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8,440 results on '"An SK"'

1. Highest fusion performance without harmful edge energy bursts in tokamak

Author: Kim, SK, Kim, SK, Shousha, R, Yang, SM, Hu, Q, Hahn, SH, Jalalvand, A, Park, J-K, Logan, NC, Nelson, AO, Na, Y-S, Nazikian, R, Wilcox, R, Hong, R, Rhodes, T, Paz-Soldan, C, Jeon, YM, Kim, MW, Ko, WH, Lee, JH, Battey, A, Yu, G, Bortolon, A, Snipes, J, Kolemen, E, Kim, SK, Kim, SK, Shousha, R, Yang, SM, Hu, Q, Hahn, SH, Jalalvand, A, Park, J-K, Logan, NC, Nelson, AO, Na, Y-S, Nazikian, R, Wilcox, R, Hong, R, Rhodes, T, Paz-Soldan, C, Jeon, YM, Kim, MW, Ko, WH, Lee, JH, Battey, A, Yu, G, Bortolon, A, Snipes, J, and Kolemen, E
Abstract: The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.
Published: 2024

2. Source Free Domain Adaptive Machine Learning and Unlearning

Author: Ahmed, Sk Miraj, K. Roy-Chowdhury, Amit1, Ahmed, Sk Miraj, Ahmed, Sk Miraj, K. Roy-Chowdhury, Amit1, and Ahmed, Sk Miraj
Abstract: Deep neural networks have demonstrated remarkable efficacy across a wide range of tasks, yet they face a significant limitation in their ability to adapt to distributional shifts. In contrast, humans possess inherent adaptability, effortlessly adjusting to changes in data distributions and modifying task strategies to accommodate environmental variations without any external supervision. This adaptability has inspired the field of unsupervised domain adaptation (UDA).Most existing UDA methods rely on access to the source data on which the model was initially trained during the adaptation phase. However, a more practical scenario involves situations where only the trained model is available, rather than the source data. This approach mirrors human learning more closely, as humans do not use previous data directly; instead, their brains are pre-trained on source data and apply this knowledge to new situations. Based on this observation, the field of source-free domain adaptation has emerged. In source-free domain adaptation, only the pre-trained source models and new target data are used during adaptation to a new environment. This dissertation encompasses five significant contributions to this emerging field. First, we explore a scenario where we leverage multiple pretrained source models, each trained on different domains, during the adaptation phase without using source data. We develop an algorithm that effectively combines these models such that the most correlated source model with respect to the target data receives the highest weight, while the least correlated one receives the lowest weight. This approach ensures maximum knowledge transfer from all sources, resulting in final adaptation performance that surpasses any individual source model. This algorithm is designed for a static target distribution, where all target data are available during adaptation and do not change over time. Expanding on this approach, we next consider a scenario where the target data
Published: 2024

3. Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021

Author: Vollset, SE, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbastabar, H, Abd Al Magied, AHA, Abd ElHafeez, S, Abdelkader, A, Abdelmasseh, M, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdullahi, A, Abebe, M, Abiodun, O, Aboagye, RG, Abolhassani, H, Abouzid, M, Aboye, GB, Abreu, LG, Absalan, A, Abualruz, H, Abubakar, B, Abukhadijah, HJJ, Addolorato, G, Adekanmbi, V, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adha, R, Adhikary, RK, Adnani, QES, Adzigbli, LA, Afrashteh, F, Afzal, MS, Afzal, S, Agbozo, F, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahlstrom, AJ, Ahmad, A, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmed, A, Ahmed, H, Ahmed, S, Ahmed, SA, Akinosoglou, K, Akkaif, MA, Akrami, AE, Akter, E, Al Awaidy, S, Al Hasan, SM, Al Mosa, AS, Al Ta'ani, O, Al Zaabi, OAM, Alahdab, F, Alajlani, MM, Al-Ajlouni, Y, Alalalmeh, SO, Al-Aly, Z, Alam, K, Alam, N, Alam, T, Alam, Z, Al-Amer, RM, Alanezi, FM, Alanzi, TM, Albakri, A, Aldhaleei, WA, Aldridge, RW, Alemohammad, SY, Alemu, YM, Al-Gheethi, AAS, Al-Hanawi, MK, Ali, A, Ali, I, Ali, MU, Ali, R, Ali, SSS, Ali, VE, Ali, W, Al-Ibraheem, A, Alicandro, G, Alif, SM, Aljunid, SM, Alla, F, Almazan, JU, Al-Mekhlafi, HM, Alqutaibi, AY, Alrawashdeh, A, Alrousan, SM, Al-Sabah, SK, Alsabri, MA, Altaany, Z, Al-Tammemi, AB, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Al-Wardat, MS, Al-Worafi, YM, Aly, H, Alyahya, MS, Alzoubi, KH, Al-Zyoud, W, Amani, R, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Andrews, EA, Anil, A, Anil, S, Anoushiravani, A, Antony, CM, Antriyandarti, E, Anuoluwa, BS, Anvari, S, Anyasodor, AE, Appiah, F, Aquilano, M, Arab, JP, Arabloo, J, Arafa, EA, Arafat, M, Aravkin, AY, Ardekani, A, Areda, D, Aregawi, BB, Aremu, A, Ariffin, H, Arkew, M, Armani, K, Artamonov, AA, Arumugam, A, Asghari-Jafarabadi, M, Ashbaugh, C, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Aujayeb, A, Ausloos, M, Awad, H, Awotidebe, AW, Ayatollahi, H, Ayuso-Mateos, JL, Azadnajafabad, S, Azeez, FK, Azevedo, RMS, Badar, M, Baghdadi, S, Bagheri, M, Bagheri, N, Bai, R, Baker, JL, Bako, AT, Balakrishnan, S, Balcha, WF, Baltatu, OC, Barchitta, M, Bardideh, E, Barker-Collo, SL, Barnighausen, TW, Barqawi, HJ, Barteit, S, Basiru, A, Basso, JD, Bastan, M-M, Basu, S, Bauckneht, M, Baune, BT, Bayati, M, Bayileyegn, NS, Behnoush, AH, Behzadi, P, Beiranvand, M, Bello, OO, Belo, L, Beloukas, A, Bemanalizadeh, M, Bensenor, IM, Benzian, H, Beran, A, Berezvai, Z, Bernstein, RS, Bettencourt, PJG, Beyene, KA, Beyene, MG, Bhagat, DS, Bhagavathula, AS, Bhala, N, Bhandari, D, Bharadwaj, R, Bhardwaj, N, Bhardwaj, P, Bhargava, A, Bhaskar, S, Bhat, V, Bhattacharjee, NV, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhuiyan, MA, Bisignano, C, Biswas, B, Bjorge, T, Bodolica, V, Bodunrin, AO, Hashemi, MB, Basara, BB, Borhany, H, Bosoka, SA, Carvajal, AB, Bouaoud, S, Boufous, S, Boxe, C, Boyko, EJ, Brady, OJ, Braithwaite, D, Brauer, M, Brazo-Sayavera, J, Brenner, H, Brown, CS, Browne, AJ, Brugha, T, Bryazka, D, Bulamu, NB, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, ZA, dos Santos, FLC, Barsbay, MC, Calina, D, Campos, LA, Cao, S, Capodici, A, Cardenas, R, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, JM, Castelpietra, G, Cattaruzza, MS, Caye, A, Cegolon, L, Cembranel, F, Cenko, E, Cerin, E, Chadban, SJ, Chadwick, J, Chakraborty, C, Chakraborty, S, Chalek, J, Chan, JSK, Chandika, RM, Chandy, S, Charan, J, Chaudhary, AA, Chaurasia, A, Chen, A-T, Chen, H, Chen, MX, Chen, S, Cherbuin, N, Chi, G, Chichagi, F, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Chirinos-Caceres, JL, Chitheer, A, Cho, DY, Cho, WCS, Choi, D-W, Chong, B, Chong, CL, Chopra, H, Chu, D-T, Chung, E, Chutiyami, M, Clayton, JT, Cogen, RM, Cohen, AJ, Columbus, A, Comfort, H, Conde, J, Connolly, JT, Cooper, EEK, Cortese, S, Cruz-Martins, N, da Silva, AG, Dadras, O, Dai, X, Dai, Z, Dalton, BE, Damiani, G, Dandona, L, Dandona, R, Das, JK, Das, S, Dash, NR, Davletov, K, De la Hoz, FP, De Leo, D, Debopadhaya, S, Delgado-Enciso, I, Denova-Gutierrez, E, Dervenis, N, Desai, HD, Devanbu, VGC, Dewan, SMR, Dhama, K, Dhane, AS, Dhingra, S, da Silva, DD, Diaz, D, Diaz, LA, Diaz, MJ, Dima, A, Ding, DD, Do, THP, Do Prado, CB, Dodangeh, M, Doegah, PT, Dohare, S, Dong, W, D'Oria, M, Doshi, R, Dowou, RK, Dsouza, HL, Dsouza, V, Dube, J, Dumith, SC, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dushpanova, A, Dutta, S, Dzianach, PA, Dziedzic, AM, Eboreime, E, Ebrahimi, A, Kalan, ME, Edinur, HA, Efendi, F, Eikemo, TA, Eini, E, Ekundayo, TC, El Arab, RA, El Sayed, I, Elamin, O, Elemam, NM, ElGohary, GMT, Elhadi, M, Elmeligy, OAA, Elmoselhi, AB, Elshaer, M, Elsohaby, I, Eltahir, ME, Emeto, TI, Eshrati, B, Eslami, M, Esmaeili, Z, Fabin, N, Fagbamigbe, AF, Fagbule, OF, Falzone, L, Fareed, M, Farinha, CSES, Faris, MEM, Faro, A, Fasihi, K, Fatehizadeh, A, Fauk, NK, Fazylov, T, Feigin, VL, Fekadu, G, Feng, X, Fereshtehnejad, S-M, Ferrara, P, Ferreira, N, Firew, BS, Fischer, F, Fitriana, I, Flavel, J, Flor, LS, Folayan, MO, Foley, KM, Fonzo, M, Force, LM, Foschi, M, Freitas, A, Fridayani, NKY, Fukutaki, KG, Furtado, JM, Fux, B, Gaal, PA, Gadanya, MA, Gallus, S, Ganesan, B, Ganiyani, MA, Gautam, RK, Gebi, TG, Gebregergis, MW, Gebrehiwot, M, Getacher, L, Getahun, GKA, Gething, PW, Ghadimi, DJ, Ghadirian, F, Ghafarian, S, Ghailan, KY, Ghasemi, M, Dabaghi, GG, Ghazy, RM, Ghoba, S, Gholami, E, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Vajargah, PG, Ghotbi, E, Gil, AU, Gill, TK, Girmay, A, Glasbey, JC, Glushkova, EV, Gnedovskaya, EV, Gobolos, L, Goldust, M, Goleij, P, Golinelli, D, Gopalani, SV, Goulart, AC, Gouravani, M, Goyal, A, Grivna, M, Grosso, G, Guarducci, G, Gubari, MIM, Guicciardi, S, Guimaraes, RA, Gulati, S, Gulisashvili, D, Gunawardane, DA, Guo, C, Gupta, AK, Gupta, R, Gupta, S, Gupta, VK, Haakenstad, A, Hadi, NR, Haep, N, Hafiz, A, Haghmorad, D, Haile, D, Ali, AH, Ali, HA, Haj-Mirzaian, A, Halboub, ES, Haller, S, Halwani, R, Abdullah, KH, Hamdy, NM, Hamoudi, R, Hanifi, N, Hankey, GJ, Haq, ZA, Haque, MR, Harapan, H, Hargono, A, Haro, JM, Hasaballah, AI, Hasan, SMM, Hasanian, M, Hasnain, MS, Hassan, A, Haubold, J, Hay, SI, Hebert, JJ, Hegazi, OE, Heidari, M, Hemmati, M, Henson, CA, Herrera-Serna, BY, Herteliu, C, Heydari, M, Hezam, K, Hidayana, I, Hiraike, Y, Hoan, NQ, Holla, R, Hoogar, P, Horita, N, Hossain, MM, Hosseinzadeh, H, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hu, C, Huang, J, Hultstrom, M, Hundie, TG, Hunt, AJ, Hushmandi, K, Hussain, J, Hussain, MA, Hussein, NR, Huynh, H-H, Hwang, B-F, Ibitoye, SE, Iftikhar, PM, Ikiroma, AI, Ikwegbue, PC, Ilic, IM, Ilic, MD, Immurana, M, Isa, MA, Islam, MR, Islam, SMS, Ismail, F, Ismail, NE, Isola, G, Iwagami, M, Iyamu, IO, Jacob, L, Jacobsen, KH, Jafarinia, M, Jahankhani, K, Jahanmehr, N, Jain, N, Jairoun, AA, Mani, DRJ, Jamil, S, Jamora, RDG, Jatau, AI, Javadov, S, Javaheri, T, Jayaram, S, Jee, SH, Jeganathan, J, Jiang, H, Jokar, M, Jonas, JB, Joseph, N, Joshua, CE, Jurisson, M, Vaishali, K, Kabir, A, Kabir, Z, Kadashetti, V, Kalankesh, LR, Kalra, S, Kamath, A, Kamath, R, Kamireddy, A, Kanaan, M, Kanchan, T, Kanmiki, EW, Kanmodi, KK, Kansal, SK, Karim, A, Karkhah, S, Kashoo, FZ, Kasraei, H, Kassel, MB, Katikireddi, SV, Kauppila, JH, Kaur, H, Kayode, GA, Kazemi, F, Kazemian, S, Kebede, F, Kendal, ES, Kesse-Guyot, E, Khademvatan, S, Khajuria, H, Khalaji, A, Khalid, A, Khalid, N, Khalilian, A, Khamesipour, F, Khan, F, Khan, MJ, Khan, MAB, Khanmohammadi, S, Khatab, K, Khatatbeh, H, Khatatbeh, MM, Khatib, MN, Kashani, HRK, Kheirallah, KA, Khokhar, M, Khormali, M, Khorrami, Z, Khosla, AA, Khosravi, M, Khosrowjerdi, M, Khubchandani, J, Kifle, ZD, Kim, G, Kim, JS, Kim, MS, Kim, YJ, Kimokoti, RW, Kisa, A, Kisa, S, Knibbs, LD, Knudsen, AKS, Kochhar, S, Kolahi, A-A, Kompani, F, Koren, G, Korzh, O, Krishan, K, Krishna, V, Krishnamoorthy, V, Bicer, BK, Kuddus, MA, Kuddus, M, Kuitunen, I, Kujan, O, Kulimbet, M, Kulkarni, V, Kumar, GA, Kumar, H, Kumar, N, Kumar, R, Kumar, V, Kundu, A, Kusuma, D, Kyei-Arthur, F, Kyto, V, Kyu, HH, La Vecchia, C, Lacey, B, Ladan, MA, Laflamme, L, Lahariya, C, Lai, DTC, Lalloo, R, Lallukka, T, Lam, J, Lan, Q, Lan, T, Landires, I, Lanfranchi, F, Langguth, B, Lansingh, VC, Laplante-Levesque, A, Larijani, B, Larsson, AO, Lasrado, S, Lauriola, P, Lawlor, HR, Le, H-H, Le, LKD, Le, NHH, Le, TTT, Le, TDT, Leasher, JL, Lee, DW, Lee, M, Lee, PH, Lee, S-W, Lee, SW, Lee, SWH, Lee, YH, Leigh, J, Leong, E, Li, M-C, Libra, M, Ligade, VS, Lim, L-L, Lim, SS, Limenh, LW, Lindholm, D, Lindstedt, PA, Listl, S, Liu, G, Liu, S, Liu, X, Llanaj, E, Lopez-Bueno, R, Lopez-Gil, JF, Loreche, AM, Lotufo, PA, Lozano, R, Lubinda, J, Lucchetti, G, Luo, L, Lusk, JB, Lv, L, Amin, HIM, Ma, ZF, Maass, KL, Machairas, N, Machoy, M, Madureira-Carvalho, AM, Abd El Razek, HM, Maghazachi, AA, Prasad, DRM, Mahalleh, M, Mahasha, PW, Mahmoud, MA, Mahmoudi, E, Mahmoudvand, G, Makama, M, Rad, EM, Malhotra, K, Malik, AA, Malta, DC, Manla, Y, Mansour, A, Mansouri, MH, Mansouri, P, Mansouri, V, Mansourian, M, Mansournia, MA, Marasini, BP, Marateb, HR, Maravilla, JC, Mardi, P, Marjani, A, Moghadam, HM, Arnedo, CAM, Martinez, G, Martinez-Piedra, R, Martins-Melo, FR, Martorell, M, Marx, W, Marzo, RR, Masoudi, S, Mathangasinghe, Y, Mathioudakis, AG, Mathur, M, Mathur, N, Matozinhos, FP, Mattumpuram, J, Maude, RJ, Maugeri, A, Mayeli, M, Mazidi, M, Mazzotti, A, McGrath, JJ, Mckee, M, McKowen, ALW, McPhail, MA, McPhail, SM, Mehmood, A, Mehrabani-Zeinabad, K, Mehravar, S, Meto, TM, Melese, EB, Mendez-Lopez, MAM, Mendoza, W, Menezes, RG, Mensah, GA, Mensah, LG, Mentis, A-FA, Meo, SA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mestrovic, T, Mettananda, KCD, Mettananda, S, Mhlanga, A, Mhlanga, L, Miazgowski, T, Michalek, IM, de Sa, ACMGN, Miller, TR, Minh, LHN, Mirahmadi, A, Mirijello, A, Mirrakhimov, EM, Mirzaei, R, Mitchell, PB, Mittal, C, Moberg, ME, Fard, AM, Mohajelin, S, Mohamadkhani, A, Mohamed, AI, Mohamed, J, Mohamed, MFH, Mohamed, NS, Mohammad, AM, Mohammadi, S, Mohammed, H, Mohammed, M, Mohammed, S, Mokdad, AH, Molokhia, M, Momani, SM, Momtazmanesh, S, Monasta, L, Mondello, S, Moni, MA, Montazeri, F, Ghalibaf, AM, Moradi, M, Moradi, Y, Moraga, P, Morawska, L, Moreira, RS, Morovatdar, N, Morrison, SD, Mosapour, A, Mosser, JF, Mossialos, E, Motappa, R, Mougin, V, Mousavi, P, Mrejen, M, Mubarik, S, Mueller, UO, Mulita, F, Munjal, K, Murillo-Zamora, E, Musallam, KM, Musina, A-M, Mustafa, G, Myung, W, Nafei, A, Nagarajan, AJ, Naghavi, P, Naik, GR, Naik, G, Nainu, F, Najdaghi, S, Ansari, NN, Nangia, V, Swamy, SN, Nargus, S, Davani, DN, Nascimento, BR, Nascimento, GG, Naser, AY, Nashwan, AJ, Natto, ZS, Nauman, J, Navaratna, SNK, Naveed, M, Nawsherwan, Nayak, BP, Nayak, VC, Negash, H, Negoi, I, Negoi, RI, Nejadghaderi, SA, Nejjari, C, Nematollahi, S, Netsere, HB, Ng, M, Nguefack-Tsague, G, Ngunjiri, JW, Nguyen, AH, Nguyen, DH, Nguyen, HTH, Nguyen, N, Nguyen, NNY, Nguyen, PT, Nguyen, QP, Nguyen, VT, Minh, DNT, Niazi, RK, Nigatu, YT, Niknam, M, Nikoobar, A, Nikpoor, AR, Nikravangolsefid, N, Noman, EA, Nomura, S, Noor, STA, Noroozi, N, Nouri, M, Nozari, M, Nri-Ezedi, CA, Ntaios, G, Nunemo, MH, Nurrika, D, Nutor, JJ, Nzoputam, CI, Nzoputam, OJ, Oancea, B, Obamiro, KO, Odetokun, IA, Oduro, MS, Ogundijo, OA, Ogunfowokan, AA, Ogunkoya, A, Oguntade, AS, Oh, I-H, Ojo-Akosile, TR, Okati-Aliabad, H, Okekunle, AP, Okonji, OC, Olagunju, AT, Olatubi, MI, Oliveira, GMM, Olusanya, BO, Olusanya, JO, Oluwafemi, YD, Omar, HA, Omer, GL, Ong, S, Onie, S, Onwujekwe, OE, Opejin, AO, Ordak, M, Orish, VN, Ortiz, A, Ortiz-Prado, E, Osman, WMS, Ostojic, SM, Ostroff, SM, Osuagwu, UL, Otoiu, A, Otstavnov, SS, Ouyahia, A, Owolabi, MO, Oyeyemi, OT, Ozair, A, Padukudru, MPA, Padron-Monedero, A, Padubidri, JR, Pal, PK, Palicz, T, Pan, F, Pan, H-F, Panda-Jonas, S, Pandey, A, Pando-Robles, V, Pangaribuan, HU, Panos, GD, Panos, LD, Pantazopoulos, I, Stoian, AMP, Parikh, RR, Park, E-K, Park, S, Parsons, N, Parthasarathi, A, Pasovic, M, Passera, R, Patel, J, Pathan, AR, Patil, S, Patoulias, D, Pawar, S, Toroudi, HP, Pease, SA, Peden, AE, Pedersini, P, Pensato, U, Pepito, VCF, Peprah, P, Pereira, M, Pereira, MO, Perianayagam, A, Perico, N, Perna, S, Pesudovs, K, Petermann-Rocha, FE, Pham, HT, Philip, AK, Phillips, MR, Pigeolet, M, Piradov, MA, Pisoni, E, Plotnikov, E, Poddighe, D, Polibin, RV, Poluru, R, Ponkilainen, VT, Popovic, DS, Postma, MJ, Pour-Rashidi, A, Prabhu, D, Prada, SI, Pradhan, J, Pradhan, PMS, Prashant, A, Prates, EJS, Priscilla, T, Purnobasuki, H, Purohit, BM, Puvvula, J, Qasim, NH, Qattea, I, Qazi, AS, Qian, G, Rad, MR, Radhakrishnan, V, Shahraki, HR, Rafferty, Q, Raggi, A, Raggi, C, Raheem, N, Rahim, F, Rahim, MJ, Rahimibarghani, S, Rahman, MMMR, Rahman, M, Rahman, MA, Rahman, T, Rahmani, AM, Rahmanian, M, Rahmanian, N, Rahmati, R, Rahmawaty, S, Raimondo, D, Raja, A, Rajput, P, Ramadan, M, Ramasamy, SK, Ramazanu, S, Ramteke, PW, Rana, K, Rana, RK, Ranabhat, CL, Rane, A, Rao, CR, Rao, M, Rasella, D, Rashedi, V, Rashid, AM, Rasouli-Saravani, A, Rastogi, P, Rasul, A, Rathish, D, Babu, GR, Rauniyar, SK, Ravangard, R, Rawaf, DL, Rawaf, S, Raza, RZ, Redwan, EMM, Reifels, L, Reitsma, MB, Remuzzi, G, Rengasamy, KRR, Reshmi, B, Resnikoff, S, Restaino, S, Reyes, LF, Rezaei, N, Rezaei, ZS, Rezaeian, M, Rhee, TG, Rickard, J, Robalik, T, Robinson-Oden, HE, Rocha, HAL, Rodrigues, M, Rodriguez, JAB, Roever, L, Romadlon, DS, Ronfani, L, Rony, MKK, Roshandel, G, Rotimi, K, Rout, HS, Roy, B, Rubagotti, E, Ruela, GDA, Rumisha, SF, Runghien, T, Russo, M, Saad, AMA, Saber, K, Saber-Ayad, MM, Sabet, CJ, Sabour, S, Sachdev, PS, Saddler, A, Sadee, BA, Sadeghi, M, Saeb, MR, Saeed, U, Safi, SZ, Sagar, R, Saghafi, A, Sagoe, D, Sahebkar, A, Sahoo, PM, Sajid, MR, Salam, N, Salamati, P, Salami, AA, Saleh, MA, Salehi, L, Salem, MR, Salemcity, AJ, Salimi, S, Kafil, HS, Samargandy, S, Samodra, YL, Samy, AM, Sanabria, J, Sanna, F, Santric-Milicevic, MM, Jose, BPS, Saraswathy, SYI, Saravanan, A, Sarmiento-Suarez, R, Sarode, GS, Sarode, SC, Sartorius, B, Satpathy, M, Sayeed, A, Scarmeas, N, Schaarschmidt, BM, Schinckus, C, Schuermans, A, Schumacher, AE, Schutte, AE, Schwebel, DC, Schwendicke, F, Selvaraj, S, Semreen, MH, Senapati, S, Sengupta, P, Senthilkumaran, S, Serban, D, Sethi, Y, Seylani, A, Shafie, M, Shah, PA, Shahbandi, A, Shahid, S, Shahid, W, Shahsavari, HR, Shahwan, MJ, Shaikh, MA, Shalash, AS, Shamekh, A, Shamim, MA, Shanawaz, M, Shankar, A, Shannawaz, M, Sharath, M, Sharfaei, S, Sharifan, A, Sharifi-Rad, J, Sharma, A, Sharma, M, Sharma, S, Sharma, V, Shastry, RP, Shayan, M, Shekhar, S, Shenoy, RR, Shetty, M, Shetty, PH, Shetty, PK, Shi, P, Shiani, A, Shigematsu, M, Shimels, T, Shiri, R, Shittu, A, Shiue, I, Shivakumar, KM, Shool, S, Shorofi, SA, Shrestha, S, Shuval, K, Si, Y, Siddig, EE, Sidhu, JK, Silva, JP, Silva, LMLR, Silva, S, Silva, TPR, Simpson, CR, Simpson, KE, Singh, A, Singh, BB, Singh, 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Abstract: BACKGROUND: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. METHODS: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects m
Published: 2024

4. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Brauer, M, Roth, GA, Aravkin, AY, Zheng, P, Abate, KH, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasi, MA, Abbasian, M, Abbasifard, M, Moradi, Y, Bensenor, IM, Benzian, H, Beran, A, Abouzid, M, Berezvai, Z, Bernabe, E, Bernstein, RS, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Moraga, P, Bhandari, D, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Aboye, GB, Bhat, AN, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Morawska, L, Bhatti, R, Bhuiyan, MA, Bhutta, ZA, Bikbov, B, Bishai, JD, Abreu, LG, Bisignano, C, Biswas, A, Biswas, B, Biswas, RK, Moreira, RS, Bjørge, T, Boachie, MK, Boakye, H, Bockarie, MJ, Bodolica, V, Bodunrin, AO, Abualruz, H, Bogale, EK, Bolla, SR, Boloor, A, Morovatdar, N, Bonakdar Hashemi, M, Boppana, SH, Bora Basara, B, Borhany, H, Botero Carvajal, A, Bouaoud, S, Boufous, S, Abubakar, B, Bourne, R, Boxe, C, Morrison, SD, Braithwaite, D, Brant, LC, Brar, A, Breitborde, NJK, Breitner, S, Brenner, H, Briko, AN, Britton, G, Abu-Gharbieh, E, Brown, CS, Morze, J, Browne, AJ, Brunoni, AR, Bryazka, D, Bulamu, NB, Bulto, LN, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Abukhadijah, HJJ, Mosaddeghi Heris, R, Bustanji, Y, Butt, NS, Butt, ZA, Caetano dos Santos, FL, Cagney, J, Cahuana-Hurtado, L, Calina, D, Cámera, LA, Campos, LA, Campos-Nonato, IR, Mossialos, E, Aburuz, S, Cao, C, Cao, F, Cao, Y, Capodici, A, Cárdenas, R, Carr, S, Carreras, G, Carrero, JJ, Carugno, A, Motappa, R, Carvalho, F, Abu-Zaid, A, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catalá-López, F, Catapano, AL, Cattaruzza, MS, Caye, A, Alemayohu, MA, Ali, BA, Cederroth, CR, Cegolon, L, Adane, MM, Cenderadewi, M, Cercy, KM, Cerin, E, Chadwick, J, Chakraborty, C, Chakraborty, PA, Chakraborty, S, Mougin, V, Chan, JSK, Chan, RNC, Chandan, JS, Addo, IY, Chandika, RM, Chaturvedi, P, Chen, A-T, Chen, CS, Chen, H, Chen, MX, Mousavi, P, Chen, M, Chen, S, Cheng, C-Y, Cheng, ETW, Addolorato, G, Cherbuin, N, Chi, G, Chichagi, F, Chimed-Ochir, O, Chimoriya, 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GS, Azizi, H, Abegaz, TM, Azzam, AY, Backhaus, IL, Mondello, S, Badar, M, Badiye, AD, Bagga, A, Baghdadi, S, Bagheri, N, Bagherieh, S, Bahrami Taghanaki, P, Bai, R, Abeldaño Zuñiga, RA, Baig, AA, Moni, MA, Baker, JL, Bakkannavar, SM, Balasubramanian, M, Baltatu, OC, Bam, K, Bandyopadhyay, S, Banik, B, Banik, PC, Banke-Thomas, A, Abiodun, O, Moodi Ghalibaf, A, Bansal, H, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barone-Adesi, F, Barqawi, HJ, Barrero, LH, Barrow, A, Moore, CE, Abiso, TL, Barteit, S, Basharat, Z, Basiru, A, Basso, JD, Bastan, M-M, Basu, S, Batchu, S, Batra, K, Batra, R, Moradi, M, Baune, BT, Aboagye, RG, Bayati, M, Bayileyegn, NS, Beaney, T, Behnoush, AH, Beiranvand, M, Béjot, Y, Bekele, A, Belgaumi, UI, Bell, AW, Bell, ML, Abolhassani, H, Bello, MB, Bello, OO, Belo, L, Beloukas, A, Bendak, S, Bennett, DA, and Bennitt, FB
Published: 2024

5. Neurodegenerative Disorders: Mechanisms of Degeneration and Therapeutic Approaches with Their Clinical Relevance

Author: Gadhave, DG, Sugandhi, VV, Jha, SK, Nangare, SN, Gupta, G, Singh, SK, Dua, K, Cho, H, Hansbro, PM, Paudel, KR, Gadhave, DG, Sugandhi, VV, Jha, SK, Nangare, SN, Gupta, G, Singh, SK, Dua, K, Cho, H, Hansbro, PM, and Paudel, KR
Published: 2024

6. Cellular Senescence in Lung Cancer: Molecular Mechanisms and Therapeutic Interventions.

Author: Jha, SK, De Rubis, G, Devkota, SR, Zhang, Y, Adhikari, R, Jha, LA, Bhattacharya, K, Mehndiratta, S, Gupta, G, Singh, SK, Panth, N, Dua, K, Hansbro, PM, Paudel, KR, Jha, SK, De Rubis, G, Devkota, SR, Zhang, Y, Adhikari, R, Jha, LA, Bhattacharya, K, Mehndiratta, S, Gupta, G, Singh, SK, Panth, N, Dua, K, Hansbro, PM, and Paudel, KR
Abstract: Lung cancer stands as the primary contributor to cancer-related fatalities worldwide, affecting both genders. Two primary types exist where non-small cell lung cancer (NSCLC), accounts for 80-85% and SCLC accounts for 10-15% of cases. NSCLC subtypes include adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. Smoking, second-hand smoke, radon gas, asbestos, and other pollutants, genetic predisposition, and COPD are lung cancer risk factors. On the other hand, stresses such as DNA damage, telomere shortening, and oncogene activation cause a prolonged cell cycle halt, known as senescence. Despite its initial role as a tumor-suppressing mechanism that slows cell growth, excessive or improper control of this process can cause age-related diseases, including cancer. Cellular senescence has two purposes in lung cancer. Researchers report that senescence slows tumor growth by constraining multiplication of impaired cells. However, senescent cells also demonstrate the pro-inflammatory senescence-associated secretory phenotype (SASP), which is widely reported to promote cancer. This review will look at the role of cellular senescence in lung cancer, describe its diagnostic markers, ask about current treatments to control it, look at case studies and clinical trials that show how senescence-targeting therapies can be used in lung cancer, and talk about problems currently being faced, and possible solutions for the same in the future.
Published: 2024

7. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Naghavi, M, Ong, KL, Aali, A, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abebe, SS, Abedi, A, Abegaz, KH, Abhilash, ES, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abtahi, D, Abu Rumeileh, S, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Accrombessi, MMK, Adal, TG, Adamu, AA, Addo, IY, Addolorato, G, Adebiyi, AO, Adekanmbi, V, Adepoju, AV, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adeyinka, DA, Adeyomoye, OI, Admass, BAA, Adnani, QES, Adra, S, Afolabi, AA, Afzal, MS, Afzal, S, Agampodi, SB, Agasthi, P, Aggarwal, M, Aghamiri, S, Agide, FD, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadzade, AM, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MS, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Akara, EM, Akbarialiabad, H, Akinosoglou, K, Akinyemiju, T, Akkaif, MA, Akyirem, S, Al Hamad, H, Al Hasan, SM, Alahdab, F, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Alam, M, Alam, N, Al-amer, RM, Alanezi, FM, Alanzi, TM, Al-Azzam, S, Albakri, A, Albashtawy, M, AlBataineh, MT, Alcalde-Rabanal, JE, Aldawsari, KA, Aldhaleei, WA, Aldridge, RW, Alema, HB, Alemayohu, MA, Alemi, S, Alemu, YM, Al-Gheethi, AAS, Alhabib, KF, Alhalaiqa, FAN, Al-Hanawi, MK, Ali, A, Ali, L, Ali, MU, Ali, R, Ali, S, Ali, SSS, Alicandro, G, Alif, SM, Alikhani, R, Alimohamadi, Y, Aliyi, AA, Aljasir, MAM, Aljunid, SM, Alla, F, Allebeck, P, Al-Marwani, S, Al-Maweri, SAA, Almazan, JU, Al-Mekhlafi, HM, Almidani, L, Almidani, O, Alomari, MA, Al-Omari, B, Alonso, J, Alqahtani, JS, Alqalyoobi, S, Alqutaibi, AY, Al-Sabah, SK, Altaany, Z, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Alwafi, H, Al-Worafi, 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Zhu, L, Ziafati, M, Zielińska, M, Zitoun, OA, Zoladl, M, Zou, Z, Zuhlke, LJ, Zumla, A, Zweck, E, Zyoud, SH, Wool, EE, and Murray, CJL
Published: 2024

8. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Ferrari, AJ, Santomauro, DF, Aali, A, Abate, YH, Abbafati, C, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdollahi, A, Abdullahi, A, Abegaz, KH, Abeldaño Zuñiga, RA, Aboagye, RG, Abolhassani, H, Abreu, LG, Abualruz, H, Abu-Gharbieh, E, Abu-Rmeileh, NME, Ackerman, IN, Addo, IY, Addolorato, G, Adebiyi, AO, Adepoju, AV, Adewuyi, HO, Afyouni, S, Afzal, S, Agodi, A, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, S, Ahmed, A, Ahmed, LA, Ahmed, MB, Ajami, M, Akinosoglou, K, Akkaif, MA, Al Hasan, SM, Alalalmeh, SO, Al-Aly, Z, Albashtawy, M, Aldridge, RW, Alemu, MD, Alemu, YM, Alene, KA, Al-Gheethi, AAS, Alharrasi, M, Alhassan, RK, Ali, MU, Ali, R, Ali, SSS, Alif, SM, Aljunid, SM, Al-Marwani, S, Almazan, JU, Alomari, MA, Al-Omari, B, Altaany, Z, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Alwafi, H, Al-Wardat, MS, Al-Worafi, YM, Aly, S, Alzoubi, KH, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Ansari, H, Antony, CM, Anwar, S, Anwar, SL, Anwer, R, Anyanwu, PE, Arab, JP, Arabloo, J, Arafat, M, Araki, DT, Aravkin, AY, Arkew, M, Armocida, B, Arndt, MB, Arooj, M, Artamonov, AA, Aruleba, RT, Arumugam, A, Ashbaugh, C, Ashemo, MY, Ashraf, M, Asika, MO, Askari, E, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awotidebe, AW, Awuviry-Newton, K, Ayala Quintanilla, BP, Ayuso-Mateos, JL, Azadnajafabad, S, Azevedo, RMS, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bah, S, Bai, R, Baker, JL, Bakkannavar, SM, Bako, AT, Balakrishnan, S, Bam, K, Banik, PC, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Barqawi, HJ, Barrow, A, Barteit, S, Barua, L, Bashiri Aliabadi, S, Basiru, A, Basu, S, Bathini, PP, Batra, K, Baune, BT, Bayileyegn, NS, Behnam, B, Behnoush, AH, Beiranvand, M, Bejarano Ramirez, DF, Bell, ML, Bello, OO, Beloukas, A, Bensenor, IM, Berezvai, Z, Bernabe, E, Bernstein, RS, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhandari, D, Bhargava, A, Bhaskar, S, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Bitra, VR, Bjørge, T, Bodolica, V, Bodunrin, AO, Bogale, EK, Bonakdar Hashemi, M, Bonny, A, Bora Basara, B, Borhany, H, Boxe, C, Brady, OJ, Bragazzi, NL, Braithwaite, D, Brant, LC, Brauer, M, Breitner, S, Brenner, H, Brown, J, Brugha, T, Bulamu, NB, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, ZA, Byun, J, Caetano dos Santos, FL, Calina, D, Cámera, LA, Campos-Nonato, IR, Cao, C, Capodici, A, Carr, S, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catapano, AL, Cattaruzza, MS, Caye, A, Cegolon, L, Cembranel, F, Cenderadewi, M, Cerin, E, Chakraborty, PA, Chan, JSK, Chan, RNC, Chandika, RM, Chandrasekar, EK, Charalampous, P, Chattu, VK, Chatzimavridou-Grigoriadou, V, Chen, AW, Chen, A-T, Chen, CS, Chen, H, Chen, NM, Cheng, ETW, Chimed-Ochir, 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M, Shah, H, Shah, NS, Shah, PA, Shahbandi, A, Shahid, S, Shahid, W, Shahwan, MJ, Shaikh, MA, Shakeri, A, Shalash, AS, Sham, S, Shamim, MA, Shamshirgaran, MA, Shamsi, MA, Shanawaz, M, Shankar, A, Shannawaz, M, Sharath, M, Sharifan, A, Sharifi-Rad, J, Sharma, M, Sharma, R, Sharma, S, Sharma, U, Sharma, V, Shastry, RP, Shavandi, A, Shayan, AM, Shayan, M, Shehabeldine, AME, Shetty, PH, Shibuya, K, Shifa, JE, Shiferaw, D, Shiferaw, WS, Shigematsu, M, Shiri, R, Shitaye, NA, Shittu, A, Shivakumar, KM, Shivarov, V, Shokati Eshkiki, Z, Shool, S, Shrestha, S, Shuval, K, Sibhat, MM, Siddig, EE, Sigfusdottir, ID, Silva, DAS, Silva, JP, Silva, LMLR, Silva, S, Simpson, CR, Singal, A, Singh, A, Singh, BB, Singh, H, Singh, JA, Singh, M, Singh, P, Skou, ST, Sleet, DA, Slepak, ELN, Solanki, R, Soliman, SSM, Song, S, Song, Y, Sorensen, RJD, Soriano, JB, Soyiri, IN, Spartalis, M, Sreeramareddy, CT, Stark, BA, Starodubova, AV, Stein, C, Stein, DJ, Steiner, C, Steiner, TJ, Steinmetz, JD, Steiropoulos, P, Stockfelt, L, Stokes, MA, Subedi, NS, Subramaniyan, V, Suemoto, CK, Suleman, M, Suliankatchi Abdulkader, R, Sultana, A, Sundström, J, Swain, CK, Szarpak, L, Tabaee Damavandi, P, Tabarés-Seisdedos, R, Tabatabaei Malazy, O, Tabatabaeizadeh, S-A, Tabatabai, S, Tabche, C, Tabish, M, Tadakamadla, SK, Taheri Abkenar, Y, Taheri Soodejani, M, Taherkhani, A, Taiba, J, Talaat, IM, Talukder, A, Tampa, M, Tamuzi, JL, Tan, K-K, Tandukar, S, Tang, H, Tavakoli Oliaee, R, Tavangar, SM, Teimoori, M, Temsah, M-H, Teramoto, M, Thangaraju, P, Thankappan, KR, Thapar, R, Thayakaran, R, Thirunavukkarasu, S, Thomas, N, Thomas, NK, Thum, CCC, Tichopad, A, Ticoalu, JHV, Tillawi, T, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Torre, AE, Touvier, M, Tovani-Palone, MR, Tran, JT, Tran, MTN, Tran, NM, Tran, N-H, Trico, D, Tromans, SJ, Truyen, TTTT, Tsatsakis, A, Tsegay, GM, Tsermpini, EE, Tumurkhuu, M, Tyrovolas, S, Udoh, A, Umair, M, Umakanthan, S, Umar, TP, Undurraga, EA, Unim, B, Unnikrishnan, B, Unsworth, CA, Upadhyay, E, Urso, D, Usman, JS, Vahabi, SM, Vaithinathan, AG, Van den Eynde, J, Varga, O, Varma, RP, Vart, P, Vasankari, TJ, Vasic, M, Vaziri, S, Vellingiri, B, Venketasubramanian, N, Veroux, M, Verras, G-I, Vervoort, D, Villafañe, JH, Violante, FS, Vlassov, V, Vollset, SE, Volovat, SR, Vongpradith, A, Waheed, Y, Wang, C, Wang, F, Wang, N, Wang, S, Wang, Y, Wang, Y-P, Ward, P, Wassie, EG, Weaver, MR, Weerakoon, KG, Weintraub, RG, Weiss, DJ, Weldemariam, AH, Wells, KM, Wen, YF, Whisnant, JL, Whiteford, HA, Wiangkham, T, Wickramasinghe, DP, Wickramasinghe, ND, Wilandika, A, Wilkerson, C, Willeit, P, Wimo, A, Woldegebreal, DH, Wolf, AW, Wong, YJ, Woolf, AD, Wu, C, Wu, F, Wu, X, Wu, Z, Wulf Hanson, S, Xia, Y, Xiao, H, Xu, X, Xu, YY, Yadav, L, Yadollahpour, A, Yaghoubi, S, Yamagishi, K, Yang, L, Yano, Y, Yao, Y, Yaribeygi, H, Yazdanpanah, MH, Ye, P, Yehualashet, SS, Yesuf, SA, Yezli, S, Yiğit, A, Yiğit, V, Yigzaw, ZA, Yismaw, Y, Yon, DK, Yonemoto, N, Younis, MZ, Yu, C, Yu, Y, Yusuf, H, Zahid, MH, Zakham, F, Zaki, L, Zaki, N, Zaman, BA, Zamora, N, Zand, R, Zandieh, GGZ, Zar, HJ, Zarrintan, A, Zastrozhin, MS, Zhang, H, Zhang, N, Zhang, Y, Zhao, H, Zhong, C, Zhong, P, Zhou, J, Zhu, Z, Ziafati, M, Zielińska, M, Zimsen, SRM, Zoladl, M, Zumla, A, Zyoud, SH, Vos, T, and Murray, CJL
Published: 2024

9. Einflüsse kognitiver und perzeptueller Defizite auf die komplexe Sprachverarbeitung: Eine Elektroenzephalographie-Studie mit Cochleaimplantat-Tragenden und Normalhörenden

Author: Pohle, U, Werle, AL, Plontke, SK, Wagner, L, Pohle, U, Werle, AL, Plontke, SK, and Wagner, L
Published: 2024

10. Klangqualität von Simulationen des Klangeindrucks von Cochlea-Implantat-Tragenden in Abhängigkeit von der Trage-Erfahrung

Author: Kopsch, AC, Plontke, SK, Rahne, T, Kopsch, AC, Plontke, SK, and Rahne, T
Published: 2024

11. Elektrisch vestibuläre Kostimulation durch Cochleaimplantate - Einfluss der Stimulationsparameter auf vestibulär evozierte myogene Potentiale

Author: Fröhlich, L, Plontke, SK, Löffler, L, Manthey, A, Rahne, T, Fröhlich, L, Plontke, SK, Löffler, L, Manthey, A, and Rahne, T
Published: 2024

12. The use of audiological measurements to evaluate efficacy of a novel drug for the treatment of Sudden Sensorineural Hearing Loss (SSNHL): Phase 2 Clinical Trial AC102-201

Author: Rahne, T, Lanting, C, Pennings, R, Arnoldner, C, Galetzka, C, Meis, A, Ward, R, Plontke, SK, Schlingensiepen, R, Rahne, T, Lanting, C, Pennings, R, Arnoldner, C, Galetzka, C, Meis, A, Ward, R, Plontke, SK, and Schlingensiepen, R
Published: 2024

13. Elektrisch evozierte Mismatch Negativity für Lautheits- und Tonhöhenabweichungen bei CI-Tragenden

Author: Rahne, T, Ladek, AS, Plontke, SK, Wagner, L, Rahne, T, Ladek, AS, Plontke, SK, and Wagner, L
Published: 2024

14. Precipitation change affects forest soil carbon inputs and pools: A global meta-analysis

Author: Xu, S, Wang, J, Sayer, EJ, Lam, SK, Lai, DYF, Xu, S, Wang, J, Sayer, EJ, Lam, SK, and Lai, DYF
Abstract: The impacts of precipitation change on forest carbon (C) storage will have global consequences, as forests play a major role in sequestering anthropogenic CO2. Although forest soils are one of the largest terrestrial C pools, there is great uncertainty around the response of forest soil organic carbon (SOC) to precipitation change, which limits our ability to predict future forest C storage. To address this, we conducted a meta-analysis to determine the effect of drought and irrigation experiments on SOC pools, plant C inputs and the soil environment based on 161 studies across 139 forest sites worldwide. Overall, forest SOC content was not affected by precipitation change, but both drought and irrigation altered plant C inputs and soil properties associated with SOC formation and storage. Drought may enhance SOC stability by altering soil aggregate fractions, but the effect of irrigation on SOC fractions remains unexplored. The apparent insensitivity of SOC to precipitation change can be explained by the short duration of most experiments and by biome-specific responses of C inputs and pools to drought or irrigation. Importantly, we demonstrate that SOC content is more likely to decline under irrigation at drier temperate sites, but that dry forests are currently underrepresented across experimental studies. Thus, our meta-analysis advances research into the impacts of precipitation change in forests by revealing important differences among forest biomes, which are likely linked to plant adaptation to extant conditions. We further demonstrate important knowledge gaps around how precipitation change will affect SOC stability, as too few studies currently consider distinct soil C pools. To accurately predict future SOC storage in forests, there is an urgent need for coordinated studies of different soil C pools and fractions across existing sites, as well as new experiments in underrepresented forest types.
Published: 2024

15. Is conservative opposition to climate change threat-based? Articulating an integrated threat model of climate change attitudes

Author: Stanley, SK, Jylha, KM, Leviston, Z, Walker, I, Stanley, SK, Jylha, KM, Leviston, Z, and Walker, I
Abstract: Throughout the literature, there are assertions that those endorsing conservative ideologies reject the science and solutions of climate change due to perceived threat. That is, they fear that accepting climate change means accepting problems with a favoured socioeconomic system and supporting action on climate change threatens to disrupt these systems. We draw together lines of research and reasoning on this topic to outline three key predictions this perspective makes about the drivers of conservative denial of climate change and opposition to climate policy. The first is that an asymmetry exists in climate-related threat perceptions, whereby greater endorsement of conservative ideology predicts lower perceived threat from climate change and greater perceived threat from climate reform. Second, climate-related threat perceptions are multifaceted, such that threats to economic and cultural well-being can be experienced, at personal or collective levels. Third, the asymmetry in threat perceptions explains conservatives' lower support for pro-climate reforms. We then specify a new integrated threat model of climate change attitudes, review the current evidence for and against each prediction in this model and outline ways to interrogate these theoretical predictions with empirical research. Doing so will advance understanding of the underpinnings of ideological disagreement on climate change.
Published: 2024

16. Longitudinal Fluctuations in Protein Concentrations and Higher-Order Structures in the Plasma Proteome of Kidney Failure Patients Subjected to a Kidney Transplant

Author: Nteak, SK, Vollmy, F, Lukassen, MV, van den Toorn, H, den Boer, MA, Bondt, A, van der Lans, SPA, Haas, PJ, van Zuilen, AD, Rooijakkers, SHM, Heck, AJR, Nteak, SK, Vollmy, F, Lukassen, MV, van den Toorn, H, den Boer, MA, Bondt, A, van der Lans, SPA, Haas, PJ, van Zuilen, AD, Rooijakkers, SHM, and Heck, AJR
Abstract: Using proteomics and complexome profiling, we evaluated in a year-long study longitudinal variations in the plasma proteome of kidney failure patients, prior to and after a kidney transplantation. The post-transplant period was complicated by bacterial infections, resulting in dramatic changes in the proteome, attributed to an acute phase response (APR). As positive acute phase proteins (APPs), being elevated upon inflammation, we observed the well-described C-reactive protein and Serum Amyloid A (SAA), but also Fibrinogen, Haptoglobin, Leucine-rich alpha-2-glycoprotein, Lipopolysaccharide-binding protein, Alpha-1-antitrypsin, Alpha-1-antichymotrypsin, S100, and CD14. As negative APPs, being downregulated upon inflammation, we identified the well-documented Serotransferrin and Transthyretin, but added Kallistatin, Heparin cofactor 2, and interalpha-trypsin inhibitor heavy chain H1 and H2 (ITIH1, ITIH2). For the patient with the most severe APR, we performed plasma complexome profiling by SEC-LC-MS on all longitudinal samples. We observed that several plasma proteins displaying alike concentration patterns coelute and form macromolecular complexes. By complexome profiling, we expose how SAA1 and SAA2 become incorporated into high-density lipid particles, replacing largely Apolipoprotein (APO)A1 and APOA4. Overall, our data highlight that the combination of in-depth longitudinal plasma proteome and complexome profiling can shed further light on correlated variations in the abundance of several plasma proteins upon inflammatory events.
Published: 2024

