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TimeLine

TimeLine: A digital tool that organizes and visualizes research timelines, enabling researchers to effectively plan, track, and communicate their projects.
TimeLine provides a user-friendly interface to create and manage custom timelines, set milestones, and monitor progress.
With its AI-powered features, researchers can easily identify potential bottlenecks, optimize resource allocation, and enhance collaboration across teams.
TimeLine empowers researchers to stay on top of their research workflows and acheive their goals more efficiently.

Most cited protocols related to «TimeLine»

1
Optimizing Transcript Abundance Estimation
We describe the online and offline inference algorithms of Salmon, which together optimize the estimates of α — a vector of the estimated number of reads originating from each transcript. Given α the η can be directly computed. An overview of the Salmon execution “timeline”, which describes when, during the execution of the algorithm different estimates are made and quantities of interest are computed, is given in Supplementary Fig. 1.
Patro R., Duggal G., Love M.I., Irizarry R.A, & Kingsford C. (2017). Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference. Nature methods, 14(4), 417-419.
Publication 2017
Cloning Vectors Salmo salar TimeLine
2
Simultaneous Measurement of Physical Activity and Energy Expenditure
Study participants spent approximately 24-h period in a whole-room indirect calorimeter (28 (link)), and followed a structured protocol for simultaneous measurements of PA and EE. The protocol included a broad range of pursuits ranging from moderate and vigorous to light and sedentary tasks, including eating meals and snacks and self-care activities. During times (30 to 120 minutes) when no activity was specifically scheduled, the participants were asked to engage in their normal daily routine as much as possible without specific suggestions. They also recorded their activities in a diary with a detailed schedule, reporting any episodes of accidental monitor nonwear intervals and other relevant comments. Sleep was defined as the period of time spent lying on a mattress at night between 9:00 pm and 6:00 am without any significant movement as determined by the floor (force platform) in the room calorimeter. The participants were instructed how to record their activities in a provided diary with a detailed schedule and a timeline. They checked off each scheduled activity and reported any episodes of accidental monitor nonwear intervals and other relevant information (e.g. treadmill speed) or comments. During the day, staff was available for assistance and the dairy was discussed with each participant after finishing the study.
Body weight was measured to the nearest 0.01 kg with a digital scale and height was measured using a wall-mounted stadiometer. The minute-to-minute EE was calculated from the rates of oxygen consumption and carbon dioxide production (33 (link)). Nonwear EE was calculated by summing EE measured by the room calorimeter during time intervals detected as nonwear by each algorithm.
The PA was measured by commercially available Actigraph GT1M accelerometer (ActiGraph, Pensacola, FL), calibrated by the manufacturer placed on the anterior axillary line of the hip on the dominant side of the body. Among commercially available accelerometers, the Actigraph used in the present study provides consistent and high quality data, supported by its feasibility, reliability and validity (9 (link)). The monitor reports counts from the summation of the measured accelerations over a specified epoch (1 ). Actigraph data were collected at a 1-second epoch and summed as counts per minute.
Choi L., Liu Z., Matthews C.E, & Buchowski M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Medicine and science in sports and exercise, 43(2), 357-364.
Publication 2011
Acceleration Accidents Actigraphy Axilla Body Weight Carbon dioxide EPOCH protocol Human Body Light Movement Oxygen Consumption Sleep Snacks TimeLine
3
Microreact: Interactive Visualization of Multidimensional Biological Data
The input to Microreact is a data file in comma separated value format (CSV, A in Fig. 1), along with an optional tree file in Newick format (Felsenstein). The CSV file should contain entries as rows and data variables as columns. The format of the column headers is summarized in Table 1. The name of the entries in the ID column must be unique and exactly match those utilized as leaf labels within the Newick tree file. Geolocations should be specified in the latitude and longitude columns as decimals (WGS84). Temporal data should be formatted into three separate columns: year, month and day. Missing data values for month and day will result in the entry being dated to the 1st of January of the year specified. Likewise, missing day values will result on the entry to be dated to the 1st of the month and year specified.
The minimal data required is a CSV with a set of IDs and at least one data column. If geographical, temporal or clustering data are included, the Microreact will link them to the data table via a map, timeline or tree, respectively. For all possible combinations of data visualization see Table S1 (available in the online Supplementary Material).
Argimón S., Abudahab K., Goater R.J., Fedosejev A., Bhai J., Glasner C., Feil E.J., Holden M.T., Yeats C.A., Grundmann H., Spratt B.G, & Aanensen D.M. (2016). Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microbial Genomics, 2(11), e000093.
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Publication 2016
Plant Leaves TimeLine Trees
4
PARDS Consensus Development Protocol
Three members of the organizing committee met in March 2012 to define the methodology, to select the subtopics for study, and to identify the experts in the field. Experts were invited based on their record of publications in PARDS in the past 5 years and their participation in clinical research studies in pediatric critical care. The final list of 27 experts, representing 21 academic institutions and eight countries, constituted the PALICC expert group (Appendix 1). Of note, only one expert declined to participate due to personal reasons; two experts initially agreed to participate but were subsequently unable for personal reasons.
