Recent activity on beam dynamics study during longitudinal bunch compression by using compact beam simulators for heavy ion inertial fusion

In heavy ion inertial fusion scenario, heavy ion beams with extreme high current are most important assignment [1]. Predictions of beam behavior are basic necessity to design the accelerator complex. Especially, a bunch compression manipulation in the final stage of accelerator complex is required to generate the beam with high current and suitable short pulse duration [2]..

Recent activity on beam dynamics study during longitudinal bunch compression by using compact beam simulators for heavy ion inertial fusion

In heavy ion inertial fusion scenario, heavy ion beams with extreme high current are most important assignment [1]. Predictions of beam behavior are basic necessity to design the accelerator complex. Especially, a bunch compression manipulation in the final stage of accelerator complex is required to generate the beam with high current and suitable short pulse duration [2]..

SRSF3, a Splicer of the PKM Gene, Regulates Cell Growth and Maintenance of Cancer-Specific Energy Metabolism in Colon Cancer Cells

Serine and arginine rich splicing factor 3 (SRSF3), an SR-rich family protein, has an oncogenic function in various kinds of cancer. However, the detailed mechanism of the function had not been previously clarified. Here, we showed that the SRSF3 splicer regulated the expression profile of the pyruvate kinase, which is one of the rate-limiting enzymes in glycolysis. Most cancer cells express pyruvate kinase muscle 2 (PKM2) dominantly to maintain a glycolysis-dominant energy metabolism. Overexpression of SRSF3, as well as that of another splicer, polypyrimidine tract binding protein 1 (PTBP1) and heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), in clinical cancer samples supported the notion that these proteins decreased the Pyruvate kinase muscle 1 (PKM1)/PKM2 ratio, which positively contributed to a glycolysis-dominant metabolism. The silencing of SRSF3 in human colon cancer cells induced a marked growth inhibition in both in vitro and in vivo experiments and caused an increase in the PKM1/PKM2 ratio, thus resulting in a metabolic shift from glycolysis to oxidative phosphorylation. At the same time, the silenced cells were induced to undergo autophagy. SRSF3 contributed to PKM mRNA splicing by co-operating with PTBP1 and hnRNPA1, which was validated by the results of RNP immunoprecipitation (RIP) and immunoprecipitation (IP) experiments. These findings altogether indicated that SRSF3 as a PKM splicer played a positive role in cancer-specific energy metabolism

Quantitative Evaluation of Human Cerebellum-Dependent Motor Learning through Prism Adaptation of Hand-Reaching Movement

<div><p>The cerebellum plays important roles in motor coordination and learning. However, motor learning has not been quantitatively evaluated clinically. It thus remains unclear how motor learning is influenced by cerebellar diseases or aging, and is related with incoordination. Here, we present a new application for testing human cerebellum-dependent motor learning using prism adaptation. In our paradigm, the participant wearing prism-equipped goggles touches their index finger to the target presented on a touchscreen in every trial. The whole test consisted of three consecutive sessions: (1) 50 trials with normal vision (BASELINE), (2) 100 trials wearing the prism that shifts the visual field 25° rightward (PRISM), and (3) 50 trials without the prism (REMOVAL). In healthy subjects, the prism-induced finger-touch error, i.e., the distance between touch and target positions, was decreased gradually by motor learning through repetition of trials. We found that such motor learning could be quantified using the “adaptability index (<i>AI</i>)”, which was calculated by multiplying each probability of [acquisition in the last 10 trials of PRISM], [retention in the initial five trials of REMOVAL], and [extinction in the last 10 trials of REMOVAL]. The <i>AI</i> of cerebellar patients less than 70 years old (mean, 0.227; n = 62) was lower than that of age-matched healthy subjects (0.867, n = 21; p < 0.0001). While <i>AI</i> did not correlate with the magnitude of dysmetria in ataxic patients, it declined in parallel with disease progression, suggesting a close correlation between the impaired cerebellar motor leaning and the dysmetria. Furthermore, <i>AI</i> decreased with aging in the healthy subjects over 70 years old compared with that in the healthy subjects less than 70 years old. We suggest that our paradigm of prism adaptation may allow us to quantitatively assess cerebellar motor learning in both normal and diseased conditions.</p></div

Quantitative evaluation of prism adaptation.

<p>(A) An example of adaptation in a healthy subject (HN13) shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119376#pone.0119376.g002" target="_blank">Fig. 2A</a>. The finger-touch error of the last 10 trials of PRISM, and that of the initial five and last 10 trials of REMOVAL are extracted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119376#pone.0119376.g002" target="_blank">Fig. 2A</a>. Acquisition, retention, and extinction of adaptation were estimated from the probability of success (<i>a</i>) in the last 10 trials of PRISM (10/10), the probability of failure (<i>b</i>) in the initial five trials of REMOVAL (5/5), and the probability of success (<i>c</i>) in the last 10 trials of REMOVAL (10/10), respectively. <i>AI</i> was calculated as <i>a</i> × <i>b</i> × <i>c</i> and 1 in this case. (B) Similar analysis in CN4 shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119376#pone.0119376.g002" target="_blank">Fig. 2B</a>. <i>a</i> = 1/10, <i>b</i> = 1/5, <i>c</i> = 6/10. <i>AI</i> = (1/10) × (1/5) × (6/10) = 0.012. Horizontally shaded areas in (A) and (B) represent the zone of “correct” touch (within ± 25mm). (C)–(F) Frequency distributions of <i>a</i> (C), <i>b</i> (D), <i>c</i> (E), and <i>AI</i> (F). Insets represent cumulative frequency curves. <i>F</i>(<i>x</i>) represents normal cumulative distribution function. (G) Frequency distribution of the time constant <i>τ</i> (for details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119376#sec002" target="_blank">Materials and Methods</a>). Insets represent cumulative frequency curves of <i>τ</i>. Red columns and lines in (C)–(G) show data for 21 HN subjects. Blue columns and lines in (C)–(G) show data for 62 CN patients. (H) Receiver operating characteristic (ROC) curve analysis in the HN and CN groups. A purple line shows ROC curve for <i>AI</i>, a red line for the probability of acquisition, a blue line for the probability of retention, a green line for the probability of extinction, and a black line for <i>τ</i>.</p

<i>AI</i> and other clinical indexes in various cerebellar diseases.

<p>(A)–(C) Scatter plots comparing <i>AI</i> with SARA score (A), 9-Hole Peg Test (B), and disease duration (C) in CN and CE patients. Linear regression lines are overlaid. (D) Comparison of <i>AI</i> between the CBL (n = 24) and CBL+ (n = 32) groups. *<i>p</i> < 0.05 by Mann-Whitney U-test. Error bar represents SEM. (E) <i>AI</i> was significantly higher in pure parkinsonian MSA patients than in SCA6, SCA31, CCA, or MSA (MSA-C and MSA-P) patients. *<i>p</i> < 0.05, post hoc Steel-Dwass test.</p