17. Evidence of thermophilization in Afromontane forests

Author: Cuni-Sanchez, Aida, Martin, Emanuel H, Uzabaho, Eustrate, Ngute, Alain SK, Bitariho, Robert, Kayijamahe, Charles, Marshall, Andrew R, Mohamed, Nassoro A, Mseja, Gideon A, Nkwasibwe, Aventino, Rovero, Francesco, Sheil, Douglas, Tinkasimire, Rogers, Tumugabirwe, Lawrence, Feeley, Kenneth J, Sullivan, Martin JP, Cuni-Sanchez, Aida, Martin, Emanuel H, Uzabaho, Eustrate, Ngute, Alain SK, Bitariho, Robert, Kayijamahe, Charles, Marshall, Andrew R, Mohamed, Nassoro A, Mseja, Gideon A, Nkwasibwe, Aventino, Rovero, Francesco, Sheil, Douglas, Tinkasimire, Rogers, Tumugabirwe, Lawrence, Feeley, Kenneth J, and Sullivan, Martin JP
Abstract: Thermophilization is the directional change in species community composition towards greater relative abundances of species associated with warmer environments. This process is well-documented in temperate and Neotropical plant communities, but it is uncertain whether this phenomenon occurs elsewhere in the tropics. Here we extend the search for thermophilization to equatorial Africa, where lower tree diversity compared to other tropical forest regions and different biogeographic history could affect community responses to climate change. Using re-census data from 17 forest plots in three mountain regions of Africa, we find a consistent pattern of thermophilization in tree communities. Mean rates of thermophilization were +0.0086 °C·y−1 in the Kigezi Highlands (Uganda), +0.0032 °C·y−1 in the Virunga Mountains (Rwanda-Uganda-Democratic Republic of the Congo) and +0.0023 °C·y−1 in the Udzungwa Mountains (Tanzania). Distinct from other forests, both recruitment and mortality were important drivers of thermophilzation in the African plots. The forests studied currently act as a carbon sink, but the consequences of further thermophilization are unclear.
Published: 2024

18. Search for 10–1000 GeV Neutrinos from Gamma-Ray Bursts with IceCube

Author: Abbasi, R, Abbasi, R, Ackermann, M, Adams, J, Agarwalla, SK, Aguilar, JA, Ahlers, M, Alameddine, JM, Amin, NM, Andeen, K, Anton, G, Argüelles, C, Ashida, Y, Athanasiadou, S, Ausborm, L, Axani, SN, Bai, X, V., A Balagopal, Baricevic, M, Barwick, SW, Basu, V, Bay, R, Beatty, JJ, Tjus, J Becker, Beise, J, Bellenghi, C, Benning, C, BenZvi, S, Berley, D, Bernardini, E, Besson, DZ, Blaufuss, E, Blot, S, Bontempo, F, Book, JY, Meneguolo, C Boscolo, Böser, S, Botner, O, Böttcher, J, Braun, J, Brinson, B, Brostean-Kaiser, J, Brusa, L, Burley, RT, Busse, RS, Butterfield, D, Campana, MA, Carloni, K, Carnie-Bronca, EG, Chattopadhyay, S, Chau, N, Chen, C, Chen, Z, Chirkin, D, Choi, S, Clark, BA, Coleman, A, Collin, GH, Connolly, A, Conrad, JM, Coppin, P, Correa, P, Cowen, DF, Dave, P, De Clercq, C, DeLaunay, JJ, Delgado, D, Deng, S, Deoskar, K, Desai, A, Desiati, P, de Vries, KD, de Wasseige, G, DeYoung, T, Diaz, A, Díaz-Vélez, JC, Dittmer, M, Domi, A, Dujmovic, H, DuVernois, MA, Ehrhardt, T, Eimer, A, Eller, P, Ellinger, E, Mentawi, S El, Elsässer, D, Engel, R, Erpenbeck, H, Evans, J, Evenson, PA, Fan, KL, Fang, K, Farrag, K, Fazely, AR, Fedynitch, A, Feigl, N, Fiedlschuster, S, Finley, C, Fischer, L, Fox, D, Franckowiak, A, Abbasi, R, Abbasi, R, Ackermann, M, Adams, J, Agarwalla, SK, Aguilar, JA, Ahlers, M, Alameddine, JM, Amin, NM, Andeen, K, Anton, G, Argüelles, C, Ashida, Y, Athanasiadou, S, Ausborm, L, Axani, SN, Bai, X, V., A Balagopal, Baricevic, M, Barwick, SW, Basu, V, Bay, R, Beatty, JJ, Tjus, J Becker, Beise, J, Bellenghi, C, Benning, C, BenZvi, S, Berley, D, Bernardini, E, Besson, DZ, Blaufuss, E, Blot, S, Bontempo, F, Book, JY, Meneguolo, C Boscolo, Böser, S, Botner, O, Böttcher, J, Braun, J, Brinson, B, Brostean-Kaiser, J, Brusa, L, Burley, RT, Busse, RS, Butterfield, D, Campana, MA, Carloni, K, Carnie-Bronca, EG, Chattopadhyay, S, Chau, N, Chen, C, Chen, Z, Chirkin, D, Choi, S, Clark, BA, Coleman, A, Collin, GH, Connolly, A, Conrad, JM, Coppin, P, Correa, P, Cowen, DF, Dave, P, De Clercq, C, DeLaunay, JJ, Delgado, D, Deng, S, Deoskar, K, Desai, A, Desiati, P, de Vries, KD, de Wasseige, G, DeYoung, T, Diaz, A, Díaz-Vélez, JC, Dittmer, M, Domi, A, Dujmovic, H, DuVernois, MA, Ehrhardt, T, Eimer, A, Eller, P, Ellinger, E, Mentawi, S El, Elsässer, D, Engel, R, Erpenbeck, H, Evans, J, Evenson, PA, Fan, KL, Fang, K, Farrag, K, Fazely, AR, Fedynitch, A, Feigl, N, Fiedlschuster, S, Finley, C, Fischer, L, Fox, D, and Franckowiak, A
Abstract: We present the results of a search for 10-1000 GeV neutrinos from 2268 gamma-ray bursts (GRBs) over 8 yr of IceCube-DeepCore data. This work probes burst physics below the photosphere where electromagnetic radiation cannot escape. Neutrinos of tens of giga electronvolts are predicted in sub-photospheric collision of free-streaming neutrons with bulk-jet protons. In a first analysis, we searched for the most significant neutrino-GRB coincidence using six overlapping time windows centered on the prompt phase of each GRB. In a second analysis, we conducted a search for a group of GRBs, each individually too weak to be detectable, but potentially significant when combined. No evidence of neutrino emission is found for either analysis. The most significant neutrino coincidence is for Fermi-GBM GRB bn 140807500, with a p-value of 0.097 corrected for all trials. The binomial test used to search for a group of GRBs had a p-value of 0.65 after all trial corrections. The binomial test found a group consisting only of GRB bn 140807500 and no additional GRBs. The neutrino limits of this work complement those obtained by IceCube at tera electronvolt to peta electronvolt energies. We compare our findings for the large set of GRBs as well as GRB 221009A to the sub-photospheric neutron-proton collision model and find that GRB 221009A provides the most constraining limit on baryon loading. For a jet Lorentz factor of 300 (800), the baryon loading on GRB 221009A is lower than 3.85 (2.13) at a 90% confidence level.
Published: 2024

19. Programmierung und elektrophysiologische Messungen mit Cochlea-Implantaten bei Patienten nach Entfernung intracochleärer Schwannome

Author: Wagner, L, Kopsch, AC, Rahne, T, Plontke, SK, Wagner, L, Kopsch, AC, Rahne, T, and Plontke, SK
Published: 2024

20. Fibonacci-Inspired Spiral Quilts

Author: Offenholley, Kathleen, Collins, SK, Radcliffe, David, Offenholley, Kathleen, Collins, SK, and Radcliffe, David
Abstract: This article provides insight into the mathematics and designs of quilts inspired by Fibonacci and logarithmic spirals. We introduce the history and development of the Fibonacci number sequence and how to hand-draw a Fibonacci spiral. Further, we explain the relationship between the Fibonacci spiral and logarithmic spirals, the advantages of using logarithmic spirals to create designs, and how to produce digital spiral designs using Desmos, a free web-based graphing calculator. Finally, we discuss methods for designing spiral quilts or other triangle and spiral designs (such as collage or other media) and derive a formula for calculating the apex angles of the triangles. We hope to inspire the reader’s imagination to spiral out of bounds to explore, create, and discover the beauty of mathematics in what you will create.
Published: 2024

21. Energy Gap of the Even-Denominator Fractional Quantum Hall State in Bilayer Graphene

Author: Assouline, Alexandre, Assouline, Alexandre, Wang, Taige, Zhou, Haoxin, Cohen, Liam A, Yang, Fangyuan, Zhang, Ruining, Taniguchi, Takashi, Watanabe, Kenji, Mong, Roger SK, Zaletel, Michael P, Young, Andrea F, Assouline, Alexandre, Assouline, Alexandre, Wang, Taige, Zhou, Haoxin, Cohen, Liam A, Yang, Fangyuan, Zhang, Ruining, Taniguchi, Takashi, Watanabe, Kenji, Mong, Roger SK, Zaletel, Michael P, and Young, Andrea F
Abstract: Bernal bilayer graphene hosts even-denominator fractional quantum Hall states thought to be described by a Pfaffian wave function with non-Abelian quasiparticle excitations. Here, we report the quantitative determination of fractional quantum Hall energy gaps in bilayer graphene using both thermally activated transport and by direct measurement of the chemical potential. We find a transport activation gap of 5.1 K at B=12 T for a half filled N=1 Landau level, consistent with density matrix renormalization group calculations for the Pfaffian state. However, the measured thermodynamic gap of 11.6 K is smaller than theoretical expectations for the clean limit by approximately a factor of 2. We analyze the chemical potential data near fractional filling within a simplified model of a Wigner crystal of fractional quasiparticles with long-wavelength disorder, explaining this discrepancy. Our results quantitatively establish bilayer graphene as a robust platform for probing the non-Abelian anyons expected to arise as the elementary excitations of the even-denominator state.
Published: 2024

22. Communication skills of medical students: Evaluation of a new communication curriculum at the University of Augsburg

Author: Zerbini, G, Reicherts, P, Reicherts, M, Roob, N, Schneider, P, Dankert, A, Greiner, SK, Kadmon, M, Lechner, V, Roos, M, Schimmel, M, Strube, W, Temizel, S, Uhrmacher, L, Kunz, M, Zerbini, G, Reicherts, P, Reicherts, M, Roob, N, Schneider, P, Dankert, A, Greiner, SK, Kadmon, M, Lechner, V, Roos, M, Schimmel, M, Strube, W, Temizel, S, Uhrmacher, L, and Kunz, M
Abstract: Objectives: Teaching communication skills plays a pivotal role in medical curricula. The aim of this article is to describe and evaluate a new communication curriculum developed at the Faculty of Medicine, University of Augsburg (KomCuA), which was conceptualized by an interdisciplinary team based on recommended quality standards (i.e., helical, integrated, longitudinal).Methods: A total of 150 medical students enrolled in the 1st, 3rd, and >=5th semester participated in the study. They completed an online survey (numerical rating scales and validated questionnaires) evaluating their current communication skills, how these developed across the curriculum in terms of quality and self-confidence, and how helpful they considered practicing in small group tutorials with simulated patients. The students' attitudes towards communication and empathy in the context of medical care were additionally assessed. The students' responses were compared across semesters using one-way univariate analysis of variance (ANOVA).Results: Overall, students reported improved communications skills due to attending the KomCuA and further considered practicing with simulated patients as being very helpful (large effect sizes). Compared to 1st semester students, 3rd and >=5th semester students reported better communication skills (medium to large effect sizes). Additionally, >=5th semester students showed stronger agreement towards the relevance of empathy in the context of medical care (medium effect size) compared to both 1st and 3rd semester students. Conclusion: The KomCuA has shown to be an effective communication curriculum to support medical students in the development of their communication skills and positive attitudes towards empathy. Additional studies assessing students' communication skills and empathic attitudes longitudinally are warranted to confirm the present results and to gain further knowledge on how these essential skills and attitudes develop across medical curricula., Zielsetzungen: Die Vermittlung kommunikativer Kompetenzen spielt in der medizinischen Ausbildung eine zentrale Rolle. Ziel dieses Artikels ist es, ein neues Kommunikationscurriculum an der Medizinischen Fakultät der Universität Augsburg (KomCuA) zu beschreiben und zu evaluieren, welches von einem interdisziplinären Team basierend auf empfohlenen Qualitätsstandards (d.h. helikal, integriert, longitudinal) konzipiert wurde.Methoden: An der Online-Studie nahmen insgesamt 150 Medizinstudierende aus den Semestern 1., 3. und >=5. teil. Mithilfe numerischer Bewertungsskalen und validierter Fragebögen wurden Aspekte wie Kommunikationskompetenz, Veränderungen in Qualität und Sicherheit der eigenen Kommunikation und Nutzen der Übungen mit Schauspielpatient*innen erfasst. Zusätzlich wurden die Einstellungen der Studierenden zu Kommunikation und Empathie im Kontext der medizinischen Versorgung bewertet. Die Antworten der Studierenden wurden mithilfe von Varianzanalysen (ANOVA) über die Semester hinweg verglichen.Ergebnisse: Insgesamt gaben die Studierenden an, dass sich ihre Kommunikationskompetenz durch die Teilnahme am KomCuA verbessert habe, und bewerteten das Üben mit Schauspielpatient*innen als sehr hilfreich (große Effektstärken). Im Vergleich zu den Studierenden des 1. Semesters berichteten die Studierenden des 3. und >=5. Semesters über bessere Kommunikationskompetenzen (mittlere bis große Effektstärken). Die Relevanz von Empathie im Kontext der medizinischen Versorgung (mittlere Effektstärke) wurde von den Studierenden des >=5. Semesters höher eingestuft als von Studierenden des 1. und 3 Semesters. Schlussfolgerung: Das KomCuA hat sich als wirksames Kommunikationscurriculum erwiesen, das sowohl die Entwicklung von Kommunikationskompetenzen bei Medizinstudierenden befördert als auch zu einer positiven Einstellung zur Rolle von Empathie beiträgt. Weitere Studien, in denen Kommunikationskompetenzen und empathischen Einstellungen der Studierenden im Längsschnitt untersucht
Published: 2024

