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Journal articles on the topic 'MSS sets'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Pribyslavska, Veronika, Brett A. Davis, and John M. Coons. "Comparisons of Volume between Agonist-Antagonist Paired Sets and Traditional Sets." Medicine & Science in Sports & Exercise 48 (May 2016): 453. http://dx.doi.org/10.1249/01.mss.0000486361.32065.47.

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2

Kwon, Young Sub, Kyra B. Hans, and Soojin Yoo. "A Descending Resistance Sets Produces Greater Training Volume than a Constant Resistance Sets." Medicine & Science in Sports & Exercise 47 (May 2015): 938. http://dx.doi.org/10.1249/01.mss.0000479282.48481.39.

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3

Kelly, Stephen B., Lee E. Brown, Jared W. Coburn, Steven M. Zinder, Lisa M. Gardner, and Diamond Nguyen. "Effect of Single Vs. Multiple Sets for Strength." Medicine & Science in Sports & Exercise 39, Supplement (May 2007): S471. http://dx.doi.org/10.1249/01.mss.0000274866.32132.1b.

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4

Winett, Richard A. "ARE MULTIPLE SETS AND USUAL AEROBIC TRAINING RECOMMENDED FOR TREATMENT OF CHD?" Medicine & Science in Sports & Exercise 41, no. 3 (March 2009): 732. http://dx.doi.org/10.1249/mss.0b013e31819139d8.

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5

Hashish, Rami, Sachithra Samarawickrame, Man-Ying Wang, Shin-Yuan Yu, Liang-Ching Tsai, and George Salem. "The Influence Of Different Marker Sets On Lower Extremity Dynamics During Walking." Medicine & Science in Sports & Exercise 43, Suppl 1 (May 2011): 918. http://dx.doi.org/10.1249/01.mss.0000402565.73723.7e.

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6

Bottaro, Martim, João Veloso, Tiago Riera, Saulo Martorelli, and André Martorelli. "Effect Of Single Vs Multiple Sets Of Resistance Exercise On Muscle Strength." Medicine & Science in Sports & Exercise 41 (May 2009): 129. http://dx.doi.org/10.1249/01.mss.0000354948.62934.39.

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7

Krysander, Mattias, and Mattias Nyberg. "STRUCTURAL ANALYSIS FOR FAULT DIAGNOSIS OF DAE SYSTEMS UTILIZING MSS SETS." IFAC Proceedings Volumes 35, no. 1 (2002): 143–48. http://dx.doi.org/10.3182/20020721-6-es-1901.00755.

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8

Signorelli, Gabriel R., and Bruno L. Franco. "Influence of Single and Multiple Sets of Static Stretching on Bench Press Efforts." Medicine & Science in Sports & Exercise 39, Supplement (May 2007): S307. http://dx.doi.org/10.1249/01.mss.0000274193.60724.82.

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9

Jennings, Will, Jason D. Stone, John D. Mata, J. Craig Garrison, Shiho Goto, Margaret T. Jones, Andrew Jagim, Adam King, and Jonathan M. Oliver. "Cluster Sets Attenuate Power Loss at Higher Intensities During the Back Squat Exercise." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 419. http://dx.doi.org/10.1249/01.mss.0000536465.04851.5f.

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10

Chae, Sungwon, Margaret T. Jones, Jennifer B. Fields, Jonathan M. Oliver, James C. Boyett, Felipe C. Vechin, Jonathan L. Nicholson, Spencer A. Moses, and Jakob L. Vingren. "Cytokine Response to Traditional and Cluster Sets in Resistance-trained Women." Medicine & Science in Sports & Exercise 51, Supplement (June 2019): 797–98. http://dx.doi.org/10.1249/01.mss.0000562882.74478.c2.

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11

Miranda, Humberto, Gabriel A. Paz, Marianna de Freitas Maia, Vicente Pinheiro Lima, Cláudio Melibeu Bentes, and Carlos Gomes de Oliveira. "Training Volume and Muscle Fatigue During Agonist-antagonist Paired Sets Adopting Different Rest Intervals." Medicine & Science in Sports & Exercise 46 (May 2014): 818–19. http://dx.doi.org/10.1249/01.mss.0000495954.49984.45.

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12

Marin, Douglas P., Carlos E. Ribeiro Fonseca, and Aylton J. Figueira Junior. "Comparison Of Diferent Rest Intervals Among Sets On Bench Press And Leg Press Performance." Medicine & Science in Sports & Exercise 40, Supplement (May 2008): S256. http://dx.doi.org/10.1249/01.mss.0000322407.66924.61.

