Measuring the Environmental Efficiency and Technology Gap of PM2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model.
Since air pollution is an important factor hindering China’s economic development, China has passed a series of bills to control air pollution. However, we still lack an understanding of the status of environmental efficiency in regard to air pollution, especially PM2.5 (diameter of fine particulate matter less than 2.5 μm) pollution. Using panel data on ten major Chinese city groups from 2004 to 2016, we first estimate the environmental efficiency of PM2.5 by epsilon-based measure (EBM) meta-frontier model. The results show that there are large differences in PM2.5 environmental efficiency between cities and city groups.
The cities with the highest environmental efficiency are the most economically developed cities and the city group with the highest environmental efficiency is mainly the eastern city group. Then, we use the meta-frontier Malmquist EBM model to measure the meta-frontier Malmquist total factor productivity index (MMPI) in each city group. The results indicate that, overall, China’s environmental total factor productivity declined by 3.68% and 3.49% when considering or not the influence of outside sources, respectively.
Finally, we decompose the MMPI into four indexes, namely, the efficiency change (EC) index, the best practice gap change (BPC) index, the pure technological catch-up (PTCU) index, and the frontier catch-up (FCU) index. We find that the trend of the MMPI is consistent with those of the BPC and PTCU indexes, which indicates that the innovation effect of the BPC and PTCU indexes are the main driving forces for productivity growth. The EC and FCU effect are the main forces hindering productivity growth.
An epistemological problem for integration in EBM.
Evidence-based medicine (EBM) calls for medical practitioners to “integrate” our best available evidence into clinical practice. A significant amount of the literature on EBM takes this integration to be unproblematic, focusing on questions like how to interpret evidence and engage with patient values, rather than critically looking at how these features of EBM can be implemented together.
Other authors have also commented on this gap in the literature, for example, identifying the lack of clarity about how patient preferences and evidence from trials is supposed to be integrated in practice. In this paper, I look at this issue from an epistemological perspective, (looking at how different types of knowledge in EBM can be used to make sounds judgements).
In particular, I introduce an epistemological issue for this integration problem, which I call the epistemic integration problem. This is essentially the problem of how we can use information that is both general (eg, about a population sample) and descriptive (eg, about what expected outcomes are) to reach clinical judgements that are individualized (applying to a particular patient) and normative (about what is best for their health).

Nimura lecture: “Three EBMs“.
The three EBMs in the title refer to the following concepts: evidence-based medicine, experience-based medicine, and echo-based medicine. Evidence-based medicine: I have carried out the following clinical research using transthoracic Doppler echocardiography: (1) noninvasive pulsed-wave Doppler echocardiographic detection of the direction of shunt flow in patients with atrial septal defect: usefulness of the right parasternal approach (1985), (2) significance of laminar systolic regurgitant flow in patients with tricuspid regurgitation: a combined pulsed-wave, continuous-wave, and two-dimensional echocardiography (1990), (3) obstruction of the inferior vena caval orifice by the giant left atrium in patients with mitral stenosis: a Doppler echocardiographic study from the right parasternal approach (1992), and (4) demonstration of a localized acceleration flow signal in the transmural penetrating coronary artery using transthoracic color and pulsed-wave Doppler echocardiography in patients with hypertrophic cardiomyopathy (1996-2017).
Experience-based medicine: Dr. Eugene Braunwald says “The best book of cardiology is the patient itself.” I have conducted my modest research activities gleaning hints through day-to-day routine work and sometimes investigating experimentally using the Doppler echocardiographic method. I have also learned from the Japanese Society of Echocardiography that a physician should stand between evidence-based medicine and experience-based medicine. Echo-based medicine:
This term is intended to express my personal determination. I believe that echocardiography is the stethoscope of the 21st century. It is a safe, painless, low-cost, and repeatable tool at the bedside. I expect that echocardiography can reduce unnecessary healthcare costs and appropriately select reasonable examinations for patients. I would like to devote the time left in my career to the study of cardiovascular medicine, believing in the power of echocardiography and the Doppler method to provide a link between evidence-based medicine and experience-based medicine.
