A constructive study of the module structure of rings of partial differential operators

The purpose of this paper is to develop constructive versions of Stafford's theorems on the module structure of Weyl algebras A n (k) (i.e., the rings of partial differential operators with polynomial coefficients) over a base field k of characteristic zero. More generally, based on results of Stafford and Coutinho-Holland, we develop constructive versions of Stafford's theorems for very simple domains D. The algorithmization is based on the fact that certain inhomogeneous quadratic equations admit solutions in a very simple domain. We show how to explicitly compute a unimodular element of a finitely generated left D-module of rank at least two. This result is used to constructively decompose any finitely generated left D-module into a direct sum of a free left D-module and a left D-module of rank at most one. If the latter is torsion-free, then we explicitly show that it is isomorphic to a left ideal of D which can be generated by two elements. Then, we give an algorithm which reduces the number of generators of a finitely presented left D-module with module of relations of rank at least two. In particular, any finitely generated torsion left D-module can be generated by two elements and is the homomorphic image of a projective ideal whose construction is explicitly given. Moreover, a non-torsion but non-free left D-module of rank r can be generated by r+1 elements but no fewer. These results are implemented in the Stafford package for D=A n (k) and their system-theoretical interpretations are given within a D-module approach. Finally, we prove that the above results also hold for the ring of ordinary differential operators with either formal power series or locally convergent power series coefficients and, using a result of Caro-Levcovitz, also for the ring of partial differential operators with coefficients in the field of fractions of the ring of formal power series or of the ring of locally convergent power series. © 2014 Springer Science+Business Media

Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia

Background There are few validated fluid biomarkers in frontotemporal dementia (FTD). Glial fibrillary acidic protein (GFAP) is a measure of astrogliosis, a known pathological process of FTD, but has yet to be explored as potential biomarker. Methods Plasma GFAP and neurofilament light chain (NfL) concentration were measured in 469 individuals enrolled in the Genetic FTD Initiative: 114 C9orf72 expansion carriers (74 presymptomatic, 40 symptomatic), 119 GRN mutation carriers (88 presymptomatic, 31 symptomatic), 53 MAPT mutation carriers (34 presymptomatic, 19 symptomatic) and 183 non-carrier controls. Biomarker measures were compared between groups using linear regression models adjusted for age and sex with family membership included as random effect. Participants underwent standardised clinical assessments including the Mini-Mental State Examination (MMSE), Frontotemporal Lobar Degeneration-C linical Dementia Rating scale and MRI. Spearman's correlation coefficient was used to investigate the relationship of plasma GFAP to clinical and imaging measures. Results Plasma GFAP concentration was significantly increased in symptomatic GRN mutation carriers (adjusted mean difference from controls 192.3 pg/mL, 95% CI 126.5 to 445.6), but not in those with C9orf72 expansions (9.0, -61.3 to 54.6), MAPT mutations (12.7, -33.3 to 90.4) or the presymptomatic groups. GFAP concentration was significantly positively correlated with age in both controls and the majority of the disease groups, as well as with NfL concentration. In the presymptomatic period, higher GFAP concentrations were correlated with a lower cognitive score (MMSE) and lower brain volume, while in the symptomatic period, higher concentrations were associated with faster rates of atrophy in the temporal lobe. Conclusions Raised GFAP concentrations appear to be unique to GRN-related FTD, with levels potentially increasing just prior to symptom onset, suggesting that GFAP may be an important marker of proximity to onset, and helpful for forthcoming therapeutic prevention trials

Observation of inverse Compton emission from a long γ-ray burst.