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13

Celes, Rodrigo, Martim Bottaro, João C. Veloso, Carlos Ernesto, and Lee E. Brown. "Effect of Rest-Interval Between Sets During Isokinetic Knee Extension in Untrained Young men." Medicine & Science in Sports & Exercise 40, Supplement (May 2008): S259. http://dx.doi.org/10.1249/01.mss.0000322419.91580.23.

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14

ALVARES, THIAGO, Cláudia M. Meirelles, Cristiane Matsuura, Yagesh N. Bhambhani, and Paulo S. C. Gomes. "L-arginine Supplementation Increases Muscle Blood Volume During Recovery After Sets Of Resistance Exercise." Medicine & Science in Sports & Exercise 40, Supplement (May 2008): S402. http://dx.doi.org/10.1249/01.mss.0000322713.56325.09.

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15

Xu, Junhai, and Arnold G. Nelson. "The Effects of Single versus Multiple Sets of Leg Presses on Myocardial Energy Expenditure." Medicine & Science in Sports & Exercise 51, Supplement (June 2019): 658. http://dx.doi.org/10.1249/01.mss.0000562465.52365.25.

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16

Cho, Austria L., Anthony B. Ciccone, Rex Hafenstine, Maria C. Pereira, Lee E. Brown, Jared W. Coburn, and Andrew J. Galpin. "Alternating Whole-Body Strength Training Increases Time to Peak Ground Reaction Force Across Multiple Sets." Medicine & Science in Sports & Exercise 46 (May 2014): 260. http://dx.doi.org/10.1249/01.mss.0000493968.78853.80.

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17

Lopes, Thiago R., Felipe S. P. de Freitas, Thiago N. Ferreira, José E. Succi, Said R. Rabbani, Antônio C. Silva, and Bruno M. Silva. "Effect of Ischemic Preconditioning on Performance and Physiological Responses to Multiple Sets of Repeated Sprints." Medicine & Science in Sports & Exercise 46 (May 2014): 265–66. http://dx.doi.org/10.1249/01.mss.0000493982.56876.6b.

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18

Miranda, Humberto, Gabriel Paz, Marianna F. Maia, Claudio M. Bentes, and Roberto Simão. "Effect of Different Rest Intervals Length Between Sets on Neuromuscular Fatigue During Leg Press Exercise." Medicine & Science in Sports & Exercise 47 (May 2015): 357. http://dx.doi.org/10.1249/01.mss.0000477394.94459.1b.

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19

HONG, JUNYOUNG, Tayler Trevino, John D. Smith, Corinna N. Ross, and sukho Lee. "Comparison of Multiple Sets and Single Set of Resistance Training on Muscle Strength and Power." Medicine & Science in Sports & Exercise 47 (May 2015): 409–10. http://dx.doi.org/10.1249/01.mss.0000477548.13553.03.

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20

Paz, Gabriel A., Marianna de Freitas Maia, Vicente Pinheiro Lima, and Humberto Miranda. "Hypotensive Effect After Traditional Set Versus Agonist-antagonist Paired Sets For Upper-Body Resistance Exercises." Medicine & Science in Sports & Exercise 46 (May 2014): 816–17. http://dx.doi.org/10.1249/01.mss.0000495948.19489.3c.

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21

Graham, Connor, Nathan B. Morris, and Ollie Jay. "Ad LibitumFluid Consumption off-sets Thermal and Cardiovascular Strain Exacerbated by Dehydration during Heat Waves." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 385. http://dx.doi.org/10.1249/01.mss.0000536354.90564.99.

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22

Slattery, Eric W., Andrew Renggli, Jarod Vance, and Randal Claytor. "Relationship Between Mechanical Work and Metabolic Cost of Multiple Sets of Resistance Exercise to Failure." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 434. http://dx.doi.org/10.1249/01.mss.0000536512.33559.9f.

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23

Suga, Tadashi, Koichi Okita, Noriteru Morita, Shingo Takada, Masashi Omokawaka, Masahiro Horiuchi, Takashi Yokota, Kagami Hirabayashi, Shintaro Kinugawa, and Hiroyuki Tsutsui. "Remarkable Effects Of Continuous Blood Flow Restriction During Multiple Sets Of Low Intensity Resistance Exercise." Medicine & Science in Sports & Exercise 41 (May 2009): 475. http://dx.doi.org/10.1249/01.mss.0000355998.42826.2b.

