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Projection

Q: What are some common challenges in using projection techniques?

Some of the key challenges in using projection techniques include accounting for unexpected events, handling missing or incomplete data, and addressing biases in the underlying data or assumptions. Researchers must also be mindful of the limitations of their models and the inherent uncertainty in any projection. Careful sensitivity analysis and validation of results are crucial to ensuring the reliability of projection-based insights.

Q: How can PubCompare.ai help with projection and research optimization?

PubCompare.ai is an AI-driven platform that can help researchers improve the reproducibility and accuracy of their projection-based studies. The platform allows you to more efficiently screen protocol literature, leveraging AI to pinpoint critical insights. PubCompare.ai can help you identify the most effective protocols related to projection for your specific research goals, highlighting key differences in protocol effectiveness so you can choose the best option for your needs. This can lead to more reliable and reproducible projection-based research.

Q: What are some key considerations when choosing projection techniques for research?

When selecting projection techniques for research, it's important to consider the nature of the data, the timescale of the projection, and the level of precision required. Different techniques, such as time series analysis, econometric modeling, or machine learning-based forecasting, may be more appropriate depending on the research context. Researchers should also carefuly evaluate their assumptions, data sources, and potential sources of error or bias. Propper planning and validation are crucial to ensuring the validity and usefulnes of projection-based research findings.

Projection is the process of estimating or forecasting future events or trends based on current data and information.
It involves the use of mathematical models, statistical techniques, and AI-driven algorithms to make predictions about future outcomes.
Projections can be used in a variety of fields, such as economics, demographics, and scientific research, to help inform decision-making and planning.
Effective projection requires careful consideration of underlying assumptions, data quality, and potential sources of uncertainty.
Researchers can leverage AI-driven platforms like PubCompare.ai to optimize their projection and research, improving reproducability and accuracy.

Most cited protocols related to «Projection»

CryoEM Movie-Frame Defocus Refinement

One of the biggest advances in cryoEM recently is the invention of direct electron detectors which allow movie recording. Beam induced movement correction using movies has greatly improved the resolution of the final reconstruction (Bai et al., 2013 (link), Li et al., 2013 (link)). The movement in the X or Y direction of a micrograph is usually around several Ångstroms (e.g. 1–10 Å), while the Z-direction movement can be over a hundred Ångstroms (Russo and Passmore, 2014 (link)). Although the movement is dominantly in the Z-direction, the small movement in the XY plane severely affects the quality of cryoEM micrographs. Motion correction programs normally consider only the drift in the XY plane because the eucentric height of the object does not affect its ideal 2D projection. However, EM micrographs are modulated by CTF, which is sensitive to Z-height changes. Beam induced movement might change the CTF from frame to frame. A hundred Ångstrom movement is not a significant change even up to a 3 Å reconstruction, but Fig. 1 suggests it might help to improve a reconstruction close to 2 Å.
Accurate defocus refinement for movie frames is implemented in Gctf to deal with large movement in the Z-direction. Similar to local defocus refinement, movie defocus refinement is performed in two steps. First, global CTF parameters are determined for the averaged micrograph of motion-corrected movies. Then based on the global values, parameters for each frame are refined using an equally weighted average of adjacent frames (suggested 5–10) to reduce the noise. Two options are provided in Gctf: coherent averaging Eq. (8) or incoherent averaging Eq. (9).

| F_{i_{ca}} (\vec{s}) | = |\sum_{j = i - N / 2}^{i + N / 2} \frac{F_{j} (\vec{s})}{N}|

| F_{i_{ica}} (\vec{s}) | = \sum_{j = i - N / 2}^{i + N / 2} \frac{| F_{j} (\vec{s}) |}{N}

where

| F_{i_{ca}} (\vec{s}) |

represents the coherent averaging of

i_{th}

frame and

i_{th}

the incoherent averaging;

N

is the number of frames to be averaged.

, & Zhang K. (2016). Gctf: Real-time CTF determination and correction. Journal of Structural Biology, 193(1), 1-12.