The first PALICC meeting took place in Chicago, IL, on October 2, 2012, in conjunction with the fall meeting of the PALISI Network. At this meeting, we discussed and agreed upon conference subtopics, the project timeline, and the consensus methodology (Fig. 1). Experts were also assigned to each of the nine subtopics. The modified Delphi approach previously employed by the French Society of Pediatric Intensive Care (13 ) was chosen as the methodology to achieve consensus. This approach was necessary because of the limited data and low level of available evidence, as well as the high variability in clinical practice in PARDS. A detailed description of this methodology is available in the supplement published in Pediatric Critical Care Medicine (14 (link)).
Between the first and second meeting, each group of experts undertook a comprehensive, standardized literature review. Upon completion, each group drafted their recommendations along with detailed arguments to support them. The second meeting occurred in Montreal, QC, Canada, on April 18–19, 2013. At this 2-day meeting, the recommendations were discussed and the wording of each agreed upon by the majority of experts. Possible omissions for any of the nine topics were also discussed. After the second meeting, recommendations with their respective arguments (long texts) were distributed to each expert for electronic scoring by the Research ANd Development/University of California Los Angeles (RAND/UCLA) appropriateness method (15 ). Experts with a disclosed conflict of interest were excluded from voting on areas where any real or perceived conflict was identified. After the initial scoring, all recommendations were consolidated by the organizing committee.
Agreement was determined by voting using the RAND/UCLA scale (scores range from 1 to 9), with each expert having an equal vote but with the highest and lowest scores discarded after each vote. “Strong” agreement required that all experts rank the recommendation 7 or higher. “Weak” agreement meant that at least one more expert ranked the recommendation below 7, but the median vote was at least 7. Those with strong agreement were considered complete, and those with weak agreement were revised based on comments by the experts. These revised recommendations were then distributed for a second round of electronic voting. After this voting, some reworded recommendations obtained a strong agreement. For the remaining recommendations with a weak agreement after the second round, the percentage of experts who rated 7 or above was calculated and is reported after each weak recommendation. With this method of calculation, a strong agreement corresponded to a percentage of agreement more than 95% (no more than one expert rated below 7 on the RAND/UCLA scale).
The third and final meeting took place on October 9, 2013, in Paris, France. Each group presented their final recommendations, and a third round of voting was conducted for several specific but unresolved recommendations related to the definitions. The organizers believed it was vital to achieve strong agreement regarding definitions, and this was accomplished after much dialog and debate. Additionally, each group of experts presented their consensus regarding key areas of controversy and future research.
Pediatric Acute Respiratory Distress Syndrome: Consensus Recommendations From the Pediatric Acute Lung Injury Consensus Conference. (2015). Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 16(5), 428-439.
Publication 2015
Committee Members Conferences Critical Care Debility Dietary Supplements Intensive Care TimeLine
5
Cytoscape App Store: Discovery and Installation
The App Store offers multiple ways for users to discover apps (Fig. 1A). First, we provide a Featured Apps section that invites new users of Cytoscape to simply click on a featured app, read about it and, with a single click, install it. Thus, we promote a learning-by-action approach to understanding the role and scope of apps by maintaining a low barrier to entry. Second, there is a list of categories for users with a general idea of the kind of app in which they are interested. Users find all apps of a given type by clicking on a category. An app can belong to several categories, each chosen by their authors. Third, users can search for an app based on its name, description, categories, authors and authors’ institutions. With these features, we hope to encourage users to browse the App Store, discover and learn about apps, and install them to expand the capabilities of Cytoscape.
App pages have several aspects to help users learn about a particular app’s capabilities and usage (Fig. 1B). App authors can provide screenshots and in-depth descriptions on their app page. They can also provide links to their own Web site and tutorials. Though not required, we encourage authors to release their app under standard open source practices and to provide a link to a code repository. Users can also learn about the popularity of an app by its rating and number of downloads. Any visitor to an app page can rate it from zero to five stars, with five stars being the best. Each app has a download statistics page with a timeline plot of downloads per version and a world map showing where downloads occurred. These app statistics are often valuable to authors, as well as potential users, to track usage and justify further development and support of their work.
Apps can be installed right from the App Store with a single click without having to leave the browser when Cytoscape 3.0 or above is running. If an already installed app is out-of-date, it can be upgraded from the Web site using the same feature. The most recent version of an app is prominently displayed; older versions of an app can be manually downloaded in the Release History panel. From within Cytoscape, apps can also be searched for, installed, upgraded and uninstalled using the App Manager tool. The underlying goal of these features is to make app installation as easy as possible. We hope to entice users to install and experiment with apps by removing cumbersome and confusing steps.
Aside from users, app authors also benefit from the App Store. Authors can directly edit their app pages with their changes reflected in real-time preview. They can provide a custom icon, screenshots and a unique description to distinguish their app. Authors can submit new apps to the App Store with immediate technical feedback for ensuring that submissions follow Cytoscape’s metadata conventions. Authors can also post a contact email address and a link to their app's source code to encourage user engagement.
Lotia S., Montojo J., Dong Y., Bader G.D, & Pico A.R. (2013). Cytoscape App Store. Bioinformatics, 29(10), 1350-1351.
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Publication 2013
Conferences CTSB protein, human Stars, Celestial TimeLine