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24

Dannecker, Erin A., Ajay Aggarwal, and James A. Kenney. "Comparison Of Different Instructional Sets For Patient-Generated Indexes Of Pain Severity." Medicine & Science in Sports & Exercise 49, no. 5S (May 2017): 115. http://dx.doi.org/10.1249/01.mss.0000517139.53575.ff.

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25

MUNN, JOANNE, ROBERT D. HERBERT, MARK J. HANCOCK, and SIMON C. GANDEVIA. "Resistance Training for Strength: Effect of Number of Sets and Contraction Speed." Medicine & Science in Sports & Exercise 37, no. 9 (September 2005): 1622–26. http://dx.doi.org/10.1249/01.mss.0000177583.41245.f8.

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26

Roberts, David. "Statistical Analysis of Maximally Similar Sets in Ecological Research." Mathematics 6, no. 12 (December 11, 2018): 317. http://dx.doi.org/10.3390/math6120317.

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Maximally similar sets (MSSs) are sets of elements that share a neighborhood in a high-dimensional space defined by a symmetric, reflexive similarity relation. Each element of the universe is employed as the kernel of a neighborhood of a given size (number of members), and elements are added to the neighborhood in order of similarity to the current members of the set until the desired neighborhood size is achieved. The set of neighborhoods is then reduced to the set of unique, maximally similar sets by eliminating all sets that are permutations of an existing set. Subsequently, the within-MSS variability of candidate explanatory variables associated with the elements is compared to random sets of the same size to estimate the probability of obtaining variability as low as was observed. Explanatory variables can be compared for effect size by the rank order of within-MSS variability and random set variability, correcting for statistical power as necessary. The analyses performed identify constraints, as opposed to determinants, in the triangular distribution of pair-wise element similarity. In the example given here, the variability in spring temperature, summer temperature, and the growing degree days of forest vegetation sample units shows the greatest constraint on forest composition of a large set of candidate environmental variables.

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27

Kirk, Erik P., Tim Heden, Curt Lox, Paul Rose, and Steven Reid. "ACSM Single-set Resistance Training Protocol Increases Energy Expenditure Similar To Three Sets Up To 72h." Medicine & Science in Sports & Exercise 43, Suppl 1 (May 2011): 267. http://dx.doi.org/10.1249/01.mss.0000400734.69398.2f.

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28

Jenkins, Nathaniel D. M., Jessie M. Miller, Noelle Yeo, Cory M. Smith, Ethan C. Hill, Kristen C. Cochrane, Haley C. Bergstrom, Terry J. Housh, and Joel T. Cramer. "Electromyographic And Mechanomyographic Responses During Three Sets To Failure Of Low- Versus High-load Resistance Training." Medicine & Science in Sports & Exercise 47 (May 2015): 935. http://dx.doi.org/10.1249/01.mss.0000479271.43536.30.

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29

Bottaro, Martim, Carlos Ernesto, Rodrigo Celes, João Veloso, Lee E. Brown, and Ricardo J. Oliveira. "Do Older Men Require Different Rest-Intervals Between Sets Than Younger Men During Isokinetic Muscle Contractions?" Medicine & Science in Sports & Exercise 40, Supplement (May 2008): S137. http://dx.doi.org/10.1249/01.mss.0000322587.72249.5b.

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30

Guimarães-Ferreira, Lucas, Michael J. Duncan, Arthur P. Azevedo, Vitor L. Silva, Joctan P. Cordeiro, Mario A. Siqueira-Filho, and Nelo E. Zanchi. "Effects Of Exercise Intensity On Rating Of Perceived Exertion During Multiple Sets To Failure In Bench Press." Medicine & Science in Sports & Exercise 46 (May 2014): 688. http://dx.doi.org/10.1249/01.mss.0000495536.32402.7e.

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31

Salvador, Amadeo F., Sarah K. Skinner, Joseph W. Beals, Justin Parel, Alexander Ulanov, Lucas Li, Scott A. Paluska, Jonathan M. Oliver, and Nicholas A. Burd. "Myofibrillar Protein Synthesis to Traditional and Cluster Sets in Trained Young Men and Women." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 646. http://dx.doi.org/10.1249/01.mss.0000537220.39785.e5.