23. Yukawa-Lorentz symmetry of interacting non-Hermitian birefringent Dirac fermions

Author: Murshed, Sk Asrap, Roy, Bitan, Murshed, Sk Asrap, and Roy, Bitan
Abstract: The energy spectra of linearly dispersing gapless spin-3/2 Dirac fermions display birefringence, featuring two effective Fermi velocities, thus breaking the space-time Lorentz symmetry. Here, we consider a non-Hermitian (NH) generalization of this scenario by introducing a masslike anti-Hermitian birefringent Dirac operator to its Hermitian counterpart. The resulting NH operator shows real eigenvalue spectra over an extended NH parameter regime, and a combination of non-spatial and discrete rotational symmetries protects the gapless nature of such quasiparticles. However, at the brink of dynamic mass generation, triggered by Hubbardlike local interactions, the birefringent parameter always vanishes under coarse grain due to Yukawa-type interactions with scalar bosonic order-parameter fluctuations. The resulting quantum critical state is, therefore, described by two decoupled copies of spin-1/2 Dirac fermions with a unique terminal Fermi velocity, which is equal to the bosonic order-parameter velocity, thereby fostering an emergent space-time Lorentz symmetry. Furthermore, depending on the internal algebra between the anti-Hermitian birefringent Dirac operator and the candidate mass order, the system achieves the emergent Yukawa-Lorentz symmetry either by maintaining its non-Hermiticity or by recovering a full Hermiticity. We discuss the resulting quantum critical phenomena and possible microscopic realizations of the proposed scenarios., Comment: 12 Pages, 3 Figures and 1 Table
Published: 2024

24. On supertranslation invariant Lorentz charges

Author: Chakraborty, Sumanta, Hoque, Sk Jahanur, Virmani, Amitabh, Chakraborty, Sumanta, Hoque, Sk Jahanur, and Virmani, Amitabh
Abstract: In recent papers, Fuentealba, Henneaux, and Troessaert (FHT) gave definitions for supertranslation invariant Lorentz charges in the ADM Hamiltonian formalism and showed that their definitions match with the Chen, Wang, Yau (CWY) definitions of Lorentz charges at null infinity which are free from ``supertranslation ambiguities''. In this brief note, motivated by the analysis of FHT, we write expressions for the supertranslation invariant Lorentz charges in Beig-Schmidt variables at spacelike and timelike infinity. We present calculations, building upon the work of Comp\`ere, Gralla, and Wei (CGW), to show that our expressions for supertranslation invariant Lorentz charges match the CWY definitions at null infinity., Comment: v1, 15 pages, no figures, for the special issue QG@RRI in Gen. Rel. Grav
Published: 2024

25. de Sitter Teukolsky waves

Author: Harsh, Hoque, Sk Jahanur, Kashyap, Sitender Pratap, Virmani, Amitabh, Harsh, Hoque, Sk Jahanur, Kashyap, Sitender Pratap, and Virmani, Amitabh
Abstract: We present de Sitter Teukolsky waves -- linearised quadrupolar gravitational waves in the transverse-traceless gauge in de Sitter spacetime. In the cosmological constant $\Lambda$ going to zero limit, our solutions match to Teukolsky solutions. For non-zero $\Lambda$, we compare our solutions to the wider literature, where different authors have constructed linearised gravitational perturbations in de Sitter spacetime with varied motivations. For de Sitter Teukolsky waves, we compute the energy flux across future timelike infinity $\mathcal{I}^{+}$ and show that it is manifestly positive., Comment: 45 pages, 2 figures
Published: 2024

26. Fabrication of lightweight ceramics of complex geometries in freezing weather

Author: Hossain, SK Saddam, Akhtar, Farid, Hossain, SK Saddam, and Akhtar, Farid
Abstract: Natural freezing weather is used to fabricate lightweight ceramic monoliths with aligned pores for the first time. A stable aqueous-based alumina slurry (16 vol%) was formulated and frozen at −10 to −25 °C in natural freezing environment. The thermally treated ceramic monoliths achieved ∼82–88 vol% porosity with short-range aligned pores. Finally, the unidirectional pores in the structure are achieved using a mold with thermal insulation properties. Natural freezing saves 300 kJ of energy per kg of alumina slurry. Mechanically stable lightweight porous ceramics with complex geometries can be designed without cryogenic fluids, refrigeration, or thermal baths. Implementing natural freezing in the porous ceramics industries, particularly in the Arctic region, will bring forward a sustainable approach by reducing energy consumption and CO2 emissions., Validerad;2024;Nivå 2;2024-07-02 (joosat);Full text license: CC BY
Published: 2024
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27. Effect of oxide nanoparticles in aqueous alumina inks for material extrusion additive manufacturing

Author: Hossain, Sk Saddam, Akhtar, Farid, Hossain, Sk Saddam, and Akhtar, Farid
Abstract: The effects of the addition of nanoparticles (NPs) of alumina (NA) and silica (NS) on the rheological properties of aqueous-based ceramic inks for material extrusion-based additive manufacturing (ME-AM) and sinterability of printed ceramic components were investigated. The inks were first designed using bimodal particle sizes of NPs with micro-alumina, and their solids fraction in printable inks was found to be dependent on the functionalization properties of NPs. NAs create a lubricating effect in the ink system and improve solids-loading in the ink. Meanwhile, NSs form silanol bonds in aqueous-based inks that increase viscosity and reduce solids-loading for a printable ME-AM ink. The inks containing NPs with higher solids-loading and optimized rheology were printed using ME-AM, and the printed components were sintered at 1400–1550 ºC. Adding NPs in the ink significantly improved the sinterability of printed shapes by enhancing sintering kinetics and filling-effects, with NS having a more significant effect than NA., Validerad;2024;Nivå 2;2024-06-28 (hanlid);Full text licence: CC BY
Published: 2024
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28. BIG DATA ANALYTICS FOR ENHANCED BUSINESS INTELLIGENCE IN FORTUNE 1000 COMPANIES: STRATEGIES, CHALLENGES, AND OUTCOMES

Author: Alam, Md Rasel Ul, Shabbir, Sk Abdur Rahim, Alam, Md Rasel Ul, and Shabbir, Sk Abdur Rahim
Abstract: This study investigates the transformative impact of big data analytics and business intelligence on the operations and strategic decision-making of Fortune 1000 companies, with a focus on Walmart. Walmart's integration of advanced data analytics tools has enabled significant optimization across various business areas, including inventory management, customer engagement, and supply chain operations. Leveraging big data, Walmart has gained deep insights into customer behavior, allowing for accurate demand forecasting and streamlined operations, which enhance operational efficiency and competitive advantage. The study highlights Walmart's use of predictive analytics to improve inventory management and supply chain efficiency, demonstrating how analyzing purchasing patterns and customer preferences reduces stockouts and excess inventory, thus boosting customer satisfaction and minimizing costs. Despite its advanced infrastructure, Walmart faces challenges in data integration and real-time analytics due to data silos created by its vast operations. Enhancing real-time analytics integration and data governance practices is crucial to ensure data quality, security, and compliance. Additionally, the study examines Walmart's strategic use of dynamic pricing algorithms to adjust prices in real-time based on market conditions, effectively maximizing sales and profitability, aligning with previous research on dynamic pricing benefits in retail. Furthermore, the broader economic implications of Walmart's data-driven strategies are discussed, noting that while Walmart's efficient operations and lower prices benefit consumers, they also pose challenges for small local businesses. This study provides a detailed analysis of Walmart's leverage of big data analytics and business intelligence to sustain its competitive advantage and drive business success, offering valuable insights for other Fortune 1000 companies on the importance of technology, organizational culture, and governanc
Published: 2024

29. Effect of mechanical weakening and crack formation on caprock integrity during underground hydrogen storage in depleted gas reservoirs - A comprehensive review

Author: Dilshan, RADP, Perera, MSA, Matthai, SK, Dilshan, RADP, Perera, MSA, and Matthai, SK
Published: 2024

30. Contrast-Induced Encephalopathy Following Transcatheter Aortic Valve Replacement: A Case Series

Author: Vinayak, M, Ahmed, A, Koshy, AN, Bronster, DJ, Dangas, GD, Tang, GHL, Kini, AS, Sharma, SK, Khera, S, Vinayak, M, Ahmed, A, Koshy, AN, Bronster, DJ, Dangas, GD, Tang, GHL, Kini, AS, Sharma, SK, and Khera, S
Abstract: Contrast-induced encephalopathy (CIE) is an idiopathic reaction following iodine-contrast dye administration in patients undergoing angiographic procedures. While it has been well-documented following coronary and carotid interventions, literature on CIE following transcatheter aortic valve replacement is limited. We report the multidisciplinary management of 3 patients with CIE following transcatheter aortic valve replacement.
Published: 2024

31. Advancing agroecosystem modelling of nitrogen losses with machine learning

Author: Lam, SK, Pan, B, Qin, AK, Chen, D, Lam, SK, Pan, B, Qin, AK, and Chen, D
Published: 2024

32. Salinity negatively correlates with the production and immunity of chicken: A molecular insight for food security and safety issues

Author: Biswas, S, Masum, MA, Sarkar, SK, Saud, B, Akter, R, Islam, KBMS, Karim, SJI, Rahman, MM, Golbar, HM, Alam, ME, Islam, MA, Begum, M, Ichii, O, Kon, Y, Biswas, S, Masum, MA, Sarkar, SK, Saud, B, Akter, R, Islam, KBMS, Karim, SJI, Rahman, MM, Golbar, HM, Alam, ME, Islam, MA, Begum, M, Ichii, O, and Kon, Y
Abstract: Salinity intrusion into the freshwater system due to climate change and anthropogenic activities is a growing global concern, which has made humans and domesticated animals more susceptible to diseases, resulting in less productivity. However, the effects of salinity on domesticated and wild birds, especially in terms of production and immunity, have not been fully elucidated yet. Therefore, this study was designed to examine the effects of salinity on the production and immunity of birds and the mechanisms by which immunity is compromised. Broiler chicks were subjected to different concentrations of salty water (control = normal water, treatment = 5 g/L, treatment = 10 g/L, and treatment = 15 g/L). The collected blood and organs from different groups of broilers were biochemically and histopathologically examined. Birds in salt-treated groups consumed significantly less feed than the control group, while the feed conversion ratio (FCR) was significantly higher. Body weight gain was significantly lower in salt-treated groups compared to control. Serum analysis revealed a lower systemic antibody titer in the salt-treated groups compared to the control. Primary lymphoid organs (thymus and bursa of Fabricius) were reduced in size in the salt-treated group due to cellular migration and depletion from these organs. Importantly, most of the parenchyma of lymphoid organs was replaced with fibrotic tissue. Gut microbes, Escherichia coli (E. coli) and Salmonella spp., from salt-treated groups, showed less viability but developed antibiotic resistance. Levels of salinity were significantly and negatively correlated with feed intake, body weight gain, antibody titer, lymphoid organ size, and viable count of gut microbes, while FCR, fibrosis of lymphoid organs, and antibiotic resistance were significant positively correlated. In conclusion, increased salinity is a possible threat to food security and safety as it decreases body weight gain, reduces immunity, and influences the
Published: 2024

33. Particle transport in fractured geo-energy reservoirs considering the effect of fluid inertia and turbulent flow: A Review

Author: Edirisinghe, EAAV, Perera, MSA, Elsworth, D, Matthai, SK, Goudeli, E, Edirisinghe, EAAV, Perera, MSA, Elsworth, D, Matthai, SK, and Goudeli, E
Published: 2024

34. Steatotic liver disease in rural and regional Victoria, according to the NAFLD and newer diagnostic criteria: retrospective cohort analyses of 2001-03 and 2016-18 data