Most recents protocols related to «TimeLine»

1
Optimizing Morphogen Exposure in Stem Cell Differentiation
Not available on PMC !

Example 6

Optimization of Morphogen Exposure

The optimal duration of caudalization and ventralization may vary depending on the parent cell line used, culture conditions, and quality of reagents. For cells with ESC origin both caudalization and ventralization are typically 1 day faster, for hiPSC derived from adult cells, the time can depend on the origin of the somatic cells. Several different types of cells have been used to produce iPSCs, including fibroblasts, neural progenitor cells, keratinocytes, melanocytes, CD34+ cells, hepatocytes, cord blood cells and adipose stem cells. In hiPSC derived from CD34+ cells caudalization and ventralization may be slower for up to 2 days. hiPSC derived from fibroblasts typically follow the time line as explained in the FIG. 1.

US11859206B2. Generation of oligodendrogenic neural progenitor cells (2024-01-02). UNIVERSITY HEALTH NETWORK [CA]. Inventors: Michael George Fehlings [CA], Mohamad Khazaei [CA].
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Patent 2024
Adipocytes Adult Blood Cells Cell Lines Cells Cone-Rod Dystrophy 2 Fibroblasts Gene Therapy, Somatic Germ Cells Hepatocyte Human Induced Pluripotent Stem Cells Induced Pluripotent Stem Cells Keratinocyte Melanocyte Neural Stem Cells Neurogenesis Parent Stem, Plant TimeLine Umbilical Cord Blood
2
High-Precision Breath Analysis via HPPI-TOFMS
HPPI-TOFMS, which consisted of a vacuum ultraviolet (VUV) lamp-based HPPI ion source and an orthogonal acceleration time-of-flight (TOF) mass analyzer, was used to detect and analyze the breath samples. A commercial VUV-Kr lamp with a photon energy of 10.6 eV was adopted in this platform. Most VOCs with an ionization potential lower than 10.6 eV were ionized in the ionization region directly [32 (link)]. Breath samples were directly introduced through a 250 μm i.d. 0.60 m long stainless-steel capillary. The HPPI ion source works in soft HPPI ionization mode, which will produce mostly radical cations (M+) by ionization reaction as M + hγ → M+  + e. Then, the ion transmission system effectively transferred these ions from the ion source into the orthogonal acceleration, reflection TOFMS mass analyzer. The TOFMS signals were recorded by a 400 ps time-to-digital conversion rate at 25 kHz, and all the mass spectra were accumulated for 60 s. Thus, it takes 1 min for one sample to go through a detection. A spectrogram with 31,666 data pairs was extracted from each exhaled breath sample. Based on the flight time and m/z calibration on the standard gas with nine compounds at a concentration of 1 ppmv, the timeline of flight can be transferred as m/z, which is in the range of (0, 350). The TOFMS signals were positively correlated with the concentration of the VOC ions. The detection limit is down to 0.015 ppbv (parts per billion by volume) for aliphatic and aromatic hydrocarbons [28 (link)]. The gas-phase breath sample was directly inhaled into the ionization region through a 250 μm i.d. 0.60 m long capillary from the sampling bag. The TOF signals were recorded by a time-to-digital converter, and all the mass spectra were accumulated for 60 s. Mass spectrum peaks with m/z < 350 were detected by HPPI-TOFMS for each exhaled breath sample. The noise-reducing and base-line correction were implemented via anti-symmetric wavelet transformation, which was achieved by Python package pywavelets [33 (link)]. To transfer the discrete signal of mass spectra data to standard breathomics data, we calculate the area of the strongest peak in the range of [x − 0.1, x + 0.1) as the feature of VOC with m/z close to x. In this study, 1500 breathomics data were detected for machine learning (ML) model construction in the ions m/z range of [20, 320) with an interval of 0.2. A statistical analysis based feature selection was executed to avoid model over-fitting, in which the features without significant difference (p > 0.05) were excluded before model training.