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32

seo, kyoungho, Jiyeon Kim, Joungbo Ko, Jonathon Martinez, and Sukho Lee. "Comparison of Progressive Single and Multiple Sets of Resistance Training on Muscle Strength and Power." Medicine & Science in Sports & Exercise 51, Supplement (June 2019): 841. http://dx.doi.org/10.1249/01.mss.0000563011.86780.e6.

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33

Bottaro, Martim, Breno Martins, Jonatas Barros, Paulo Gentil, and Dale Wagner. "Effects of Rest Duration Between Sets of Resistance Training on Hormonal Responses in Young Women." Medicine & Science in Sports & Exercise 39, Supplement (May 2007): S294. http://dx.doi.org/10.1249/01.mss.0000274138.28926.ea.

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34

Terra, Bruno, Rafael T. Teixeira, Breno M. Leite, Marta Inez R. Pereira, and Paulo Sergio C. Gomes. "Effect of Whole Body Vibration on Three Maximum Repetition Sets of Push-ups - Pilot Study." Medicine & Science in Sports & Exercise 39, Supplement (May 2007): S297. http://dx.doi.org/10.1249/01.mss.0000274149.89915.bb.

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35

Korak, John A., Max R. Paquette, and John M. Coons. "Peak Muscle Activity across Four Sets to Volitional Fatigue between Rest-Pause and Traditional Bench Press." Medicine & Science in Sports & Exercise 49, no. 5S (May 2017): 464. http://dx.doi.org/10.1249/01.mss.0000518161.69003.dd.

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36

Ryan, Michael J., Matthew Robisnon, Jessica Alsup, Shinichi Asano, and Paul Reneau. "The Effects of Caffeine and Pre workout Supplementation on Exercise Repetition during Sets and Muscular Endurance." Medicine & Science in Sports & Exercise 49, no. 5S (May 2017): 295. http://dx.doi.org/10.1249/01.mss.0000517669.07852.b5.

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37

Theou, Olga, Gareth R. Jones, Lee E. Brown, and Anthony A. Vandervoort. "Effect of Rest-Interval on Decline of Torque Between Sets of a Isokinetic Knee Extensor Exercise in Young and Old Women." Medicine & Science in Sports & Exercise 39, Supplement (May 2007): S299. http://dx.doi.org/10.1249/01.mss.0000274159.99324.7c.

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38

Krzysztofik, Michal, and Michal Wilk. "The Effects of Plyometric Conditioning on Post-Activation Bench Press Performance." Journal of Human Kinetics 74, no. 1 (August 31, 2020): 99–108. http://dx.doi.org/10.2478/hukin-2020-0017.

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Abstract The present study aimed to determine the effects of plyometric push-ups as a conditioning activity (CA) on high-loaded bench press performance. Two groups of resistance-trained males age (24.5 ± 2.6 years, body mass 84.8 ± 8 kg) performed one of two CA protocols: 3 sets of 5 repetitions of plyometric push-ups with a 1 min rest interval between sets (PAPE; n=12) or equal time aerobic warm-up (CONT; n=12). Four minutes after completion of the CA protocols the participants performed 3 sets of 3 repetitions of the bench press exercise at 70%1RM and 4 min rest interval between sets to assess post-activation differences in peak power output (PP), mean power output (MP), peak bar velocity (PV), and mean bar velocity (MV) between conditions. The two-way ANOVA revealed significant condition × set interaction effect for PP (p<0.01), MP (p<0.05), PV (p<0.01), and MV (p=0.02). The post hoc for condition × set interaction showed that PAPE caused a significant decrease in PP and PV for P-Set2 and P-Set3 when compared to baseline (BA). The MP and MV for the PAPE condition decreased significantly during the P-Set3 compared to BA and to P-Set1. The t-test comparisons for delta values showed significant differences between PAPE and CONT in PP for P-Set1 – BA (p<0.01), in MP for P-Set2 – P-Set1 (p<0.03) and for P-Set3 – P-Set1 (p=0.04). Furthermore, there were significant differences in PV for P-Set3 – BA; P-Set2 – P-Set1; P-Set3 – P-Set1 (p<0.01; p<0.01; p<0.02 respectively). Finally, there were significant differences in MV for P-Set1 – BA; P-Set2 – P-Set1 and P-Set3 – P-Set1 (p<0.01; p<0.01; p<0.02 respectively). This study demonstrated that plyometric push-ups lead to performance enhancement of the bench press exercise at 70%1RM. The increases in performance were observed only in the first set following the CA, while a significant decrease of these variables was registered in P-Set2 and P-Set3.