An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology.
Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme.
First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis.
GFP Expressing Human Renal Adenocarcinoma Cells (ACHN) |
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TR04-GFP | Neuromics | 500,000 Cells | EUR 1354 |
GFP Expressing Human Prostate Carcinoma Cells (DU 145) |
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TR03-GFP | Neuromics | 500,000 Cells | EUR 1354 |
EBM Recombinant Protein (Human) |
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RP055977 | ABM | 100 ug | Ask for price |
Lenti-HOXA9-GFP Lentivirus |
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LV640 | ABM | 10 ml | EUR 811 |
Lenti-HOXB8-GFP Lentivirus |
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LV634 | ABM | 10 ml | EUR 811 |
Lenti-HOXA10-GFP Lentivirus |
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LV646 | ABM | 10 ml | EUR 811 |
pMIR-Reporter-RASA1(3 |
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PVTB00444-2a | Lifescience Market | 2 ug | EUR 356 |
pMIR-Reporter-IL13(3 |
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PVTB00445-2a | Lifescience Market | 2 ug | EUR 356 |
EBM sgRNA CRISPR Lentivector (Human) (Target 3) |
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K0650104 | ABM | 1.0 ug DNA | EUR 154 |
Rev-CEM-GFP HIV Reporter Cells |
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HRC-4 | 101Bio | - | Ask for price |
Rev-A3R5-GFP HIV Reporter Cells |
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HRC-1 | Cusabio | - | Ask for price |
EBM Lentiviral Vector (Human) (CMV) (pLenti-GIII-CMV-GFP-2A-Puro) |
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LV723685 | ABM | 1.0 ug DNA | Ask for price |
EBM Protein Vector (Human) (pPB-C-His) |
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PV074637 | ABM | 500 ng | Ask for price |
EBM Protein Vector (Human) (pPB-N-His) |
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PV074638 | ABM | 500 ng | Ask for price |
EBM Protein Vector (Human) (pPM-C-HA) |
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PV074639 | ABM | 500 ng | Ask for price |
EBM Protein Vector (Human) (pPM-C-His) |
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PV074640 | ABM | 500 ng | Ask for price |
Rev-A3R5-GFP/Luc HIV Reporter Cells |
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HRC-2 | 101Bio | - | Ask for price |
Rev-CEM-GFP/Luc HIV Reporter Cells |
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HRC-5 | 101Bio | - | Ask for price |
pGL3 3'UTR reporter WT 1.3 kb CD274 Hs 3'UTR Final Plasmid |
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PVT17094 | Lifescience Market | 2 ug | EUR 325 |
EBM Lentiviral Vector (Human) (CMV) (pLenti-GIII-CMV) |
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LV723683 | ABM | 1.0 ug DNA | Ask for price |
EBM Lentiviral Vector (Human) (UbC) (pLenti-GIII-UbC) |
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LV723687 | ABM | 1.0 ug DNA | Ask for price |
EBM Lentiviral Vector (Human) (EF1a) (pLenti-GIII-EF1a) |
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LV723688 | ABM | 1.0 ug DNA | Ask for price |
EBM sgRNA CRISPR Lentivector set (Human) |
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K0650101 | ABM | 3 x 1.0 ug | EUR 339 |
Lenti-HOXB8-GFP Lentivirus, High Titer |
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LV637 | ABM | 5 x 20 ul | EUR 1521 |
Lenti-HOXA9-GFP Lentivirus, High Titer |
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LV643 | ABM | 5 x 20 ul | EUR 1521 |
Lenti-HOXA10-GFP Lentivirus, High Titer |
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LV649 | ABM | 5 x 20 ul | EUR 1521 |
pMIR- Reporter Plasmid |