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Publication 2016

Cryoelectron Microscopy Electrons Movement Reading Frames Reconstructive Surgical Procedures

Molecular Frame Photoelectron Distributions

The photoelectron momentum distributions with respect to the molecular axis shown in Fig. 2 were generated in the following way. Initially, the ions were assigned to the one of the two breakup channels, direct and indirect, by requiring the magnitude of the ion momentum to be within 3.5–17 a.u. and 37–46 a.u., respectively. This gating ensure that the ion comes from the breakup of the dimer along II(1/2)_g state (Fig. 1a). The ionization of atomic neon as well as dissociation over the other potential curves^{43 (link)} would result in the ion momentum smaller than 3 a.u. Subsequently, only ionization events have been considered, where ion and electron momentum vectors lie within slices along the polarization plane, defined by the conditions |p_x| < 0.55 a.u. for electrons as well as |p_x| < 3.5 a.u. and |p_x| < 12.0 a.u. for ions from the direct and indirect dissociation channels, respectively (the x-direction is the light propagation direction). These conditions ensure that the angle between a momentum vector and the polarization plane does not exceed 45° in the worst case. For the majority of events this angle is, however, smaller than 30°. Both, electron and ion momentum vectors were projected onto the polarization plane. The projection of the ion momentum defines the k_|| direction, whereas the two components, k_|| and k_⊥, of the electron projection are plotted in Fig. 2. This type of molecular frame transformation avoids nodes along the dimer axis. It does not conserve the product k·R, but the loss of contrast in the interference patterns is negligible. Another type of transformation, a natural one, where the ion momentum vector, not its projection, defines the k_|| direction is presented in the Supplementary Note 3 and Supplementary Fig. 4.

Kunitski M., Eicke N., Huber P., Köhler J., Zeller S., Voigtsberger J., Schlott N., Henrichs K., Sann H., Trinter F., Schmidt L.P., Kalinin A., Schöffler M.S., Jahnke T., Lein M, & Dörner R. (2019). Double-slit photoelectron interference in strong-field ionization of the neon dimer. Nature Communications, 10, 1.

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Publication 2019

Cloning Vectors Electrons Epistropheus Light Neon Product R Reading Frames

Consensus Assembly of Strain Mixtures

Genomic differences between related strains often result in “bulges” and “tips” in the de Bruijn graphs that are not unlike artifacts caused by sequencing errors in genome assembly (Pevzner et al. 2004 (link); Zerbino and Birney 2008 (link)). For example, a sequencing error often results in a bulge formed by two short alternative paths between the same vertices in the de Bruijn graph, a “correct” path with high coverage and an “erroneous” path with low coverage. Similarly, a substitution or a small indel in a rare strain (compared with an abundant strain) often results in a bulge formed by a high-coverage path corresponding to the abundant strain and an alternative low-coverage path corresponding to the rare strain.
Aiming at the consensus assembly of a strain mixture, metaSPAdes masks the majority of strain differences using a modification of the SPAdes procedures for masking sequencing errors (the algorithms for removal of tips, “simple” bulges [Bankevich et al. 2012 (link)], and “complex” bulges [Nurk et al. 2013 (link)]). metaSPAdes uses more aggressive settings than the ones used in assemblies of isolates; for example, it collapses larger bulges and removes longer tips than SPAdes. We note that the bulge projection approach in SPAdes improves on the originally proposed bulge removal approach (Pevzner et al. 2004 (link); Zerbino and Birney 2008 (link)) used in most existing assemblers since it stores valuable information about the processed bulges (see “Bulge Projection Approach” in the Supplemental Material). This feature is important for the repeat resolution approach in metaSPAdes described below.

Nurk S., Meleshko D., Korobeynikov A, & Pevzner P.A. (2017). metaSPAdes: a new versatile metagenomic assembler. Genome Research, 27(5), 824-834.