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39

Bartschi, Trezlyn M., David C. Sanders, Tyler M. Farney, Joke Kokkonen, and Arnold G. Nelson. "A Pre-exercise Dose Of Muscle Sentry® Has No Effect On Performing Repeated Leg Press Sets To Failure." Medicine & Science in Sports & Exercise 47 (May 2015): 339. http://dx.doi.org/10.1249/01.mss.0000477341.32019.c2.

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40

Jou, Jerwen. "Task-switching cost and repetition priming: Two overlooked confounds in the fixed-set procedure of the Sternberg paradigm and how they affect memory set-size effects." Quarterly Journal of Experimental Psychology 67, no. 10 (October 2014): 1871–94. http://dx.doi.org/10.1080/17470218.2013.873064.

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Subjects performed Sternberg-type memory recognition tasks (Sternberg paradigm) in four experiments. Category-instance names were used as learning and testing materials. Sternberg's original experiments demonstrated a linear relation between reaction time (RT) and memory-set size (MSS). A few later studies found no relation, and other studies found a nonlinear relation (logarithmic) between the two variables. These deviations were used as evidence undermining Sternberg's serial scan theory. This study identified two confounding variables in the fixed-set procedure of the paradigm (where multiple probes are presented at test for a learned memory set) that could generate a MSS RT function that was either flat or logarithmic rather than linearly increasing. These two confounding variables were task-switching cost and repetition priming. The former factor worked against smaller memory sets and in favour of larger sets whereas the latter factor worked in the opposite way. Results demonstrated that a null or a logarithmic RT-to-MSS relation could be the artefact of the combined effects of these two variables. The Sternberg paradigm has been used widely in memory research, and a thorough understanding of the subtle methodological pitfalls is crucial. It is suggested that a varied-set procedure (where only one probe is presented at test for a learned memory set) is a more contamination-free procedure for measuring the MSS effects, and that if a fixed-set procedure is used, it is worthwhile examining the RT function of the very first trials across the MSSs, which are presumably relatively free of contamination by the subsequent trials.

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41

Bakkiyaraj, M., G. Saikrishnan, and V. Balasubramanian. "Estimating the mechanical properties of friction welded AISI410 MSS joints using empirical relationship." Metallurgical Research & Technology 117, no. 6 (2020): 618. http://dx.doi.org/10.1051/metal/2020075.

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The present study aims to develop the numerical relationship to attain maximum tensile strength of the friction welded AISI410 MSS joints by using response surface methodology. The friction welding (FW) process parameters considered for optimization namely rotational speed, upset pressure, friction time, and upset time. The FW parameters of 31 sets have been used to manufacture the joints to attain the maximum strength. Moreover, the detail relates the optimization procedure with respect to welding conditions on AISI410 MSS joints and its optimized parameters were reported. The optimized results have been correlated with the weld interface of the specimen with reference to tensile properties, macro and microstructure features, hardness, and fracture morphology. Finally, the results concluded that the rotational speed as the most effectual process parameter on the joint properties of the FW AISI410 MSS joint.

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42

Buechler, Steven Allen. "Comparison of immune activity profiles of microsatellite instable colon tumors to microsatellite stable tumors having high ColoType CMS1-score." Journal of Clinical Oncology 39, no. 3_suppl (January 20, 2021): 26. http://dx.doi.org/10.1200/jco.2021.39.3_suppl.26.

