edge AI-driven comparisons with these complementary technologies and chemical tools, scientists can optimize their Singer research protocols, improve the overall quality of their studies, and drive scientific progress forward with greater efficiency and accuracy.