Publication 2017

Genome INDEL Mutation Shock Strains

Anchored Profile-Profile Alignment Protocol

The alignment anchors computed at node are used to perform an anchored profile-profile global alignment with modified MUSCLE 3.7 software [44] (link). Global profile-profile alignment requires the input sequences to be free from rearrangement. Therefore, we partition the anchors in into groups that are free from breakpoints in any pairwise projection. A fully fledged locally collinear block at node , no longer constrained to two dimensions, is a maximal set in which each pair-wise projection of into and in is contained in a common pair-wise LCB in . One or more of the original pair-wise LCBs from may be truncated by this restriction, and hence the partitioning into LCBs at node can be thought of as the intersection among constituent pairwise LCBs. Then each LCB in is independently subjected to anchored profile-profile alignment using methods described elsewhere [44] (link). In order to capture the full region of homology at the boundaries of each LCB, sequence regions outside LCBs are randomly split and assigned to neighboring LCBs. An example is shown with the yellow regions in Figure 2 step 5.
After the initial profile-profile alignment, we then apply window-based iterative refinement to improve the alignment. Step 6 of Figure 2 corresponds to this process. Importantly, MUSCLE refines the alignment with a multitude of alternative guide trees and is not restricted to the guide tree chosen for progressive anchoring. The use of multiple guide trees is a particularly important feature in microbial genomes, which are subject to lateral gene transfer. It should be noted that our use of MUSCLE as a refinement step is an approach used in other software pipelines as well [45] (link).

Darling A.E., Mau B, & Perna N.T. (2010). progressiveMauve: Multiple Genome Alignment with Gene Gain, Loss and Rearrangement. PLoS ONE, 5(6), e11147.

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Publication 2010

Gene Transfer, Horizontal Genome, Microbial Muscle Tissue Trees

Visualizing Somatosensory Cortex Projections

EGFP-Cre (Addgene) was cloned into the pAAV-MCS vector (Stratagene). The resultant recombinant viral vector was packaged in the capsid of serotype 8, and high-titer virus (approximately 10¹³ genome copy (gc)/ml) was produced by Harvard Gene Therapy Initiative (HGTI). To visualize the projections from the somatosensory cortex, 1–2 μl of rAAV-EGFP-Cre (1.6 × 10¹³ gc/ml) was injected into anesthetized Ai14 mice at corresponding stereotaxic coordinates using a glass micropipette attached to a Picospritzer (Parker Hannifin). The virus was administered slowly by a number of low pressure air puffs to minimize tissue damage (10 psi, 10–20 ms duration, 2 Hz and 10 min/μl). Mice were then recovered and housed individually until they were utilized for further analysis.

Madisen L., Zwingman T.A., Sunkin S.M., Oh S.W., Zariwala H.A., Gu H., Ng L.L., Palmiter R.D., Hawrylycz M.J., Jones A.R., Lein E.S, & Zeng H. (2009). A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nature neuroscience, 13(1), 133-140.

Publication 2009

Air Pressure Capsid Proteins Cloning Vectors Genome Mice, House Somatosensory Cortex Therapy, Gene Tissues Virus

Most recents protocols related to «Projection»

Surgical Stapler with Releasable Clamping Mechanism

Not available on PMC !

Example 20

A surgical stapler comprising: (a) a first half comprising: (i) a first elongate member, (ii) a surface having a plurality of staple forming pockets, (iii) a distal latch projection, (iv) a proximal pivot projection, and (v) a detent member extending laterally outwardly from the first elongate member; and (b) a second half configured to releasably couple with the first half, wherein the second half comprises: (i) a second elongate member having a distal portion operable to deploy staples toward the first half and at least one proximal notch configured to pivotably receive the proximal pivot projection of the first half, (ii) a latching lever pivotably coupled to the second elongate member and configured to selectively engage the distal latch projection for clamping the first half against the second half to thereby define a clamped state of the surgical stapler, and (iii) a shoulder member configured to selectively engage the detent member in response to rotation of the first half away from the second half about the proximal pivot projection to an open state in which the first and second elongate members assume a predetermined maximum angular orientation relative to one another and remain releasably coupled together at their proximal ends.

US11864758B2. Surgical linear cutter wishbone separation mechanism with detent (2024-01-09). Cilag GmbH International [CH]. Inventors: Brian D. Schings [US].

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Patent 2024

Operative Surgical Procedures Shoulder Staple, Surgical Surgical Staplers

Photolithographic 3D Printing System

Not available on PMC !