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26 Background: Microsatellite instable (MSI) metastatic colorectal cancer (CRC) tumors have been found to be responsive to immune checkpoint inhibitors. Most MSI tumors are in consensus molecular subtype CMS1. Here, in a search for microsatellite stable (MSS) samples that may respond to checkpoint inhibitors, we used the ColoType CMS1-score to identify microsatellite stable (MSS) CRC tumors that have immune activity profiles similar to MSI tumors. Methods: This study was performed in Affymetrix cohort (n = 1,888) of primary CRC tumor samples assayed with hgu133plus2 Affymetrix microarrays. Degrees of infiltration of populations of immune cells were assessed with MCPcounter. Gene Set Enrichment Analysis (GSEA) using the Hallmark gene sets was used to compare biological features between subsets of samples. An 18-gene genomic score (PembroSig) of Ayers, et al, was used to predict response to Pembrolizumab. ColoType 40-gene signature identifies the CMS of a CRC tumor using values of continuous scores, one score for each of CMS1-4. CMS1+ denotes the samples with CMS1-score sufficiently high to predict it is in CMS1. A genomic score was used to predict MSI status for all samples in Affymetrix cohort; MSI predictions using said score had 97% agreement with clinically determined MSI status for 749 Affymetrix cohort samples. Results: The MSI score predicted 240 MSI samples in Affymetrix cohort, and 211 MSS samples in CMS1+ (CMS1+/MSS). The union of MSI and CMS1+/MSS compared to other samples exhibited elevated infiltration of cytotoxic lymphocytes (p < 0.0001) and CD8 T-cells (p < 0.0001), activity of interferon-gamma (p = 0.006) and interferon-alpha signaling (p = 0.012), inflammatory response (p = 0.012), and elevated PembroSig score (p < 0.0001). CMS1+/MSS compared to MSI showed no significance difference in any of these measures. None of the immune regulatory genes PD-1, PD-L1, PD-L2, CTLA4, LAG3, and IDO1 were differentially expressed between CMS1+/MSS and MSI. Of note, the only Hallmark gene sets enriched in MSI compared to CMS1+/MSS were DNA repair (p = 0.002) and MYC targets V1 (p = 0.009) and V2 (p = 0.013). We further analyzed the biological differences between MSI samples in CMS1+ (CMS1+/MSI, n = 190) and MSI samples not in CMS1+ (CMS1-/MSI, n = 50). We found that CMS1+/MSI compared to CMS1-/MSI exhibited elevated cytotoxic lymphocyte infiltration (p < 0.0001), interferon-gamma activity (p = 0.027), and PembroSig score (p < 0.0001), highlighting the relationship between immune activity and CMS1-score. Conclusions: ColoType CMS1-score identified MSS samples in Affymetrix cohort (11%) with similar immune activity as MSI samples (13%) and equivalently high values of a genomic score predictive of Pembrolizumab response.

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43

Fang, Shenying, Yuling Wang, Dawen Sui, Huey Liu, Merrick I. Ross, Jeffrey E. Gershenwald, Janice N. Cormier, et al. "C-Reactive Protein As a Marker of Melanoma Progression." Journal of Clinical Oncology 33, no. 12 (April 20, 2015): 1389–96. http://dx.doi.org/10.1200/jco.2014.58.0209.

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Purpose To investigate the association between blood levels of C-reactive protein (CRP) in patients with melanoma and overall survival (OS), melanoma-specific survival (MSS), and disease-free survival. Patients and Methods Two independent sets of plasma samples from a total of 1,144 patients with melanoma (587 initial and 557 confirmatory) were available for CRP determination. Kaplan-Meier method and Cox regression were used to evaluate the relationship between CRP and clinical outcome. Among 115 patients who underwent sequential blood draws, we evaluated the relationship between change in disease status and change in CRP using nonparametric tests. Results Elevated CRP level was associated with poorer OS and MSS in the initial, confirmatory, and combined data sets (combined data set: OS hazard ratio, 1.44 per unit increase of logarithmic CRP; 95% CI, 1.30 to 1.59; P < .001; MSS hazard ratio, 1.51 per unit increase of logarithmic CRP; 95% CI, 1.36 to 1.68; P < .001). These findings persisted after multivariable adjustment. As compared with CRP < 10 mg/L, CRP ≥ 10 mg/L conferred poorer OS in patients with any-stage, stage I/II, or stage III/IV disease and poorer disease-free survival in those with stage I/II disease. In patients who underwent sequential evaluation of CRP, an association was identified between an increase in CRP and melanoma disease progression. Conclusion CRP is an independent prognostic marker in patients with melanoma. CRP measurement should be considered for incorporation into prospective studies of outcome in patients with melanoma and clinical trials of systemic therapies for those with melanoma.

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44

Řehoř, Jan, Rudolf Brázdil, Miroslav Trnka, Milan Fischer, Jan Balek, Petr Štěpánek, Pavel Zahradníček, Daniela Semerádová, and Monika Bláhová. "Effects of Climatic and Soil Data on Soil Drought Monitoring Based on Different Modelling Schemes." Atmosphere 12, no. 7 (July 15, 2021): 913. http://dx.doi.org/10.3390/atmos12070913.