Fu L., Wang L., Wang H., Yang M., Yang Q., Lin Y., Guan S., Deng Y., Liu L., Li Q., He M., Zhang P., Chen H, & Deng G. (2023). A cross-sectional study: a breathomics based pulmonary tuberculosis detection method. BMC Infectious Diseases, 23, 148.
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Publication 2023
Acceleration BaseLine dental cement Capillaries Cations Fingers Hydrocarbons, Aromatic Hypophosphatasia, Infantile Ions Mass Spectrometry polysucrose-400 Python Reflex Seizures Stainless Steel TimeLine Transmission, Communicable Disease Vacuum
3
Evaluation of Wound Healing Intervention
Table 1 shows the participant timeline.

Participant timeline

aUntil complete healing of the wound

bIntervention group only

Herraiz-Ahijado B., Folguera-Álvarez C., Verdú-Soriano J., Mori-Vara P, & Rico-Blázquez M. (2023). Active legs: Impact of physical activity as an adjuvant treatment in the healing of venous ulcers in primary care: a RCT protocol study. BMC Nursing, 22, 65.
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Publication 2023
TimeLine
4
Qualitative Exploration of Social Support
Participants were taking part in a wider study and responded to three measures during one-on-one interviews, beginning with demographic questions, and followed by the Multidimensional Scale of Perceived Social Support (MSPSS) and semi-structured interview. A timeline approach was utilized by the researcher, which helped to organize the data and provided a participative space in which to discuss sensitive topics. In some cases, the participants spontaneously started talking about their lives and in this case the researcher allowed them to talk freely without interruption and reordered the interview as seemed to fit best, whilst ensuring that the basic interview topics were covered. In this paper we report on the qualitative elements of the study.
McGrath J., Crossley S., Lhussier M, & Forster N. (2023). Social capital and women’s narratives of homelessness and multiple exclusion in northern England. International Journal for Equity in Health, 22, 41.
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Publication 2023
Speech TimeLine
5
Dulaglutide Effects on Blood Pressure in T2D
The analyses included six pivotal randomized, double-blind trials of dulaglutide 1.5 mg in participants with T2D that measured sitting SBP and DBP from vital sign data around the timeline of 6 months (week 24 to week 26). Five placebo-controlled studies were used to estimate the effects between dulaglutide 1.5 mg and placebo. AWARD-1 (NCT01064687), AWARD-5 (NCT00734474), AWARD-8 (NCT01769378), and AWARD-10 (NCT02597049) were phase 3, placebo-controlled trials which investigated the safety and glycemic efficacy of dulaglutide with various background glycemic therapies (Table 1). Ferdinand et al. (NCT01149421) was a phase 2, randomized, double-blind, placebo-controlled trial which evaluated BP and heart rate effects of dulaglutide vs. placebo in participants with T2D with and without hypertension and BP < 140/90 mmHg. In addition, AWARD-11 (NCT03495102) was a phase 3, non-placebo-controlled trial to evaluate safety and glycemic efficacy of dulaglutide 3.0 mg and 4.5 mg to dulaglutide 1.5 mg.