Example 3

To print structures, a custom built system shown schematically in FIG. 1 was used, equipped with a 405 nm light source. A three-dimensional CAD model was sliced vertically into a series of layers. Using a spatial light modulator (SLM) as a dynamically reconfigurable digital photomask, each two-dimensional image slice was sequentially transmitted to the SLM which took on the pattern of the image. UV light illuminated the SLM from a photodiode, and a patterned beam was reflected. The patterned beam was reflected by a galvanometer mirror pair onto a flat-field scan lens which flattened the images and acted as a reduction optic before passing the light onto the photoresin. Where two-dimensional image hit the resin a solid forms. The galvanometer mirror and scan lens allowed the patterned 2D image to be scanned over a large area, creating a mosaic of highly detailed cells. Subsequently, the substrate on which the layer rests was lowered, reflowing a thin film of liquid over the cured layer. The image projection was then repeated, with the next image slice forming the subsequent layer.

US11880133B2. Architected three dimensional graphene via additive manufacturing (2024-01-23). LAWRENCE LIVERMORE NATIONAL SECURITY, LLC [US]. Inventors: Marcus A Worsley [US], Patrick G. Campbell [US], Eric B. Duoss [US], James S. Oakdale [US], Christopher M. Spadaccini [US], Ryan Hensleigh [US].

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Patent 2024

Cells Eye Fingers Graphene Lens, Crystalline Resins, Plant REST protein, human Ultraviolet Rays

Invasive LV Pressure and CAD Evaluation

A total of 42 patients, who underwent LV catheterization for coronary angiography, were prospectively included. The invasive LV pressure was recorded. The LV dp/dt min, tau and LVEDP were averaged over 3–6 cardiac cycles. An LVEDP value of > 16 mmHg was defined as an elevated LV filling pressure [23 (link)]. The invasive values were measured by two researchers, who were blinded to the results of the MW measurements. All patients underwent coronary angiography with multiple projections. CAD was defined when the lumen was stenotic for more than 50% in one or more major epicardial coronary arteries [24 (link)].

Guo Y., Wang X., Yang C.G., Meng X.Y., Li Y., Xia C.X., Xu T., Weng S.X., Zhong Y., Zhang R.S, & Wang F. (2023). Noninvasive assessment of myocardial work during left ventricular isovolumic relaxation in patients with diastolic dysfunction. BMC Cardiovascular Disorders, 23, 129.

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Publication 2023

Angiography Artery, Coronary Catheterization Catheterizations, Cardiac Coronary Angiography Heart Patients Pressure Stenosis

Cryopreservation and Immunostaining of Retinal Organoids

The AON-treated ROs were fixed in 2% paraformaldehyde (Thermo Fisher Scientific) and 5% sucrose (Thermo Fisher Scientific) for 15 min at 4°C, followed by a 30-min incubation in 7.5% sucrose, 30 min in 15% sucrose, and 2 h incubation in 30% sucrose. The organoids were transferred to a cryomold and embedded in 7.5% gelatin (porcine skin; Merck KGaA) and 10% sucrose. The sample blocks were then frozen at −80°C. Sections of 10 μm thickness were sliced on a Cryotome FSE (Thermo Fisher Scientific), rehydrated in PBS, and stained following the protocol described by Cowan et al.⁴⁶ (link) ABCA4 was detected using the anti-ABCA4 3F4 clone (1:100; Abcam, Cambridge, UK), rhodopsin was stained using the anti-rhodopsin 4D2 clone (1:300, Invitrogen), mitochondria were detected with an anti-MTCO2 antibody (1:150; Abcam), and nuclei were stained with Hoechst 33,342 (1:1000; Thermo Fisher Scientific). Images were collected on an LSM 800 confocal microscope (Carl Zeiss, Oberkochen, Germany) using a 60× objective and analyzed with ZEN Blue edition (Carl Zeiss) using the maximum intensity projection.

Kaltak M., de Bruijn P., Piccolo D., Lee S.E., Dulla K., Hoogenboezem T., Beumer W., Webster A.R., Collin R.W., Cheetham M.E., Platenburg G, & Swildens J. (2023). Antisense oligonucleotide therapy corrects splicing in the common Stargardt disease type 1-causing variant ABCA4 c.5461-10T>C. Molecular Therapy. Nucleic Acids, 31, 674-688.

