AC1: Response to reviews of Adcock et al., 16 Feb 2017

Adcock, Karina E.

doi:10.5194/acp-2017-978-ac1

Cited by 7 publications

(10 citation statements)

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“…Standardized and transparent reporting of adverse donor outcomes is of paramount importance and continuous clinical auditing is warranted. (11)(12) The Clavien-Dindo classification of postoperative complications has been validated for liver transplantation and allows simple reproducible reporting of all complications.…”

Section: Discussionmentioning

confidence: 99%

Living - Donor Liver Transplantation at King Hussein Medical Center : Donor Complications and Outcome in the First 100 Donors

Ajarma¹,

Al-Faouri²,

Abbadi³

2016

JRMS

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Over a period of 11 years 100 live-liver donors underwent surgery. Demographic, clinical and perioperative data of these donors were collected. Postoperative complications were registered and classified according to the Clavien-Dindo classification. Statistical analysis was used to identify potential patients' or grafts' factors associated with complications. Results: The mean age of donors was 30.71±7.17 and mean body mass index was 24.50± 2.56. Three procedures were abandoned after laparotomy. 71 underwent right hepatectomy, 12 right hepatectomy with inclusion of middle hepatic vein, 11 left hepatectomy and 3 left lateral sectorectomy. The overall complication rate was 36% with most of these being minor grade I and II (26%) complications. 9 patients developed grade III complications while one patient had grade IVa. The mortality rate was zero. Older age and higher body mass index were identified as potential risk factors for complications. Gender, graft type, estimated future liver remnant, inclusion of middle hepatic vein and preoperative biochemical profile were not found in this study to correlate with occurrence of complication. Conclusion: Strict donor selection and meticulous surgical procedure remain the only modifiable factors in donor hepatectomy. Continuous transparent clinical audit is mandatory to identify potentially preventable adverse outcomes.

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Section: Discussionmentioning

confidence: 99%

Living - Donor Liver Transplantation at King Hussein Medical Center : Donor Complications and Outcome in the First 100 Donors

Ajarma¹,

Al-Faouri²,

Abbadi³

2016

JRMS

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“…However, the number of physical resources it requires scales as

. As we will see, this exponential scaling (with respect to the number of qubits) has been used to question whether the QRAM can be built in an experimental setting or whether it can provide a genuine computational advantage [ 7 , 47 ]. Fundamentally, the issue can be related to whether the exponential number of components needs to be continuously ‘active’.…”

Section: Data Access Communication and Parallelismmentioning

confidence: 99%

“…We do not aim for completeness but rather discuss only the most relevant results in quantum algorithms for learning. For the interested reader there are now a number of resources covering QML in the broader sense of the term [ 7 , 8 ]. For an introduction to quantum algorithms, we refer to the reviews of Montanaro [ 9 ] and Bacon & Van Dam [ 10 ], while for ML to the books by Bishop [ 11 ] and Murphy [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Quantum machine learning: a classical perspective

et al. 2018

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Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

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“…Igneous rocks -especially basaltic rocksand minerals and their alteration products can provide nutrients and habitats for microbes, and thus, are critical for assessing potential biosignatures (e.g., Hausrath et al, 2007;Baumeister et al, 2015;Cockell et al, 2019). Redox reactions occurring in igneous rocks can be used for energy and basaltic terrestrial analogue studies show that life can thrive in volcanic environments (Adcock et al, 2018;Des Marais et al, 2005). Specific igneous phases such as phosphates and hydrated silica provide essential nutrients for life and can host biosignatures, respectively (Adcock & Hausrath, 2015;Costello et al, 2020).…”

Section: Understanding and Finding Habitable Environmentsmentioning

confidence: 99%