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Satisfactory requirements for the spatial resolution of climate and the influences of soil data in defining the starting points, endings, and the intensities of droughts have become matters of discussion in recent years. The overall inclusiveness of the modelling tools applied is also frequently discussed. In this light, five model setups (MSs) of the daily SoilClim water balance model were developed and tested for the Czech Republic (CR) in the 1961–2020 period. These included two versions of the SoilClim model, two sets of soil data, and two sets of climatic data at different spatial resolutions. MS1–MS4 were based on local, spatially-interpolated data from meteorological stations (500 × 500 m resolution), while MS5 was developed for global drought monitoring, based on the coarser ERA5-Land reanalysis (0.1° × 0.1°). During the 1961–2020 period, all the MSs indicated strong, statistically significant increases in the occurrence of 10th-percentile soil drought in the April–June season; however, trends remained largely non-significant for the remainder of the year. Variations among MS1–MS4 demonstrate that the range of soil property input data affects results to a lesser extent than different modelling schemes. The major simplification of the model grid in MS5 still led to an acceptable conformity of results, while the non-conformities disclosed may be explained by differences between meteorological inputs. Comparison with the Palmer Drought Severity Index (PDSI) confirmed that the SoilClim model depicts the variability of soil drought occurrence in greater detail, while PDSI tends to highlight the most severe events. The discussion arising out of the study centers around model uncertainties and the expression of soil drought episodes in different MSs.

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Mählitz, Paul Martin, Nathalie Korf, Kristine Sperlich, Olivier Münch, Matthias Rösslein, and Vera Susanne Rotter. "Characterizing the Urban Mine—Simulation-Based Optimization of Sampling Approaches for Built-in Batteries in WEEE." Recycling 5, no. 3 (September 4, 2020): 19. http://dx.doi.org/10.3390/recycling5030019.

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Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither product- nor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality.

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Zhou, Tao, Libin Chen, Jing Guo, Mengmeng Zhang, Huanhuan Liu, Yanrui Zhang, Huina Wang, Shanbo Cao, Haijun Wang, and Feng Lou. "MSIFinder: A python package for detecting MSI status using random forest classifier." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 2601. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.2601.

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2601 Background: Microsatellite instability (MSI) is a common genomic alteration in several tumors, such as colorectal cancer, endometrial carcinoma, and stomach, which is characterized as microsatellite instability-high (MSI-H) and microsatellite stable (MSS) based on a high degree of polymorphism in microsatellite lengths. MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer (CRC) patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; However, they are considerably affected by the sequencing depth and panel size. Methods: We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, RFC model were built by 54 feature markers from the training sets. Second. The software was validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. Results: With this test set, MSIFinder achieved a sensitivity (recall) of 0.997, a specificity of 1, an accuracy of 0.998, a positive predictive value (PPV) of 0.954, an F1 score of 0.977, and an area under curve (AUC) of 0.999. We discovered that MSIFinder is less affected by low sequencing depth and can achieve a concordance of 0.993, while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 m (million base). Conclusions: These results indicated that MSIFinder is a robust MSI classification tool and not affected by the panel size and sequencing depth. Furthermore, MSIFinder can provide reliable MSI detection for scientific and clinical purposes.[Table: see text]

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Qin, Jinlei, and Zheng Li. "Reliability and Sensitivity Analysis Method for a Multistate System with Common Cause Failure." Complexity 2019 (May 28, 2019): 1–8. http://dx.doi.org/10.1155/2019/6535726.

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With the increasing complexity of industrial products and systems, some intermediate states, other than the traditional two states, are often encountered during reliability assessments. A system with more than two states is called a multistate system (MSS) which has already become a general phenomenon in the components and/or systems. Moreover, common cause failure (CCF) often plays a very important role in the assessment of system reliability. A method is proposed to assess the reliability and sensitivity of an MSS with CCF. Some components are not only in a failure state that can cause failure itself, but also in a state that can cause the failure of other components with a certain probability. The components that are affected by one type of CCF make up some sets which can overlap on some components. Using the technology of a universal generating function (UGF), the CCF of a component can be incorporated in the expression of its UGF. Consequently, indices of reliability can be calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers to judge which type of CCF should be eliminated first under various resource limitations. Examples illustrate and validate this method.

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Garbe, Claus, Ulrike Keim, Stefan Suciu, Teresa Amaral, Thomas K. Eigentler, Anja Gesierich, Axel Hauschild, et al. "Prognosis of Patients With Stage III Melanoma According to American Joint Committee on Cancer Version 8: A Reassessment on the Basis of 3 Independent Stage III Melanoma Cohorts." Journal of Clinical Oncology 38, no. 22 (August 1, 2020): 2543–51. http://dx.doi.org/10.1200/jco.19.03034.