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Publication 2023

Antibodies, Anti-Idiotypic Cell Nucleus Clone Cells Freezing Gelatins Microscopy, Confocal Mitochondria Organoids paraform Pigs Rhodopsin Skin Sucrose

Gut Microbiota and Metabolite Analysis

Student’s t-test, nonparametric Mann-Whitney, or Pearson correlation analysis was used when appropriate. Here, four parameters (ace, chao, shannon and simpson) were calculated to evaluate the alpha diversity of gut microbiota. The principal coordinate analysis (PCoA) was used to assess the beta diversity of gut microbiota. The linear discriminant-analysis (LDA) effect size (LEfSe) was conducted to identify the differential gut microbiota between the two groups, and the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database was performed to predict the potential functions of the differential gut microbiota. To identify the differential microbial metabolites (variable importance in projection (VIP) > 1.0 and p-value<0.05) between the two groups, the orthogonal partial least squares (OPLS) model was built using microbial metabolites. All the analyses was carried out using SPSS 19.0, R software 4.0 and Cytoscape 5.0, and p<0.05 was considered to be statistically significant.

Xie J., Wu W.T., Chen J.J., Zhong Q., Wu D., Niu L., Wang S., Zeng Y, & Wang Y. (2023). Tryptophan metabolism as bridge between gut microbiota and brain in chronic social defeat stress-induced depression mice. Frontiers in Cellular and Infection Microbiology, 13, 1121445.

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Publication 2023

Gastrointestinal Microbiome Genome Student

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Sp8 confocal microscope by Leica

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Lsm 800 by Zeiss

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The LSM 800 is a laser scanning microscope designed for high-resolution imaging. It features a confocal optical system that allows for the acquisition of optical sections, enabling the visualization of 3D structures within a sample.

What is projection and how is it used in research?

Projection is the process of estimating or forecasting future events and trends based on current data and information. It involves using mathematical models, statistical techniques, and AI-driven algorithms to make predictions about future outcomes. Projection is widely used in fields like economics, demographics, and scientific research to inform decision-making and planning. Effective projection requires carefully considering underlying assumptions, data quality, and potential sources of uncertainty.

What are some common challenges in using projection techniques?

Some of the key challenges in using projection techniques include accounting for unexpected events, handling missing or incomplete data, and addressing biases in the underlying data or assumptions. Researchers must also be mindful of the limitations of their models and the inherent uncertainty in any projection. Careful sensitivity analysis and validation of results are crucial to ensuring the reliability of projection-based insights.

How can PubCompare.ai help with projection and research optimization?

PubCompare.ai is an AI-driven platform that can help researchers improve the reproducibility and accuracy of their projection-based studies. The platform allows you to more efficiently screen protocol literature, leveraging AI to pinpoint critical insights. PubCompare.ai can help you identify the most effective protocols related to projection for your specific research goals, highlighting key differences in protocol effectiveness so you can choose the best option for your needs. This can lead to more reliable and reproducible projection-based research.

What are some key considerations when choosing projection techniques for research?

When selecting projection techniques for research, it's important to consider the nature of the data, the timescale of the projection, and the level of precision required. Different techniques, such as time series analysis, econometric modeling, or machine learning-based forecasting, may be more appropriate depending on the research context. Researchers should also carefuly evaluate their assumptions, data sources, and potential sources of error or bias. Propper planning and validation are crucial to ensuring the validity and usefulnes of projection-based research findings.

More about "Projection"

Forecasting, Predictive Modeling, Trend Analysis, Projection Methods, Statistical Modeling, AI-Driven Algorithms, Data-Driven Predictions, Econometrics, Demographic Projections, Scientific Forecasting, Reproducibility, Accuracy, PubCompare.ai, MATLAB, LSM 710, LSM 880, ZEN Software, LSM 700, LSM 780, LSM 710 Confocal Microscope, SP8 Confocal Microscope, SIMCA-P 14.1, LSM 800.
Projection is the process of estimating or forecasting future events or trends based on current data and information.
It involves the use of mathematical models, statistical techniques, and AI-driven algorithms to make predictions about future outcomes.
Projections can be used in a variety of fields, such as economics, demographics, and scientific research, to help inform decision-making and planning.
Effective projection requires careful consideration of underlying assumptions, data quality, and potential sources of uncertainty.
Researchers can leverage AI-driven platforms like PubCompare.ai to optimize their projection and research, improving reproducability and accuarcy.
PubCompare.ai helps locate optimal protocols from literature, pre-prints, and patents using AI-driven comparisons to identify the best protocols and products, unleashing the power of projection and research optimization.