Author: Vaz, K, Kemp, WW, Majeed, A, Lubel, J, Magliano, D, Glenister, K, Bourke, L, Simmons, D, Roberts, SK, Vaz, K, Kemp, WW, Majeed, A, Lubel, J, Magliano, D, Glenister, K, Bourke, L, Simmons, D, and Roberts, SK
Published: 2024

35. NANOGrav 15-year gravitational-wave background methods

Author: Johnson, AD, Meyers, PM, Baker, PT, Cornish, NJ, Hazboun, JS, Littenberg, TB, Romano, JD, Taylor, SR, Vallisneri, M, Vigeland, SJ, Olum, KD, Siemens, X, Ellis, JA, Van Haasteren, R, Hourihane, S, Agazie, G, Anumarlapudi, A, Archibald, AM, Arzoumanian, Z, Blecha, L, Brazier, A, Brook, PR, Burke-Spolaor, S, Bécsy, B, Casey-Clyde, JA, Charisi, M, Chatterjee, S, Chatziioannou, K, Cohen, T, Cordes, JM, Crawford, F, Cromartie, HT, Crowter, K, Decesar, ME, Demorest, PB, Dolch, T, Drachler, B, Ferrara, EC, Fiore, W, Fonseca, E, Freedman, GE, Garver-Daniels, N, Gentile, PA, Glaser, J, Good, DC, Gültekin, K, Jennings, RJ, Jones, ML, Kaiser, AR, Kaplan, DL, Kelley, LZ, Kerr, M, Key, JS, Laal, N, Lam, MT, Lamb, WG, Lazio, TJW, Lewandowska, N, Liu, T, Lorimer, DR, Luo, J, Lynch, RS, Ma, CP, Madison, DR, McEwen, A, McKee, JW, McLaughlin, MA, McMann, N, Meyers, BW, Mingarelli, CMF, Mitridate, A, Ng, C, Nice, DJ, Ocker, SK, Pennucci, TT, Perera, BBP, Pol, NS, Radovan, HA, Ransom, SM, Ray, PS, Sardesai, SC, Schmiedekamp, C, Schmiedekamp, A, Schmitz, K, Shapiro-Albert, BJ, Simon, J, Siwek, MS, Stairs, IH, Stinebring, DR, Stovall, K, Susobhanan, A, Swiggum, JK, Turner, JE, Unal, C, Wahl, HM, Witt, CA, Young, O, Johnson, AD, Meyers, PM, Baker, PT, Cornish, NJ, Hazboun, JS, Littenberg, TB, Romano, JD, Taylor, SR, Vallisneri, M, Vigeland, SJ, Olum, KD, Siemens, X, Ellis, JA, Van Haasteren, R, Hourihane, S, Agazie, G, Anumarlapudi, A, Archibald, AM, Arzoumanian, Z, Blecha, L, Brazier, A, Brook, PR, Burke-Spolaor, S, Bécsy, B, Casey-Clyde, JA, Charisi, M, Chatterjee, S, Chatziioannou, K, Cohen, T, Cordes, JM, Crawford, F, Cromartie, HT, Crowter, K, Decesar, ME, Demorest, PB, Dolch, T, Drachler, B, Ferrara, EC, Fiore, W, Fonseca, E, Freedman, GE, Garver-Daniels, N, Gentile, PA, Glaser, J, Good, DC, Gültekin, K, Jennings, RJ, Jones, ML, Kaiser, AR, Kaplan, DL, Kelley, LZ, Kerr, M, Key, JS, Laal, N, Lam, MT, Lamb, WG, Lazio, TJW, Lewandowska, N, Liu, T, Lorimer, DR, Luo, J, Lynch, RS, Ma, CP, Madison, DR, McEwen, A, McKee, JW, McLaughlin, MA, McMann, N, Meyers, BW, Mingarelli, CMF, Mitridate, A, Ng, C, Nice, DJ, Ocker, SK, Pennucci, TT, Perera, BBP, Pol, NS, Radovan, HA, Ransom, SM, Ray, PS, Sardesai, SC, Schmiedekamp, C, Schmiedekamp, A, Schmitz, K, Shapiro-Albert, BJ, Simon, J, Siwek, MS, Stairs, IH, Stinebring, DR, Stovall, K, Susobhanan, A, Swiggum, JK, Turner, JE, Unal, C, Wahl, HM, Witt, CA, and Young, O
Published: 2024

36. Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Author: Jahangir, CA, Page, DB, Broeckx, G, Gonzalez, CA, Burke, C, Murphy, C, Reis-Filho, JS, Ly, A, Harms, PW, Gupta, RR, Vieth, M, Hida, A, Kahila, M, Kos, Z, van Diest, PJ, Verbandt, S, Thagaard, J, Khiroya, R, Abduljabbar, K, Haab, GA, Acs, B, Adams, S, Almeida, JS, Alvarado-Cabrero, I, Azmoudeh-Ardalan, F, Badve, S, Baharun, NB, Bellolio, ER, Bheemaraju, V, Blenman, KRM, Fujimoto, LBM, Burgues, O, Chardas, A, Cheang, MCU, Ciompi, F, Cooper, LAD, Coosemans, A, Corredor, G, Portela, FLD, Deman, F, Demaria, S, Dudgeon, SN, Elghazawy, M, Fernandez-Martin, C, Fineberg, S, Fox, SB, Giltnane, JM, Gnjatic, S, Gonzalez-Ericsson, P, Grigoriadis, A, Halama, N, Hanna, MG, Harbhajanka, A, Hart, SN, Hartman, J, Hewitt, S, Horlings, HM, Husain, Z, Irshad, S, Janssen, EAM, Kataoka, TR, Kawaguchi, K, Khramtsov, A, Kiraz, U, Kirtani, P, Kodach, LL, Korski, K, Akturk, G, Scott, E, Kovacs, A, Laenkholm, A-V, Lang-Schwarz, C, Larsimont, D, Lennerz, JK, Lerousseau, M, Li, X, Madabhushi, A, Maley, SK, Narasimhamurthy, VM, Marks, DK, McDonald, ES, Mehrotra, R, Michiels, S, Kharidehal, D, Minhas, FUAA, Mittal, S, Moore, DA, Mushtaq, S, Nighat, H, Papathomas, T, Penault-Llorca, F, Perera, RD, Pinard, CJ, Pinto-Cardenas, JC, Pruneri, G, Pusztai, L, Rajpoot, NM, Rapoport, BL, Rau, TT, Ribeiro, JM, Rimm, D, Vincent-Salomon, A, Saltz, J, Sayed, S, Hytopoulos, E, Mahon, S, Siziopikou, KP, Sotiriou, C, Stenzinger, A, Sughayer, MA, Sur, D, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, EA, Tramm, T, Tran, WT, van Der Laak, J, Verghese, GE, Viale, G, Wahab, N, Walter, T, Waumans, Y, Wen, HY, Yang, W, Yuan, Y, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Stovgaard, ES, Salgado, R, Gallagher, WM, Rahman, A, Jahangir, CA, Page, DB, Broeckx, G, Gonzalez, CA, Burke, C, Murphy, C, Reis-Filho, JS, Ly, A, Harms, PW, Gupta, RR, Vieth, M, Hida, A, Kahila, M, Kos, Z, van Diest, PJ, Verbandt, S, Thagaard, J, Khiroya, R, Abduljabbar, K, Haab, GA, Acs, B, Adams, S, Almeida, JS, Alvarado-Cabrero, I, Azmoudeh-Ardalan, F, Badve, S, Baharun, NB, Bellolio, ER, Bheemaraju, V, Blenman, KRM, Fujimoto, LBM, Burgues, O, Chardas, A, Cheang, MCU, Ciompi, F, Cooper, LAD, Coosemans, A, Corredor, G, Portela, FLD, Deman, F, Demaria, S, Dudgeon, SN, Elghazawy, M, Fernandez-Martin, C, Fineberg, S, Fox, SB, Giltnane, JM, Gnjatic, S, Gonzalez-Ericsson, P, Grigoriadis, A, Halama, N, Hanna, MG, Harbhajanka, A, Hart, SN, Hartman, J, Hewitt, S, Horlings, HM, Husain, Z, Irshad, S, Janssen, EAM, Kataoka, TR, Kawaguchi, K, Khramtsov, A, Kiraz, U, Kirtani, P, Kodach, LL, Korski, K, Akturk, G, Scott, E, Kovacs, A, Laenkholm, A-V, Lang-Schwarz, C, Larsimont, D, Lennerz, JK, Lerousseau, M, Li, X, Madabhushi, A, Maley, SK, Narasimhamurthy, VM, Marks, DK, McDonald, ES, Mehrotra, R, Michiels, S, Kharidehal, D, Minhas, FUAA, Mittal, S, Moore, DA, Mushtaq, S, Nighat, H, Papathomas, T, Penault-Llorca, F, Perera, RD, Pinard, CJ, Pinto-Cardenas, JC, Pruneri, G, Pusztai, L, Rajpoot, NM, Rapoport, BL, Rau, TT, Ribeiro, JM, Rimm, D, Vincent-Salomon, A, Saltz, J, Sayed, S, Hytopoulos, E, Mahon, S, Siziopikou, KP, Sotiriou, C, Stenzinger, A, Sughayer, MA, Sur, D, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, EA, Tramm, T, Tran, WT, van Der Laak, J, Verghese, GE, Viale, G, Wahab, N, Walter, T, Waumans, Y, Wen, HY, Yang, W, Yuan, Y, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Stovgaard, ES, Salgado, R, Gallagher, WM, and Rahman, A
Published: 2024

37. The role of nitrogen management in achieving global sustainable development goals

Author: Zhang, C, Gu, B, Liang, X, Lam, SK, Zhou, Y, Chen, D, Zhang, C, Gu, B, Liang, X, Lam, SK, Zhou, Y, and Chen, D
Published: 2024

38. Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets

Author: Chlap, P, Min, H, Dowling, J, Field, M, Cloak, K, Leong, T, Lee, M, Chu, J, Tan, J, Tran, P, Kron, T, Sidhom, M, Wiltshire, K, Keats, S, Kneebone, A, Haworth, A, Ebert, MA, Vinod, SK, Holloway, L, Chlap, P, Min, H, Dowling, J, Field, M, Cloak, K, Leong, T, Lee, M, Chu, J, Tan, J, Tran, P, Kron, T, Sidhom, M, Wiltshire, K, Keats, S, Kneebone, A, Haworth, A, Ebert, MA, Vinod, SK, and Holloway, L
Abstract: BACKGROUND AND OBJECTIVES: Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not. METHODS: We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by mul
Published: 2024

39. Methylphenidate and Sleep Difficulties in Children and Adolescents With ADHD: Results From the 2-Year Naturalistic Pharmacovigilance ADDUCE Study

Author: Häge, A, Man, KKC, Inglis, SK, Buitelaar, J, Carucci, S, Danckaerts, M, Dittmann, RW, Falissard, B, Garas, P, Hollis, C, Konrad, K, Kovshoff, H, Liddle, E, McCarthy, S, Neubert, A, Nagy, P, Rosenthal, E, Sonuga-Barke, EJS, Zuddas, A, Wong, ICK, Coghill, D, Banaschewski, T, Häge, A, Man, KKC, Inglis, SK, Buitelaar, J, Carucci, S, Danckaerts, M, Dittmann, RW, Falissard, B, Garas, P, Hollis, C, Konrad, K, Kovshoff, H, Liddle, E, McCarthy, S, Neubert, A, Nagy, P, Rosenthal, E, Sonuga-Barke, EJS, Zuddas, A, Wong, ICK, Coghill, D, and Banaschewski, T
Abstract: OBJECTIVE: Short-term RCTs have demonstrated that MPH-treatment significantly reduces ADHD-symptoms, but is also associated with adverse events, including sleep problems. However, data on long-term effects of MPH on sleep remain limited. METHODS: We performed a 2-year naturalistic prospective pharmacovigilance multicentre study. Participants were recruited into three groups: ADHD patients intending to start MPH-treatment (MPH-group), those not intending to use ADHD-medication (no-MPH-group), and a non-ADHD control-group. Sleep problems were assessed with the Children's-Sleep-Habits-Questionnaire (CSHQ). RESULTS: 1,410 participants were enrolled. Baseline mean CSHQ-total-sleep-scores could be considered clinically significant for the MPH-group and the no-MPH-group, but not for controls. The only group to show a significant increase in any aspect of sleep from baseline to 24-months was the control-group. Comparing the MPH- to the no-MPH-group no differences in total-sleep-score changes were found. CONCLUSION: Our findings support that sleep-problems are common in ADHD, but don't suggest significant negative long-term effects of MPH on sleep.
Published: 2024