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PVT1324 | Lifescience Market | 2 ug | EUR 266 |
2C::tdTomato Reporter |
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PVT10473 | Lifescience Market | 2 ug | EUR 266 |
Green Fluorescent Protein (GFP-fusion protein) ELISA Kit, 96 tests, Quantitative |
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800-420-GFP | Alpha Diagnostics | 1 kit | EUR 712 |
lenti dCAS-VP64_Blast vector |
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PVT12113 | Lifescience Market | 2 ug | EUR 352 |
NF-kB/Jurkat/GFP Transcriptional Reporter Cell Line |
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TR850A-1 | SBI | >2 x 10^6 cells | EUR 3843 |
EBM sgRNA CRISPR Lentivector (Human) (Target 1) |
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K0650102 | ABM | 1.0 ug DNA | EUR 154 |
EBM sgRNA CRISPR Lentivector (Human) (Target 2) |
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K0650103 | ABM | 1.0 ug DNA | EUR 154 |
Lenti-CMV-hTERT-GFP-2A-Puro Virus |
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LV623 | ABM | 10 ml | EUR 1059 |
Luciferase Reporter Assay Kit |
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K2181-200 | ApexBio | 200 assays | EUR 181 |
Luciferase Reporter Assay Kit |
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K801-200 | Biovision | EUR 196 |
pGL3- FOXO- Reporter- Luc |
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PVT10792 | Lifescience Market | 2 ug | EUR 301 |
pTAL- p53- Reporter- Luc |
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PVT10822 | Lifescience Market | 2 ug | EUR 301 |
TFEB promoter-luciferase reporter |
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PVT18227 | Lifescience Market | 2 ug | EUR 300 |
Luciferase Reporter Assay Kit |
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55R-1540 | Fitzgerald | 200 assays | EUR 245 |
Description: Assay Kit for detection of Luciferase Reporter in the research laboratory |
NF-kB/293/GFP-Luc Transcriptional Reporter Cell Line |
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TR860A-1 | SBI | >2 x 10^6 cells | EUR 3263 |
EBM sgRNA CRISPR/Cas9 All-in-One Lentivector (Human) (Target 3) |
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K0650108 | ABM | 1.0 ug DNA | EUR 167 |
lenti- sgRNA- TagRFP- uspzz- 3 Plasmid |
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PVT7232 | Lifescience Market | 2 ug | EUR 266 |
CuO-GFP-T2A-Luciferase Cumate-Inducible Dual Reporter Enhanced Episomal Vector (EEV) |
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EEV605A-1 | SBI | 10 ug | EUR 736 |
Jurkat T / GFP Stable Cell (CD43 Promoter) |
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SC049-3 | GenTarget | 2 x 106 cell/ml x 1ml | EUR 1500 |
Description: GFP expression stable cell line in human Jurkat T Cells with Puromycin resistance. GFP is expressed under the promoter of human CD43 gene. |
pLL-CMV-GFP-T2A-Puro [Lenti-LabelerTM plasmid] |
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LL100PA-1 | SBI | 10 ug | EUR 675 |
pLL-CMV-GFP-T2A-Puro [Lenti-LabelerTM virus] |
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LL100VA-1 | SBI | >2x10^6 IFUs | EUR 675 |
pLL-CMV-GFP-T2A-Blast [Lenti-LabelerTM plasmid] |
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LL105PA-1 | SBI | 10 ug | EUR 675 |
pLL-CMV-GFP-T2A-Blast [Lenti-LabelerTM virus] |
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LL105VA-1 | SBI | >2x10^6 IFUs | EUR 675 |
pLL-EF1a-GFP-T2A-Puro [Lenti-LabelerTM plasmid] |
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LL200PA-1 | SBI | 10 ug | EUR 675 |
pLL-EF1a-GFP-T2A-Puro [Lenti-LabelerTM virus] |
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LL200VA-1 | SBI | >2x10^6 IFUs | EUR 675 |
pLL-EF1a-GFP-T2A-Blast [Lenti-LabelerTM plasmid] |
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LL205PA-1 | SBI | 10 ug | EUR 675 |