Long-duration γ-ray bursts (GRBs) originate from ultra-relativistic jets launched from the collapsing cores of dying massive stars. They are characterized by an initial phase of bright and highly variable radiation in the kiloelectronvolt-to-megaelectronvolt band, which is probably produced within the jet and lasts from milliseconds to minutes, known as the prompt emission1,2. Subsequently, the interaction of the jet with the surrounding medium generates shock waves that are responsible for the afterglow emission, which lasts from days to months and occurs over a broad energy range from the radio to the gigaelectronvolt bands1-6. The afterglow emission is generally well explained as synchrotron radiation emitted by electrons accelerated by the external shock7-9. Recently, intense long-lasting emission between 0.2 and 1 teraelectronvolts was observed from GRB 190114C10,11. Here we report multi-frequency observations of GRB 190114C, and study the evolution in time of the GRB emission across 17 orders of magnitude in energy, from 5 × 10-6 to 1012 electronvolts. We find that the broadband spectral energy distribution is double-peaked, with the teraelectronvolt emission constituting a distinct spectral component with power comparable to the synchrotron component. This component is associated with the afterglow and is satisfactorily explained by inverse Compton up-scattering of synchrotron photons by high-energy electrons. We find that the conditions required to account for the observed teraelectronvolt component are typical for GRBs, supporting the possibility that inverse Compton emission is commonly produced in GRBs

All-sky search for long-duration gravitational wave transients with initial LIGO

We present the results of a search for long-duration gravitational wave transients in two sets of data collected by the LIGO Hanford and LIGO Livingston detectors between November 5, 2005 and September 30, 2007, and July 7, 2009 and October 20, 2010, with a total observational time of 283.0 days and 132.9 days, respectively. The search targets gravitational wave transients of duration 10-500 s in a frequency band of 40-1000 Hz, with minimal assumptions about the signal waveform, polarization, source direction, or time of occurrence. All candidate triggers were consistent with the expected background; as a result we set 90% confidence upper limits on the rate of long-duration gravitational wave transients for different types of gravitational wave signals. For signals from black hole accretion disk instabilities, we set upper limits on the source rate density between 3.4×10-5 and 9.4×10-4 Mpc-3 yr-1 at 90% confidence. These are the first results from an all-sky search for unmodeled long-duration transient gravitational waves. © 2016 American Physical Society

Radioactivity control strategy for the JUNO detector

602siopenJUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day (cpd), therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz (i.e. ∼1 cpd accidental background) in the default fiducial volume, above an energy threshold of 0.7 MeV. [Figure not available: see fulltext.]openAbusleme A.; Adam T.; Ahmad S.; Ahmed R.; Aiello S.; Akram M.; An F.; An Q.; Andronico G.; Anfimov N.; Antonelli V.; Antoshkina T.; Asavapibhop B.; de Andre J.P.A.M.; Auguste D.; Babic A.; Baldini W.; Barresi A.; Basilico D.; Baussan E.; Bellato M.; Bergnoli A.; Birkenfeld T.; Blin S.; Blum D.; Blyth S.; Bolshakova A.; Bongrand M.; Bordereau C.; Breton D.; Brigatti A.; Brugnera R.; Bruno R.; Budano A.; Buscemi M.; Busto J.; Butorov I.; Cabrera A.; Cai H.; Cai X.; Cai Y.; Cai Z.; Cammi A.; Campeny A.; Cao C.; Cao G.; Cao J.; Caruso R.; Cerna C.; Chang J.; Chang Y.; Chen P.; Chen P.-A.; Chen S.; Chen X.; Chen Y.-W.; Chen Y.; Chen Y.; Chen Z.; Cheng J.; Cheng Y.; Chetverikov A.; Chiesa D.; Chimenti P.; Chukanov A.; Claverie G.; Clementi C.; Clerbaux B.; Conforti Di Lorenzo S.; Corti D.; Cremonesi O.; Dal Corso F.; Dalager O.; De La Taille C.; Deng J.; Deng Z.; Deng Z.; Depnering W.; Diaz M.; Ding X.; Ding Y.; Dirgantara B.; Dmitrievsky S.; Dohnal T.; Dolzhikov D.; Donchenko G.; Dong J.; Doroshkevich E.; Dracos M.; Druillole F.; Du S.; Dusini S.; Dvorak M.; Enqvist T.; Enzmann H.; Fabbri A.; Fajt L.; Fan D.; Fan L.; Fang J.; Fang W.; Fargetta M.; Fedoseev D.; Fekete V.; Feng L.-C.; Feng Q.; Ford R.; Formozov A.; Fournier A.; Gan H.; Gao F.; Garfagnini A.; Giammarchi M.; Giaz A.; Giudice N.; Gonchar M.; Gong G.; Gong H.; Gornushkin Y.; Gottel A.; Grassi M.; Grewing C.; Gromov V.; Gu M.; Gu X.; Gu Y.; Guan M.; Guardone N.; Gul M.; Guo C.; Guo J.; Guo W.; Guo X.; Guo Y.; Hackspacher P.; Hagner C.; Han R.; Han Y.; Hassan M.S.; He M.; He W.; Heinz T.; Hellmuth P.; Heng Y.; Herrera R.; Hor Y.K.; Hou S.; Hsiung Y.; Hu B.-Z.; Hu H.; Hu J.; Hu J.; Hu S.; Hu T.; Hu Z.; Huang C.; Huang G.; Huang H.; Huang W.; Huang X.; Huang X.; Huang Y.; Hui J.; Huo L.; Huo W.; Huss C.; Hussain S.; Ioannisian A.; Isocrate R.; Jelmini B.; Jen K.-L.; Jeria I.; Ji X.; Ji X.; Jia H.; Jia J.; Jian S.; Jiang D.; Jiang X.; Jin R.; Jing X.; Jollet C.; Joutsenvaara J.; Jungthawan S.; Kalousis L.; Kampmann P.; Kang L.; Karaparambil R.; Kazarian N.; Khan W.; Khosonthongkee K.; Korablev D.; Kouzakov K.; Krasnoperov A.; Kruth A.; Kutovskiy N.; Kuusiniemi P.; Lachenmaier T.; Landini C.; Leblanc S.; Lebrin V.; Lefevre F.; Lei R.; Leitner R.; Leung J.; Li D.; Li F.; Li F.; Li H.; Li H.; Li J.; Li M.; Li M.; Li N.; Li N.; Li Q.; Li R.; Li S.; Li T.; Li W.; Li W.; Li X.; Li X.; Li X.; Li Y.; Li Y.; Li Z.; Li Z.; Li Z.; Liang H.; Liang H.; Liao J.; Liebau D.; Limphirat A.; Limpijumnong S.; Lin G.