Study design for placebo-controlled trials included in the meta-analysis

ParametersAWARD-1AWARD-5AWARD-8AWARD-10AWARD-11Ferdinand et al
PhasePhase IIIPhase II/IIIPhase IIIPhase IIIPhase IIIPhase II
RandomizationRandomizedRandomizedRandomizedRandomizedRandomizedRandomized
BlindingBlindingDouble-blindDouble-blindDouble-blindDouble-blindDouble-blind
Primary EndpointA1cA1cA1cA1cA1c24-h SBP
Study Treatment Period52 weeks24 months24 weeks24 weeks52 weeks26 weeks
Last scheduled visit with PBO26 weeks6 months24 weeks24 weeks52 weeks (no PBO)26 weeks
Background therapy (Add-ons)Met + TZDMet monoSU monoSGLT2i with or without metforminMet monoStable OAM
Key inclusion/ exclusion criteria
 Age ≥ 18 years18–75 years ≥ 18 years ≥ 18 years ≥ 18 years ≥ 18 years
 T2D durationNA ≥ 6 monthsNANAfor ≥ 6 monthsNA
 A1c7.0–11.07.0–9.57.5–9.57.0–9.57.5–117–9.5
 BMI23–4525–40 ≤ 45 ≤ 45 ≥ 25NA
 MedicationStable OAMDiet & exercise / metformin and/or other OAMStable SUSGLT2i with or without metformin for ≥ 3 monthsStable metformin for ≥ 3 monthsOAM

BMI body mass index, NA not applicable for the study’s design, Met metformin, mono monotherapy, OAM oral antihyperglycemic medication, PBO placebo, SBP systolic blood pressure, SGLT2i sodium-glucose cotransporter-2 inhibitors, SU sulfonylurea, T2D type 2 diabetes, TZD thiazolidinediones

Ferdinand K.C., Dunn J., Nicolay C., Sam F., Blue E.K, & Wang H. (2023). Weight-dependent and weight-independent effects of dulaglutide on blood pressure in patients with type 2 diabetes. Cardiovascular Diabetology, 22, 49.
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Publication 2023
Diabetes Mellitus, Non-Insulin-Dependent dulaglutide High Blood Pressures Hypoglycemic Agents Index, Body Mass inhibitors Metformin Pharmaceutical Preparations Placebos Rate, Heart Safety Signs, Vital SLC5A2 protein, human Sulfonylurea Compounds Systolic Pressure Therapeutics Thiazolidinediones TimeLine

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C57bl 6j mice by Jackson ImmunoResearch
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C57BL/6J mice are a widely used inbred mouse strain. They are a commonly used model organism in biomedical research.
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
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Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.

More about "TimeLine"

TimeLine is a cutting-edge digital tool that revolutionizes the way researchers plan, track, and communicate their projects.
This innovative platform provides a user-friendly interface to create and manage custom research timelines, set milestones, and monitor progress with ease.
Powered by advanced AI features, TimeLine enables researchers to identify potential bottlenecks, optimize resource allocation, and enhance collaboration across their teams.
By leveraging this powerful tool, researchers can stay on top of their workflows and achieve their goals more efficiently.
Researchers can utilize TimeLine to plan and execute their studies, whether they are working with C57BL/6J mice, Sprague-Dawley rats, or other animal models.
The platform's timeline functionality can be particularly useful for projects involving the administration of drugs like Tamoxifen or the analysis of data using software such as Prism 8 or SAS version 9.4.
In addition to timeline management, TimeLine offers seamless integration with other research tools, allowing researchers to streamline their workflows and enhance productivity.
Whether you're working with C57BL/6 mice, DMSO, STZ, or Long-Evans rats, TimeLine can be a valuable asset in your research arsenal.
With its AI-powered features and user-friendly design, TimeLine empowers researchers to take control of their research projects, optimize resource utilization, and improve collaboration with their peers.
This innovative tool is a game-changer for researchers seeking to enhance the efficiency and impact of their work.