40. The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening.

Author: Patrick, R, Naval-Sanchez, M, Deshpande, N, Huang, Y, Zhang, J, Chen, X, Yang, Y, Tiwari, K, Esmaeili, M, Tran, M, Mohamed, AR, Wang, B, Xia, D, Ma, J, Bayliss, J, Wong, K, Hun, ML, Sun, X, Cao, B, Cottle, DL, Catterall, T, Barzilai-Tutsch, H, Troskie, R-L, Chen, Z, Wise, AF, Saini, S, Soe, YM, Kumari, S, Sweet, MJ, Thomas, HE, Smyth, IM, Fletcher, AL, Knoblich, K, Watt, MJ, Alhomrani, M, Alsanie, W, Quinn, KM, Merson, TD, Chidgey, AP, Ricardo, SD, Yu, D, Jardé, T, Cheetham, SW, Marcelle, C, Nilsson, SK, Nguyen, Q, White, MD, Nefzger, CM, Patrick, R, Naval-Sanchez, M, Deshpande, N, Huang, Y, Zhang, J, Chen, X, Yang, Y, Tiwari, K, Esmaeili, M, Tran, M, Mohamed, AR, Wang, B, Xia, D, Ma, J, Bayliss, J, Wong, K, Hun, ML, Sun, X, Cao, B, Cottle, DL, Catterall, T, Barzilai-Tutsch, H, Troskie, R-L, Chen, Z, Wise, AF, Saini, S, Soe, YM, Kumari, S, Sweet, MJ, Thomas, HE, Smyth, IM, Fletcher, AL, Knoblich, K, Watt, MJ, Alhomrani, M, Alsanie, W, Quinn, KM, Merson, TD, Chidgey, AP, Ricardo, SD, Yu, D, Jardé, T, Cheetham, SW, Marcelle, C, Nilsson, SK, Nguyen, Q, White, MD, and Nefzger, CM
Abstract: A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening.
Published: 2024

41. Pre-diagnosis tea and coffee consumption and survival after a diagnosis of ovarian cancer: results from the Ovarian Cancer Association Consortium.

Author: Nagle, CM, Ibiebele, TI, Bandera, EV, Cramer, D, Doherty, JA, Giles, GG, Goodman, MT, Hanley, GE, Harris, HR, Jensen, A, Kjaer, SK, Lee, AW, Milne, RL, Qin, B, Richardson, J, Sasamoto, N, Sieh, W, Terry, KL, Titus, L, Trabert, B, Wentzensen, N, Wu, AH, Berchuck, A, Pike, M, Pearce, CL, Webb, PM, Nagle, CM, Ibiebele, TI, Bandera, EV, Cramer, D, Doherty, JA, Giles, GG, Goodman, MT, Hanley, GE, Harris, HR, Jensen, A, Kjaer, SK, Lee, AW, Milne, RL, Qin, B, Richardson, J, Sasamoto, N, Sieh, W, Terry, KL, Titus, L, Trabert, B, Wentzensen, N, Wu, AH, Berchuck, A, Pike, M, Pearce, CL, and Webb, PM
Abstract: BACKGROUND: Tea and coffee are the most frequently consumed beverages in the world. Green tea in particular contains compounds with potential anti-cancer effects, but its association with survival after ovarian cancer is uncertain. METHODS: We investigated the associations between tea and coffee consumption before diagnosis and survival using data from 10 studies in the Ovarian Cancer Association Consortium. Data on tea (green, black, herbal), coffee and caffeine intake were available for up to 5724 women. We used Cox proportional hazards regression to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). RESULTS: Compared with women who did not drink any green tea, consumption of one or more cups/day was associated with better overall survival (aHR = 0.84, 95% CI 0.71-1.00, p-trend = 0.04). A similar association was seen for ovarian cancer-specific survival in five studies with this information (aHR = 0.81, 0.66-0.99, p-trend = 0.045). There was no consistent variation between subgroups defined by clinical or lifestyle characteristics and adjustment for other aspects of lifestyle did not appreciably alter the estimates. We found no evidence of an association between coffee, black or herbal tea, or caffeine intake and survival. CONCLUSION: The observed association with green tea consumption before diagnosis raises the possibility that consumption after diagnosis might improve patient outcomes.
Published: 2024

42. Can species climate niche predict canopy growth, functional traits and phenotypic plasticity in urban trees?

Author: Kibria, MG, Tjoelker, MG, Marchin, RM, Arndt, SK, Rymer, PD, Kibria, MG, Tjoelker, MG, Marchin, RM, Arndt, SK, and Rymer, PD
Published: 2024

43. Hybrid deep learning model for wave height prediction in Australia's wave energy region

Author: Ahmed, AAM, Jui, SJJ, AL-Musaylh, MS, Raj, N, Saha, R, Deo, RC, Saha, SK, Ahmed, AAM, Jui, SJJ, AL-Musaylh, MS, Raj, N, Saha, R, Deo, RC, and Saha, SK
Published: 2024

44. Spatially analysed expansion of individual street tree crowns enables species-specific crown expansion predictions in different rainfall zones

Author: Torquato, PR, Hahs, AK, Szota, C, Arndt, SK, Sun, Q, Hurley, J, Livesley, SJ, Torquato, PR, Hahs, AK, Szota, C, Arndt, SK, Sun, Q, Hurley, J, and Livesley, SJ
Published: 2024

45. Solastalgia following the Australian summer of bushfires: Qualitative and quantitative insights about environmental distress and recovery

Author: Stanley, SK, Heffernan, T, Macleod, E, Lane, J, Walker, I, Evans, O, Greenwood, LM, Kurz, T, Calear, AL, Reynolds, J, Cruwys, T, Christensen, BK, Sutherland, S, Rodney, RM, Stanley, SK, Heffernan, T, Macleod, E, Lane, J, Walker, I, Evans, O, Greenwood, LM, Kurz, T, Calear, AL, Reynolds, J, Cruwys, T, Christensen, BK, Sutherland, S, and Rodney, RM
Published: 2024

46. Bio-corrosion in concrete sewer systems: Mechanisms and mitigation strategies

Author: Pramanik, SK, Bhuiyan, M, Robert, D, Roychand, R, Gao, L, Cole, I, Pramanik, BK, Pramanik, SK, Bhuiyan, M, Robert, D, Roychand, R, Gao, L, Cole, I, and Pramanik, BK
Abstract: The deterioration of concrete sewer structures due to bio-corrosion presents critical and escalating challenges from structural, economic and environmental perspectives. Despite decades of research, this issue remains inadequately addressed, resulting in billions of dollars in maintenance costs and a shortened service life for sewer infrastructure worldwide. This challenge is exacerbated by the absence of standardized test methods and universally accepted mitigation strategies, leaving industries and stakeholders confronting an increasingly pressing problem. This paper aims to bridge this knowledge gap by providing a comprehensive review of the complex mechanisms of bio-corrosion, focusing on the formation and accumulation of hydrogen sulfide, its conversion into sulfuric acid and the subsequent deterioration of concrete materials. The paper also explores various factors affecting bio-corrosion rates, including environmental conditions, concrete properties and wastewater characteristics. The paper further highlights existing corrosion test strategies, such as chemical tests, in-situ tests and microbial simulations tests along with their general analytical parameters. The conversion of hydrogen sulfide into sulfuric acid is a primary cause of concrete decay and its progression is influenced by environmental conditions, inherent concrete characteristics, and the composition of wastewater. Through illustrative case studies, the paper assesses the practical implications and efficacy of prevailing mitigation techniques. Coating materials provide a protective barrier against corrosive agents among the discussed techniques, while optimised concrete mix designs enhance the inherent resistance and durability of the concrete matrix. Finally, this review also outlines the future prospects and challenges in bio-corrosion research with an aim to promote the creation of more resilient and cost-efficient materials for sewer systems.
Published: 2024

47. NAFLD and MAFLD independently increase the risk of major adverse cardiovascular events (MACE): a 20-year longitudinal follow-up study from regional Australia

Author: Vaz, K, Kemp, W, Majeed, A, Lubel, J, Magliano, DJ, Glenister, KM, Bourke, L, Simmons, D, Roberts, SK, Vaz, K, Kemp, W, Majeed, A, Lubel, J, Magliano, DJ, Glenister, KM, Bourke, L, Simmons, D, and Roberts, SK
Abstract: BACKGROUND AND AIMS: The association between fatty liver disease (FLD) and cardiovascular disease (CVD) in an Australian context has yet to be defined. The primary aim of this study was to investigate the association between FLD and 3-point major adverse cardiovascular events (MACE). METHODS: This was a longitudinal follow-up study of a randomly sampled adult cohort from regional Australia between 2001 and 2003. Baseline covariates included demographic details, anthropometry, health and lifestyle data, and laboratory tests. Non-alcoholic fatty liver disease (NAFLD) and metabolic-(dysfunction) associated fatty liver disease (MAFLD) were diagnosed in participants with fatty liver index (FLI) ≥ 60 and meeting other standard criteria. ICD-10 codes were used to define clinical outcomes linked to hospitalisations. Three-point MACE defined as non-fatal myocardial infarction (MI) and cerebrovascular accident (CVA) and CVD death. RESULTS: In total, 1324 and 1444 participants met inclusion criteria for NAFLD and MAFLD analysis, respectively. Over 23,577 and 25,469 person-years follow-up, NAFLD and MAFLD were independent predictors for 3-point MACE, adjusting for demographic covariates and known cardiometabolic risk factors, whilst considering non-CVD death as a competing event (NAFLD: sub-hazard ratio [sHR] 1.56, 95% confidence interval [CI 1.12-2.19]; MAFLD: sHR 1.51, 95% CI 1.11-2.06). The results held true on several sensitivity analyses. CONCLUSIONS: Both forms of FLD increase the risk for CVD independent of traditional cardiometabolic risk factors.
Published: 2024

48. ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

Author: Kuczynski, MT, Neeteson, NJ, Stok, KS, Burghardt, AJ, Hernandez, MAE, Vicory, J, Tse, JJ, Durongbhan, P, Bonaretti, S, Wong, AKO, Boyd, SK, Manske, SL, Kuczynski, MT, Neeteson, NJ, Stok, KS, Burghardt, AJ, Hernandez, MAE, Vicory, J, Tse, JJ, Durongbhan, P, Bonaretti, S, Wong, AKO, Boyd, SK, and Manske, SL
Published: 2024

49. Healthcare-associated infections in long-term care facilities: a systematic review and meta-analysis of point prevalence studies

Author: Bennett, N, Tanamas, SK, James, R, Ierano, C, Malloy, MJ, Watson, E, Sluggett, JK, Dunt, D, Thursky, K, Worth, LJ, Bennett, N, Tanamas, SK, James, R, Ierano, C, Malloy, MJ, Watson, E, Sluggett, JK, Dunt, D, Thursky, K, and Worth, LJ
Abstract: Objectives Residents of long-term care facilities (LTCFs) are especially vulnerable to acquiring healthcare-associated infections (HAIs). Our systematic review and meta-analysis estimated the burden of HAIs, identified the most frequent HAIs and explored the impact of facility-level and surveillance methodological differences on HAI burden in LTCFs, as determined by point prevalence studies (PPS). Design Systematic review and meta-analysis. Data sources Bibliographic databases MEDLINE (Ovid), Embase (Ovid) and CINAHL (EBSCOhost) were searched for potentially eligible English-language original research publications. References of short-listed full-text publications, the European Centre for Disease Control and Prevention website and an unpublished 2016–2022 Australian Aged Care PPS report were also checked. Eligibility criteria PPS monitoring HAIs, published and undertaken between January 1991 and June 2023 in LTCFs. Data extraction and synthesis Two reviewers independently screened for eligible PPS and if included, assessed risk of bias for each PPS using the Joanna Briggs Institute (JBI) critical appraisal tool for prevalence studies. Meta-analysis was performed using a generalised linear mixed model. Results 31 publications (including 123 PPS from 33 countries encompassing 709 860 residents) were included. Nine PPS (7.3%) were allocated a JBI quality score greater than 80% while 30 PPS (24.4%) were allocated a score between 70% and 80%. The overall pooled HAI point prevalence was 3.5% (95% CI 3.1% to 4.0%); 3.9% (95% CI 3.2% to 4.7%) when higher bias-risk PPS (<70% quality score) were excluded. Of 120 PPS, the most frequently reported HAIs were urinary tract (UTIs; 38.9%), respiratory tract (RTIs; 33.6%) and skin or soft tissue (SSTIs; 23.7%) infections. HAI point prevalence varied by geographical region (p<0.001), study decade (p<0.001) and HAI surveillance definitions used (p<0.001). There was no difference across facility types (p=0.57) or season (p=
Published: 2024

50. Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images

Author: Hans, R, Sharma, SK, Aickelin, U, Hans, R, Sharma, SK, and Aickelin, U
Abstract: Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.
Published: 2024

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