pLL-EF1a-GFP-T2A-Blast [Lenti-LabelerTM virus] |
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LL205VA-1 | SBI | >2x10^6 IFUs | EUR 675 |
pLL-CMV-rFLuc-T2A-GFP [Lenti-LabelerTM plasmid] |
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LL300PA-1 | SBI | 10 ug | EUR 697 |
pLL-CMV-rFLuc-T2A-GFP [Lenti-LabelerTM virus] |
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LL300VA-1 | SBI | >2x10^6 IFUs | EUR 697 |
Lenti-hTERT virus |
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G200 | ABM | 10 ml | EUR 811 |
Lenti-SV40 Lentivirus |
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G203 | ABM | 10 ml | EUR 811 |
Lenti-SV40T Lentivirus |
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G256 | ABM | 10 ml | EUR 735 |
Lenti-SV40Tt Lentivirus |
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G258 | ABM | 10 ml | EUR 735 |
Lenti-Bmi1 Virus |
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LV610 | ABM | 10 ml | EUR 811 |
Lenti-CDK4 Virus |
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LV611 | ABM | 10 ml | EUR 811 |
Lenti- Cas9- Puro |
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PVT10981 | Lifescience Market | 2 ug | EUR 266 |
EBM Lentiviral Vector (Human) (CMV) (pLenti-GIII-CMV-C-term-HA) |
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LV723684 | ABM | 1.0 ug DNA | Ask for price |
EBM Lentiviral Vector (Human) (CMV) (pLenti-GIII-CMV-RFP-2A-Puro) |
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LV723686 | ABM | 1.0 ug DNA | Ask for price |
Genome editeid iPSCs (KI) Lineage-specific reporter AAVS-DCX-GFP |
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ASE-9502 | Applied StemCell | 1 vial (1 x 10^6) | EUR 3087.5 |
Description: 12 month |
Lentiviral Dual Reporter: CMV-GFP-T2A-Luciferase plasmid (10 ug) |
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BLIV101PA-1 | SBI | 10 ug | EUR 803 |
Lentiviral Dual Reporter: CMV-GFP-T2A-Luciferase pre-packaged virus |
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BLIV101VA-1 | SBI | >2 x10^6 IFUs | EUR 803 |
Minicircle Dual Reporter: CMV-GFP-T2A-Luciferase DNA (30 ug) |
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BLIV501MC-1 | SBI | 30 ug | EUR 716 |
Minicircle Dual Reporter: EF1-GFP-T2A-Luciferase DNA (30 ug) |
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BLIV503MC-1 | SBI | 30 ug | EUR 716 |
Minicircle Dual Reporter: UBC-GFP-T2A-Luciferase DNA (30 ug) |
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BLIV505MC-1 | SBI | 30 ug | EUR 716 |
Minicircle Dual Reporter: MSCV-GFP-T2A-Luciferase DNA (30 ug) |
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BLIV507MC-1 | SBI | 30 ug | EUR 716 |
Single-Luciferase Reporter Assay Kit |
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20-abx098133 | Abbexa |
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Double-Luciferase Reporter Assay Kit |
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20-abx098134 | Abbexa |
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Dual Luciferase Reporter Assay Kit |
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DL101-01 | Vazyme | 100 rxn | EUR 258 |
Oct4 CR4-pGreenFire Response Reporter |
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SR20070-PA-1 | SBI | 10 ug | EUR 1749 |
Sox2 SRR2-pGreenFire Response Reporter |
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SR20071-PA-1 | SBI | 10 ug | EUR 1749 |
NLS-AmCherry1-NES reporter (pDN160) |
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PVT12190 | Lifescience Market | 2 ug | EUR 703 |
NLS-mCherry-NES reporter (pDN160) |
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PVT12293 | Lifescience Market | 2 ug | EUR 703 |
Luciferase Reporter Gene Assay Kit |
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Z5030001 | Biochain | 200 assays | EUR 358 |