-L.; Lin S.; Lin T.; Ling J.; Lippi I.; Liu F.; Liu H.; Liu H.; Liu H.; Liu H.; Liu H.; Liu J.; Liu J.; Liu M.; Liu Q.; Liu Q.; Liu R.; Liu S.; Liu S.; Liu S.; Liu X.; Liu X.; Liu Y.; Liu Y.; Lokhov A.; Lombardi P.; Lombardo C.; Loo K.; Lu C.; Lu H.; Lu J.; Lu J.; Lu S.; Lu X.; Lubsandorzhiev B.; Lubsandorzhiev S.; Ludhova L.; Luo F.; Luo G.; Luo P.; Luo S.; Luo W.; Lyashuk V.; Ma B.; Ma Q.; Ma S.; Ma X.; Ma X.; Maalmi J.; Malyshkin Y.; Mantovani F.; Manzali F.; Mao X.; Mao Y.; Mari S.M.; Marini F.; Marium S.; Martellini C.; Martin-Chassard G.; Martini A.; Mayer M.; Mayilyan D.; Mednieks I.; Meng Y.; Meregaglia A.; Meroni E.; Meyhofer D.; Mezzetto M.; Miller J.; Miramonti L.; Montini P.; Montuschi M.; Muller A.; Nastasi M.; Naumov D.V.; Naumova E.; Navas-Nicolas D.; Nemchenok I.; Nguyen Thi M.T.; Ning F.; Ning Z.; Nunokawa H.; Oberauer L.; Ochoa-Ricoux J.P.; Olshevskiy A.; Orestano D.; Ortica F.; Othegraven R.; Pan H.-R.; Paoloni A.; Parmeggiano S.; Pei Y.; Pelliccia N.; Peng A.; Peng H.; Perrot F.; Petitjean P.-A.; Petrucci F.; Pilarczyk O.; Pineres Rico L.F.; Popov A.; Poussot P.; Pratumwan W.; Previtali E.; Qi F.; Qi M.; Qian S.; Qian X.; Qian Z.; Qiao H.; Qin Z.; Qiu S.; Rajput M.U.; Ranucci G.; Raper N.; Re A.; Rebber H.; Rebii A.; Ren B.; Ren J.; Ricci B.; Robens M.; Roche M.; Rodphai N.; Romani A.; Roskovec B.; Roth C.; Ruan X.; Ruan X.; Rujirawat S.; Rybnikov A.; Sadovsky A.; Saggese P.; Sanfilippo S.; Sangka A.; Sanguansak N.; Sawangwit U.; Sawatzki J.; Sawy F.; Schever M.; Schwab C.; Schweizer K.; Selyunin A.; Serafini A.; Settanta G.; Settimo M.; Shao Z.; Sharov V.; Shaydurova A.; Shi J.; Shi Y.; Shutov V.; Sidorenkov A.; Simkovic F.; Sirignano C.; Siripak J.; Sisti M.; Slupecki M.; Smirnov M.; Smirnov O.; Sogo-Bezerra T.; Sokolov S.; Songwadhana J.; Soonthornthum B.; Sotnikov A.; Sramek O.; Sreethawong W.; Stahl A.; Stanco L.; Stankevich K.; Stefanik D.; Steiger H.; Steinmann J.; Sterr T.; Stock M.R.; Strati V.; Studenikin A.; Sun S.; Sun X.; Sun Y.; Sun Y.; Suwonjandee N.; Szelezniak M.; Tang J.; Tang Q.; Tang Q.; Tang X.; Tietzsch A.; Tkachev I.; Tmej T.; Treskov K.; Triossi A.; Troni G.; Trzaska W.; Tuve C.; Ushakov N.; van den Boom J.; van Waasen S.; Vanroyen G.; Vassilopoulos N.; Vedin V.; Verde G.; Vialkov M.; Viaud B.; Vollbrecht M.C.; Volpe C.; Vorobel V.; Voronin D.; Votano L.; Walker P.; Wang C.; Wang C.-H.; Wang E.; Wang G.; Wang J.; Wang J.; Wang K.; Wang L.; Wang M.; Wang M.; Wang M.; Wang R.; Wang S.; Wang W.; Wang W.; Wang W.; Wang X.; Wang X.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Z.; Wang Z.; Wang Z.; Wang Z.; Waqas M.; Watcharangkool A.; Wei L.; Wei W.; Wei W.; Wei Y.; Wen L.; Wiebusch C.; Wong S.C.-F.; Wonsak B.; Wu D.; Wu F.; Wu Q.; Wu Z.; Wurm M.; Wurtz J.; Wysotzki C.; Xi Y.; Xia D.; Xie X.; Xie Y.; Xie Z.; Xing Z.; Xu B.; Xu C.; Xu D.; Xu F.; Xu H.; Xu J.; Xu J.; Xu M.; Xu Y.; Xu Y.; Yan B.; Yan T.; Yan W.; Yan X.; Yan Y.; Yang A.; Yang C.; Yang C.; Yang H.; Yang J.; Yang L.; Yang X.; Yang Y.; Yang Y.; Yao H.; Yasin Z.; Ye J.; Ye M.; Ye Z.; Yegin U.; Yermia F.; Yi P.; Yin N.; Yin X.; You Z.; Yu B.; Yu C.; Yu C.; Yu H.; Yu M.; Yu X.; Yu Z.; Yu Z.; Yuan C.; Yuan Y.; Yuan Z.; Yuan Z.; Yue B.; Zafar N.; Zambanini A.; Zavadskyi V.; Zeng S.; Zeng T.; Zeng Y.; Zhan L.; Zhang A.; Zhang F.; Zhang G.; Zhang H.; Zhang H.; Zhang J.; Zhang J.; Zhang J.; Zhang J.; Zhang J.; Zhang P.; Zhang Q.; Zhang S.; Zhang S.; Zhang T.; Zhang X.; Zhang X.; Zhang X.