Results 121 to 130 of about 401,655 (316)

An investigation of factors influencing disrupted sleep in university students

فصلنامه دانشگاه علوم پزشکی جهرم, 2013
Introduction: Sleep disruption is one of the psychosocial issues among college students which might adversely affect their lives. This study examines the role of individual and psychosocial factors and intensive scholastic schedule in disrupted sleep ...
Ishagh Rahimian Boogar, Mahdi Ghodrati Mirkouhi   +1 more
doaj  

Machine learning based disruption prediction using long short-term memory in KSTAR

Nuclear Fusion
This study presents a machine learning model for predicting plasma disruptions using the KSTAR database. The model employs a long short-term memory (LSTM) network to capture temporal patterns in zero-dimensional plasma signals.
Jeongwon Lee, Jayhyun Kim, Jinsu Kim, Sang-hee Hahn, Hyunsun Han, Giwook Shin, Yong-Su Na, Yong Un Nam   +7 more
doaj   +1 more source

Disruption Prediction and Analysis Through Multimodal Deep Learning in Kstar

, 2023
Jinsu Kim, Jeong Won Lee, Jaemin Seo, Yong-Su Na   +3 more
openalex   +1 more source

Cytoplasmic p21 promotes stemness of colon cancer cells via activation of the NFκB pathway

Molecular Oncology, EarlyView.
Cytoplasmic p21 promotes colorectal cancer stem cell (CSC) features by destabilizing the NFκB–IκB complex, activating NFκB signaling, and upregulating BCL‐xL and COX2. In contrast to nuclear p21, cytoplasmic p21 enhances spheroid formation and stemness transcription factor CD133.
Arnatchai Maiuthed, Kerstin Huebner, Katharina Erlenbach‐Wuensch, Chuanpit Hampel, Daniela Thalheim, Adriana Vial‐Roehe, Bodee Nutho, Susanne Merkel, Arndt Hartmann, Pithi Chanvorachote, Regine Schneider‐Stock   +10 more
wiley   +1 more source

Adaptive learning for disruption prediction in non-stationary conditions

Nuclear Fusion, 2019
For many years, machine learning tools have proved to be very powerful disruption predictors in tokamaks. On the other hand, the vast majority of the techniques deployed assume that the input data is independent and is sampled from exactly the same probability distribution for the training set, the test set and the final real time deployment.
Murari, A., Lungaroni, M., Gelfusa, M., Peluso, E., Vega, J., Abduallev, S., Abhangi, M., Abreu, P., Afanasev, V, Afzal, M., Aggarwal, K. M., Ahlgren, T., Aho-Mantila, L., Aiba, N., Airila, M., Alarcon, T., Albanese, R., Alegre, D., Aleiferis, S., Alessi, E., Aleynikov, P., Alkseev, A., Allinson, M., Alper, B., Alves, E., Ambrosino, G., Ambrosino, R., Amosov, V, Sunden, E. Andersson, Andrews, R., Angelone, M., Anghel, M., Angioni, C., Appel, L., Appelbee, C., Arena, P., Ariola, M., Arshad, S., Artaud, J., Arter, W., Ash, A., Ashikawa, N., Aslanyan, V, Asunta, O., Asztalos, O., Auriemma, F., Austin, Y., Avotina, L., Axton, M., Ayres, C., Baciero, A., Baiao, D., Balboa, I, Balden, M., Balshaw, N., Bandaru, V. K., Banks, J., Baranov, Y. F., Barcellona, C., Barnard, T., Barnes, M., Barnsley, R., Wiechec, A. Baron, Orte, L. Barrera, Baruzzo, M., Basiuk, V, Bassan, M., Bastow, R., Batista, A., Batistoni, P., Baumane, L., Bauvir, B., Baylor, L., Beaumont, P. S., Beckers, M., Beckett, B., Bekris, N., Beldishevski, M., Bell, K., Belli, F., Belonohy, E., Benayas, J., Bergsaker, H., Bernardo, J., Bernert, M., Berry, M., Bertalot, L., Besiliu, C., Betar, H., Beurskens, M., Bielecki, J., Biewer, T., Bilato, R., Biletskyi, O., Bilkova, P., Binda, F., Birkenmeier, G., Bizarro, J. P. S., Bjorkas, C., Blackburn, J., Blackman, T. R., Blanchard, P., Blatchford, P., Bobkov, V, Boboc, A., Bogar, O., Bohm, P., Bohm, T., Bolshakova, I, Bolzonella, T., Bonanomi, N., Boncagni, L., Bonfiglio, D., Bonnin, X., Boom, J., Borba, D., Borodin, D., Borodkina, I, Boulbe, C., Bourdelle, C., Bowden, M., Bowman, C., Boyce, T., Boyer, H., Bradnam, S. C., Braic, V, Bravanec, R., Breizman, B., Brennan, D., Breton, S., Brett, A., Brezinsek, S., Bright, M., Brix, M., Broeckx, W., Brombin, M., Broslawski, A., Brown, B., Brunetti, D., Bruno, E., Buch, J., Buchanan, J., Buckingham, R., Buckley, M., Bucolo, M., Budny, R., Bufferand, H., Buller, S., Bunting, P., Buratti, P., Burckhart, A., Burroughes, G., Buscarino, A., Busse, A., Butcher, D., Butler, B., Bykov, I, Cahyna, P., Calabro, G., Calacci, L., Callaghan, D., Callaghan, J., Calvo, I, Camenen, Y., Camp, P., Campling, D. C., Cannas, B., Capat, A., Carcangiu, S., Card, P., Cardinali, A., Carman, P., Carnevale, D., Carr, M., Carralero, D., Carraro, L., Carvalho, B. B., Carvalho, I, Carvalho, P., Carvalho, D. D., Casson, F. J., Castaldo, C., Catarino, N., Causa, F., Cavazzana, R., Cave-Ayland, K., Cavedon, M., Cecconello, M., Ceccuzzi, S., Cecil, E., Challis, C. D., Chandra, D., Chang, C. S., Chankin, A., Chapman, I. T., Chapman, B., Chapman, S. C., Chernyshova, M., Chiariello, A., Chitarin, G., Dal Molin, A., Dreval, M., Enachescu, M., Fanni, A., Formisano, A., Fresa, R., Kos, B., Leichtle, D., Loarte, A., Macusova, E., Marchetto, C., Marconato, N., Martone, R., Matejcik, S., Mattei, M., Nordlund, K., Pavlichenko, R., Piron, L., Plyusnin, V, Predebon, I, Ratta, G., Refy, D., Romanelli, F., Safi, E., Sauter, O., Soare, S., Sonato, P., Subba, F., Tokitani, M., Vicente, J., Wischmeier, M.   +230 more
openaire   +8 more sources

Class IIa HDACs forced degradation allows resensitization of oxaliplatin‐resistant FBXW7‐mutated colorectal cancer

Molecular Oncology, EarlyView.
HDAC4 is degraded by the E3 ligase FBXW7. In colorectal cancer, FBXW7 mutations prevent HDAC4 degradation, leading to oxaliplatin resistance. Forced degradation of HDAC4 using a PROTAC compound restores drug sensitivity by resetting the super‐enhancer landscape, reprogramming the epigenetic state of FBXW7‐mutated cells to resemble oxaliplatin ...
Vanessa Tolotto, Nicolò Gualandi, Ylenia Cortolezzis, Raffaella Picco, Monica Colitti, Francesca D'Este, Mariachiara Gani, Wayne W. Hancock, Giovanni Terrosu, Cristina Degrassi, Francesca Agostini, Claudio Brancolini, Luigi E. Xodo, Eros Di Giorgio   +13 more
wiley   +1 more source

Disruption to left inferior frontal cortex modulates semantic prediction effects in reading and subsequent memory: Evidence from simultaneous TMS‐EEG [PDF]

, 2023
Jack Silcox, Brian J. Mickey, Brennan R. Payne   +2 more
openalex   +1 more source

In vitro models of cancer‐associated fibroblast heterogeneity uncover subtype‐specific effects of CRISPR perturbations

Molecular Oncology, EarlyView.
Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra, Shamsudheen Karuthedath Vellarikkal, Lixia Li, Hong Sun, Khoa Nguyen, Amber Montano, Suchitra Natarajan, Federica Piccioni, Alex Michael Tamburino, Xin Yu, Aleksandra Katarzyna Olow   +10 more
wiley   +1 more source

The traveler costs of unplanned transport network disruptions: An activity-based modeling approach [PDF]

In this paper we introduce an activity-based modeling approach for evaluating the traveler costs of transport network disruptions. The model handles several important aspects of such events: increases in travel time may be very long in relation to the ...
David Levinson, Erik Jenelius, Lars-Goran Mattsson   +2 more
core  

Detection of Changes in the Dynamics of Thermonuclear Plasmas to Improve the Prediction of Disruptions [PDF]

, 2022
T. Craciunescu, A. Murari
openalex   +1 more source