Description: Premade ready to use kits will always come in handy. Get your experiment done right form the first try by using a validated kit with perfectly balanced reagents proportions and compatibility and by following a clear protocol. |
Luciferase Reporter Gene Assay Kit |
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Z5030002 | Biochain | 500 assays | EUR 647 |
Description: Premade ready to use kits will always come in handy. Get your experiment done right form the first try by using a validated kit with perfectly balanced reagents proportions and compatibility and by following a clear protocol. |
Luciferase Reporter Gene Assay Kit |
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Z5030003 | Biochain | 1,000 assays | EUR 1100 |
Description: Premade ready to use kits will always come in handy. Get your experiment done right form the first try by using a validated kit with perfectly balanced reagents proportions and compatibility and by following a clear protocol. |
lenti-EF1a-dCas9-KRAB-Puro vector |
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PVT12069 | Lifescience Market | 2 ug | EUR 352 |
Anti-14-3-3 alpha + beta Rabbit Monoclonal Antibody |
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M02431-3 | BosterBio | 100ug/vial | EUR 397 |
Description: Rabbit Monoclonal 14-3-3 alpha + beta Antibody. Validated in Flow Cytometry, IP, IF, IHC, ICC, WB and tested in Human, Mouse, Rat. |
IL-3 Interleukin-3 Human Recombinant Protein, His Tag |
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PROTP08700-3 | BosterBio | Regular: 50ug | EUR 317 |
Description: Interleukin-3 Human Recombinant produced in E.Coli is single, a non-glycosylated, Polypeptide chain containing 154 amino acids fragment (20-152) and having a total molecular mass of 17.3kDa and fused with a 20 aa N-terminal His tag. ;The IL3 His is purified by proprietary chromatographic techniques. |
Lenti-CMV-hTERT-GFP-2A-Puro Virus, High Titer |
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LV624 | ABM | 5 x 20 ul | EUR 1826 |
3-D Life Thioglycerol |
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T10-3 | Cellendes | 180 µl | EUR 48 |
Individual Reaction Mix 3 |
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G065-3 | ABM | 200 reactions | EUR 167 |
Western Blot Box - 2 7/8 x 1 3/16 x 3/4in.; 7.3 x 3 x 1.9cm |
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B1200-3 | MTC Bio | 5/pack | EUR 63.77 |
Description: Western Blot Boxes |
Minicircle Dual Reporter: CMV-GFP-T2A-Luciferase Parental Plasmid (10 ug) |
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BLIV501MN-1 | SBI | 10 ug | EUR 972 |
Minicircle Dual Reporter: EF1-GFP-T2A-Luciferase Parental Plasmid (10 ug) |
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BLIV503MN-1 | SBI | 10 ug | EUR 972 |
Minicircle Dual Reporter: UBC-GFP-T2A-Luciferase Parental Plasmid (10 ug) |
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BLIV505MN-1 | SBI | 10 ug | EUR 972 |
Minicircle Dual Reporter: MSCV-GFP-T2A-Luciferase Parental Plasmid (10 ug) |
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BLIV507MN-1 | SBI | 10 ug | EUR 972 |
TGF-b-3 Transforming Growth Factor-Beta 3 Human Recombinant Protein, Plant |
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PROTP10600-3 | BosterBio | Regular: 5ug | EUR 317 |
Description: TGFB3 Human Recombinant produced in plant is a disulfide-linked homodimeric, glycosylated, polypeptide chain containing 118 amino acids and having a molecular mass of 27.2kDa. ;The TGFB3 is fused to 6xHis tag at N-terminus and purified by standard chromatographic techniques. |
3-D Life PEG-Link |
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L50-3 | Cellendes | 3x 200 µl | EUR 94 |
The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.