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PURPOSE Three new therapies have been approved recently for the adjuvant treatment of stage III melanoma, substantially reducing the risk of tumor recurrences. This study evaluates 3 independent data sets to clarify the survival probabilities of patients with stage III melanoma. PATIENTS AND METHODS The Central Malignant Melanoma Registry (CMMR) evaluated 1,553 patients with a primary diagnosis of stage III melanoma from 2000 to 2012. Studies from the European Organisation for Research and Treatment of Cancer (EORTC), of 573 patients in the observation arm of the 18991 study and 445 patients in the placebo arm of the 18071 study, were evaluated as reference cohorts. The survival outcomes were compared with the published American Joint Committee on Cancer version 8 (AJCCv8) stage III survival data. RESULTS For the CMMR stage III cohort versus the AJCCv8 cohort, the melanoma-specific survival (MSS) rates at 5 years were 67% versus 77%, and at 10 years were 56% versus 69%, respectively. For stage IIIA, the MSS rates at 5 years were 80% versus 93%, and at 10 years were 71% versus 88%; for stage IIIB, the MSS rates at 5 years were 75% versus 83%, and at 10 years were 61% versus 77%. The MSS rates of the EORTC studies either overlapped with or were lower than, the CMMR data. CONCLUSION The MSS rates in the CMMR and EORTC cohorts over the entire stage III are less favorable than those published in AJCCv8. This is particularly true for substages IIIA and IIIB.

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Hanuš, Oto, Miloslav Hronek, Radomír Hyšpler, Tao Yong, Alena Tichá, Petra Fikrová, Kristýna Hanušová, Kamila Sojková, Jaroslav Kopecký, and Radoslava Jedelská. "Relationship between somatic cell count and lactose content in milk of various species of mammals." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 2 (2010): 87–100. http://dx.doi.org/10.11118/actaun201058020087.

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Somatic cell count (SCC) is an indicator of mammary gland health state. Lactose (L) can be reduced with mastitis and SCC increase. Mammary gland health is an essential factor of milk quality. Monitoring of mammary gland health is important for prevention and treatment of milk secretion disorders. The goal of this work was to analyse the relationship between SCC and L in various biological species. 7 sets of individual and bulk milk samples (MSs) were analysed (n = 479, 479, 345, 80, 90 and 102) for SCC and L content. 3 sets were with cow (C) milk and 1 set with goat (G), 1 with sheep (S) and 1 with human (H) MSs. The relations in C milk were used as reference. SCC geometric means were markedly lower in C milk (62, 99 and 81 103 . ml−1) and H milk (103 103 . ml−1) as compared to small ruminants (G 3 509 and S 609 103 . ml−1). The mean L values were lower in small ruminants (G 4.36% and S 4.42%) as compared to C milk (4.95%, 4.97% and in 1st lactation 5.10%) and higher in H milk (5.77%). L contents in Czech Fleckvieh and Holstein correlated negatively to SCC (log SCC)) in all lactations (−0.36 P < 0.001 and −0.33 P < 0.001). L content in 1st lactation correlated with SCC markedly narrower than in cows for all lactations (−0.49 P < 0.001). The SCC×L relationship in G (White short–haired) milk (−0.35 P < 0.01) was in good relation to C milk and in S (Tsigai) milk (−0.51 P < 0.001) was markedly narrower as in C and G milk. Lower mean SCC in H milk as compared to G and S milk and comparable to C milk did not show significant negative relationship to L which was 0.08 (P > 0.05) for original SCC values. Surprisingly there was not found the SCC×L relationship in H milk which could be comparable to other mammal species milk. It could be caused by bacteriologically negative results in MSs with higher SCC (> 300 103 . ml−1). As well as at C milk also at G and S milk and in contrast to H milk it is possible to use the SCC×L relationship for improvement of result interpretation and prevention control in occurrence of milk secretion production disorders in routine monitoring systems such as milk recording with individual MSs and milk quality control with bulk MSs. The quality of decision-making schemes in algorithms for practical monitoring of mammary gland health could be improved.

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Pačínková, Anna, and Vlad Popovici. "Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer." BioMed Research International 2019 (March 17, 2019): 1–9. http://dx.doi.org/10.1155/2019/6763596.

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The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.

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