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Z.; Zhang Z.; Zhao F.; Zhao J.; Zhao R.; Zhao S.; Zhao T.; Zheng D.; Zheng H.; Zheng M.; Zheng Y.; Zhong W.; Zhou J.; Zhou L.; Zhou N.; Zhou S.; Zhou T.; Zhou X.; Zhu J.; Zhu K.; Zhu K.; Zhu Z.; Zhuang B.; Zhuang H.; Zong L.; Zou J.Abusleme, A.; Adam, T.; Ahmad, S.; Ahmed, R.; Aiello, S.; Akram, M.; An, F.; An, Q.; Andronico, G.; Anfimov, N.; Antonelli, V.; Antoshkina, T.; Asavapibhop, B.; de Andre, J. P. A. M.; Auguste, D.; Babic, A.; Baldini, W.; Barresi, A.; Basilico, D.; Baussan, E.; Bellato, M.; Bergnoli, A.; Birkenfeld, T.; Blin, S.; Blum, D.; Blyth, S.; Bolshakova, A.; Bongrand, M.; Bordereau, C.; Breton, D.; Brigatti, A.; Brugnera, R.; Bruno, R.; Budano, A.; Buscemi, M.; Busto, J.; Butorov, I.; Cabrera, A.; Cai, H.; Cai, X.; Cai, Y.; Cai, Z.; Cammi, A.; Campeny, A.; Cao, C.; Cao, G.; Cao, J.; Caruso, R.; Cerna, C.; Chang, J.; Chang, Y.; Chen, P.; Chen, P. -A.; Chen, S.; Chen, X.; Chen, Y. -W.; Chen, Y.; Chen, Y.; Chen, Z.; Cheng, J.; Cheng, Y.; Chetverikov, A.; Chiesa, D.; Chimenti, P.; Chukanov, A.; Claverie, G.; Clementi, C.; Clerbaux, B.; Conforti Di Lorenzo, S.; Corti, D.; Cremonesi, O.; Dal Corso, F.; Dalager, O.; De La Taille, C.; Deng, J.; Deng, Z.; Deng, Z.; Depnering, W.; Diaz, M.; Ding, X.; Ding, Y.; Dirgantara, B.; Dmitrievsky, S.; Dohnal, T.; Dolzhikov, D.; Donchenko, G.; Dong, J.; Doroshkevich, E.; Dracos, M.; Druillole, F.; Du, S.; Dusini, S.; Dvorak, M.; Enqvist, T.; Enzmann, H.; Fabbri, A.; Fajt, L.; Fan, D.; Fan, L.; Fang, J.; Fang, W.; Fargetta, M.; Fedoseev, D.; Fekete, V.; Feng, L. -C.; Feng, Q.; Ford, R.; Formozov, A.; Fournier, A.; Gan, H.; Gao, F.; Garfagnini, A.; Giammarchi, M.; Giaz, A.; Giudice, N.; Gonchar, M.; Gong, G.; Gong, H.; Gornushkin, Y.; Gottel, A.; Grassi, M.; Grewing, C.; Gromov, V.; Gu, M.; Gu, X.; Gu, Y.; Guan, M.; Guardone, N.; Gul, M.; Guo, C.; Guo, J.; Guo, W.; Guo, X.; Guo, Y.; Hackspacher, P.; Hagner, C.; Han, R.; Han, Y.; Hassan, M. S.; He, M.; He, W.; Heinz, T.; Hellmuth, P.; Heng, Y.; Herrera, R.; Hor, Y. K.; Hou, S.; Hsiung, Y.; Hu, B. -Z.; Hu, H.; Hu, J.; Hu, J.; Hu, S.; Hu, T.; Hu, Z.; Huang, C.; Huang, G.; Huang, H.; Huang, W.; Huang, X.; Huang, X.; Huang, Y.; Hui, J.; Huo, L.; Huo, W.; Huss, C.; Hussain, S.; Ioannisian, A.; Isocrate, R.; Jelmini, B.; Jen, K. -L.; Jeria, I.; Ji, X.; Ji, X.; Jia, H.; Jia, J.; Jian, S.; Jiang, D.; Jiang, X.; Jin, R.; Jing, X.; Jollet, C.; Joutsenvaara, J.; Jungthawan, S.; Kalousis, L.; Kampmann, P.; Kang, L.; Karaparambil, R.; Kazarian, N.; Khan, W.; Khosonthongkee, K.; Korablev, D.; Kouzakov, K.; Krasnoperov, A.; Kruth, A.; Kutovskiy, N.; Kuusiniemi, P.; Lachenmaier, T.; Landini, C.; Leblanc, S.; Lebrin, V.; Lefevre, F.; Lei, R.; Leitner, R.; Leung, J.; Li, D.; Li, F.; Li, F.; Li, H.; Li, H.; Li, J.; Li, M.; Li, M.; Li, N.; Li, N.; Li, Q.; Li, R.; Li, S.; Li, T.; Li, W.; Li, W.; Li, X.; Li, X.; Li, X.; Li, Y.; Li, Y.; Li, Z.; Li, Z.; Li, Z.; Liang, H.; Liang, H.; Liao, J.; Liebau, D.; Limphirat, A.; Limpijumnong, S.; Lin, G. -L.; Lin, S.; Lin, T.; Ling, J.; Lippi, I.; Liu, F.; Liu, H.; Liu, H.; Liu, H.; Liu, H.; Liu, H.; Liu, J.; Liu, J.; Liu, M.; Liu, Q.; Liu, Q.; Liu, R.; Liu, S.; Liu, S.; Liu, S.; Liu, X.; Liu, X.; Liu, Y.; Liu, Y.; Lokhov, A.; Lombardi, P.; Lombardo, C.; Loo, K.; Lu, C.; Lu, H.; Lu, J.; Lu, J.; Lu, S.; Lu, X.; Lubsandorzhiev, B.; Lubsandorzhiev, S.; Ludhova, L.; Luo, F.; Luo, G.; Luo, P.; Luo, S.; Luo, W.; Lyashuk, V.; Ma, B.; Ma, Q.; Ma, S.; Ma, X.; Ma, X.; Maalmi, J.; Malyshkin, Y.; Mantovani, F.; Manzali, F.; Mao, X.; Mao, Y.; Mari, S. M.; Marini, F.; Marium, S.; Martellini, C.; Martin-Chassard, G.; Martini, A.; Mayer, M.; Mayilyan, D.; Mednieks, I.; Meng, Y.; Meregaglia, A.; Meroni, E.; Meyhofer, D.; Mezzetto, M.; Miller, J.; Miramonti, L.; Montini, P.; Montuschi, M.; Muller, A.; Nastasi, M.; Naumov, D. V.; Naumova, E.; Navas-Nicolas, D.; Nemchenok, I.; Nguyen Thi, M. T.; Ning, F.; Ning, Z.; Nunokawa, H.; Oberauer, L.; Ochoa-Ricoux, J. P.; Olshevskiy, A.; Orestano, D.; Ortica, F.; Othegraven, R.; Pan, H. -R.; Paoloni, A.; Parmeggiano, S.; Pei, Y.; Pelliccia, N.; Peng, A.; Peng, H.; Perrot, F.; Petitjean, P. -A.; Petrucci, F.; Pilarczyk, O.; Pineres Rico, L. F.; Popov, A.; Poussot, P.; Pratumwan, W.; Previtali, E.; Qi, F.; Qi, M.; Qian, S.; Qian, X.; Qian, Z.; Qiao, H.; Qin, Z.; Qiu, S.; Rajput, M. U.; Ranucci, G.; Raper, N.; Re, A.; Rebber, H.; Rebii, A.; Ren, B.; Ren, J.; Ricci, B.; Robens, M.; Roche, M.; Rodphai, N.; Romani, A.; Roskovec, B.; Roth, C.; Ruan, X.; Ruan, X.; Rujirawat, S.; Rybnikov, A.; Sadovsky, A.; Saggese, P.; Sanfilippo, S.; Sangka, A.; Sanguansak, N.; Sawangwit, U.; Sawatzki, J.; Sawy, F.; Schever, M.; Schwab, C.; Schweizer, K.; Selyunin, A.; Serafini, A.; Settanta, G.; Settimo, M.; Shao, Z.; Sharov, V.; Shaydurova, A.; Shi, J.; Shi, Y.; Shutov, V.; Sidorenkov, A.; Simkovic, F.; Sirignano, C.; Siripak, J.; Sisti, M.; Slupecki, M.; Smirnov, M.; Smirnov, O.; Sogo-Bezerra, T.; Sokolov, S.; Songwadhana, J.; Soonthornthum, B.; Sotnikov, A.; Sramek, O.; Sreethawong, W.; Stahl, A.; Stanco, L.; Stankevich, K.; Stefanik, D.; Steiger, H.; Steinmann, J.; Sterr, T.; Stock, M. R.; Strati, V.; Studenikin, A.; Sun, S.; Sun, X.; Sun, Y.; Sun, Y.; Suwonjandee, N.; Szelezniak, M.; Tang, J.; Tang, Q.; Tang, Q.; Tang, X.; Tietzsch, A.; Tkachev, I.; Tmej, T.; Treskov, K.; Triossi, A.; Troni, G.; Trzaska, W.; Tuve, C.; Ushakov, N.; van den Boom, J.; van Waasen, S.; Vanroyen, G.; Vassilopoulos, N.; Vedin, V.; Verde, G.; Vialkov, M.; Viaud, B.; Vollbrecht, M. C.; Volpe, C.; Vorobel, V.; Voronin, D.; Votano, L.; Walker, P.; Wang, C.; Wang, C. -H.; Wang, E.; Wang, G.; Wang, J.; Wang, J.; Wang, K.; Wang, L.; Wang, M.; Wang, M.; Wang, M.; Wang, R.; Wang, S.; Wang, W.; Wang, W.; Wang, W.; Wang, X.; Wang, X.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Z.; Wang, Z.; Wang, Z.; Wang, Z.; Waqas, M.; Watcharangkool, A.; Wei, L.; Wei, W.; Wei, W.; Wei, Y.; Wen, L.; Wiebusch, C.; Wong, S. C. -F.; Wonsak, B.; Wu, D.; Wu, F.; Wu, Q.; Wu, Z.; Wurm, M.; Wurtz, J.; Wysotzki, C.; Xi, Y.; Xia, D.; Xie, X.; Xie, Y.; Xie, Z.; Xing, Z.; Xu, B.; Xu, C.; Xu, D.; Xu, F.; Xu, H.; Xu, J.; Xu, J.; Xu, M.; Xu, Y.; Xu, Y.; Yan, B.; Yan, T.; Yan, W.; Yan, X.; Yan, Y.; Yang, A.; Yang, C.; Yang, C.; Yang, H.; Yang, J.; Yang, L.; Yang, X.; Yang, Y.; Yang, Y.; Yao, H.; Yasin, Z.; Ye, J.; Ye, M.; Ye, Z.; Yegin, U.; Yermia, F.; Yi, P.; Yin, N.; Yin, X.; You, Z.; Yu, B.; Yu, C.; Yu, C.; Yu, H.; Yu, M.; Yu, X.; Yu, Z.; Yu, Z.; Yuan, C.; Yuan, Y.; Yuan, Z.; Yuan, Z.; Yue, B.; Zafar, N.; Zambanini, A.; Zavadskyi, V.; Zeng, S.; Zeng, T.; Zeng, Y.; Zhan, L.; Zhang, A.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, H.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, P.; Zhang, Q.; Zhang, S.; Zhang, S.; Zhang, T.; Zhang, X.; Zhang, X.; Zhang, X.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Z.; Zhang, Z.; Zhao, F.; Zhao, J.; Zhao, R.; Zhao, S.; Zhao, T.; Zheng, D.; Zheng, H.; Zheng, M.; Zheng, Y.; Zhong, W.; Zhou, J.; Zhou, L.; Zhou, N.; Zhou, S.; Zhou, T.; Zhou, X.; Zhu, J.; Zhu, K.; Zhu, K.; Zhu, Z.; Zhuang, B.; Zhuang, H.; Zong, L.; Zou, J

All-sky search for long-duration gravitational wave transients with initial LIGO

We present the results of a search for long-duration gravitational wave transients in two sets of data collected by the LIGO Hanford and LIGO Livingston detectors between November 5, 2005 and September 30, 2007, and July 7, 2009 and October 20, 2010, with a total observational time of 283.0 days and 132.9 days, respectively. The search targets gravitational wave transients of duration 10-500 s in a frequency band of 40-1000 Hz, with minimal assumptions about the signal waveform, polarization, source direction, or time of occurrence. All candidate triggers were consistent with the expected background; as a result we set 90% confidence upper limits on the rate of long-duration gravitational wave transients for different types of gravitational wave signals. For signals from black hole accretion disk instabilities, we set upper limits on the source rate density between 3.4×10-5 and 9.4×10-4 Mpc-3 yr-1 at 90% confidence. These are the first results from an all-sky search for unmodeled long-duration transient gravitational waves. © 2016 American Physical Society

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset