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New Zealand's South Island (Te Waipounamu)
Lonely Planet New Zealand's South Island is your passport to the most relevant, up-to-date advice on what to see and skip, and what hidden discoveries await you. Skiing the scenic slopes around Queenstown, encounter wild kiwis on unspoilt Stewart Island, or indulge in deliciously fresh seafood in Kaikoura; all with your trusted travel companion.
Book
AMPK is a mechano-metabolic sensor linking cell adhesion and mitochondrial dynamics to Myosin-dependent cell migration
Cell migration is crucial for cancer dissemination. We find that AMP-activated protein kinase (AMPK) controls cell migration by acting as an adhesion sensing molecular hub. In 3-dimensional matrices, fast-migrating amoeboid cancer cells exert low adhesion/low traction linked to low ATP/AMP, leading to AMPK activation. In turn, AMPK plays a dual role controlling mitochondrial dynamics and cytoskeletal remodelling. High AMPK activity in low adhering migratory cells, induces mitochondrial fission, resulting in lower oxidative phosphorylation and lower mitochondrial ATP. Concurrently, AMPK inactivates Myosin Phosphatase, increasing Myosin II-dependent amoeboid migration. Reducing adhesion or mitochondrial fusion or activating AMPK induces efficient rounded-amoeboid migration. AMPK inhibition suppresses metastatic potential of amoeboid cancer cells in vivo, while a mitochondrial/AMPK-driven switch is observed in regions of human tumours where amoeboid cells are disseminating. We unveil how mitochondrial dynamics control cell migration and suggest that AMPK is a mechano-metabolic sensor linking energetics and the cytoskeleton.
Cell metabolism must adapt to the energy needs of migrating cells. This study finds that fast amoeboid migrating cells harbor high AMPK activity, which controls both mitochondrial dynamics and cytoskeletal remodeling, enabling reduced energy needs.
Journal Article
High Performance PostgreSQL for Rails: Reliable, Scalable, Maintainable Database Applications
Build faster, more reliable Rails apps by taking the best advanced PostgreSQL and Active Record capabilities, and using them to solve your application scale and growth challenges. Gain the skills needed to comfortably work with multi-terabyte databases, and with complex Active Record, SQL, and specialized Indexes. Develop your skills with PostgreSQL on your laptop, then take them into production, while keeping everything in sync. Make slow queries fast, perform any schema or data migration without errors, use scaling techniques like read/write splitting, partitioning, and sharding, to meet demanding workload requirements from Internet scale consumer apps to enterprise SaaS.Deepen your firsthand knowledge of high-scale PostgreSQL databases and Ruby on Rails applications with dozens of practical and hands-on exercises. Unlock the mysteries surrounding complex Active Record. Make any schema or data migration change confidently, without downtime. Grow your experience with modern and exclusive PostgreSQL features like SQL Merge, Returning, and Exclusion constraints. Put advanced capabilities like Full Text Search and Publish Subscribe mechanisms built into PostgreSQL to work in your Rails apps. Improve the quality of the data in your database, using the advanced and extensible system of types and constraints to reduce and eliminate application bugs. Tackle complex topics like how to improve query performance using specialized indexes. Discover how to effectively use built-in database functions and write your own, administer replication, and make the most of partitioning and foreign data wrappers. Use more than 40 well-supported open source tools to extend and enhance PostgreSQL and Ruby on Rails. Gain invaluable insights into database administration by conducting advanced optimizations - including high-impact database maintenance - all while solving real-world operational challenges. Take your new skills into production today and then take your PostgreSQL and Rails applications to a whole new level of reliability and performance.What You Need:A computer running macOS, Linux, or Windows and WSL2PostgreSQL version 16, installed by package manager, compiled, or running with DockerAn Internet connection
eBook
Controlling the dynamics of the Nek2 leucine zipper by engineering of “kinetic” disulphide bonds
Nek2 is a dimeric serine/ threonine protein kinase that belongs to the family of NIMA-related kinases (Neks). Its N-terminal catalytic domain and its C-terminal regulatory region are bridged by a leucine zipper, which plays an important role in the activation of Nek2's catalytic activity. Unusual conformational dynamics on the intermediary/slow timescale has thwarted all attempts so far to determine the structure of the Nek2 leucine zipper by means of X-ray crystallography and Nuclear Magnetic Resonance (NMR). Disulfide engineering, the strategic placement of non-native disulfide bonds into flexible regions flanking the coiled coil, was used to modulate the conformational exchange dynamics of this important dimerization domain. The resulting reduction in exchange rate leads to substantial improvements of important features in NMR spectra, such as line width, coherence transfer leakage and relaxation. These effects were comprehensively analyzed for the wild type protein, two single disulfide bond-bearing mutants and another double disulfide bonds-carrying mutant. Furthermore, exchange kinetics were measured across a wide temperature range, allowing for a detailed analysis of activation energy (ΔG‡) and maximal rate constant (k'ex). For one mutant carrying a disulfide bond at its C-terminus, a full backbone NMR assignment could be obtained for both conformers, demonstrating the benefits of the disulfide engineering. Our study demonstrates the first successful application of 'kinetic' disulfide bonds for the purpose of controlling the adverse effects of protein dynamics. Firstly, this provides a promising, robust platform for the full structural and functional investigation of the Nek2 leucine zipper in the future. Secondly, this work broadens the toolbox of protein engineering by disulfide bonds through the addition of a kinetic option in addition to the well-established thermodynamic uses of disulfide bonds.
Journal Article
Metabolomic and lipidomic plasma profile changes in human participants ascending to Everest Base Camp
At high altitude oxygen delivery to the tissues is impaired leading to oxygen insufficiency (hypoxia). Acclimatisation requires adjustment to tissue metabolism, the details of which remain incompletely understood. Here, metabolic responses to progressive environmental hypoxia were assessed through metabolomic and lipidomic profiling of human plasma taken from 198 human participants before and during an ascent to Everest Base Camp (5,300 m). Aqueous and lipid fractions of plasma were separated and analysed using proton (
1
H)-nuclear magnetic resonance spectroscopy and direct infusion mass spectrometry, respectively. Bayesian robust hierarchical regression revealed decreasing isoleucine with ascent alongside increasing lactate and decreasing glucose, which may point towards increased glycolytic rate. Changes in the lipid profile with ascent included a decrease in triglycerides (48–50 carbons) associated with
de novo
lipogenesis, alongside increases in circulating levels of the most abundant free fatty acids (palmitic, linoleic and oleic acids). Together, this may be indicative of fat store mobilisation. This study provides the first broad metabolomic account of progressive exposure to environmental hypobaric hypoxia in healthy humans. Decreased isoleucine is of particular interest as a potential contributor to muscle catabolism observed with exposure to hypoxia at altitude. Substantial changes in lipid metabolism may represent important metabolic responses to sub-acute exposure to environmental hypoxia.
Journal Article
Corneal confocal microscopy identifies small fibre damage and progression of diabetic neuropathy
Accurately quantifying the progression of diabetic peripheral neuropathy is key to identify individuals who will progress to foot ulceration and to power clinical intervention trials. We have undertaken detailed neuropathy phenotyping to assess the longitudinal utility of different measures of neuropathy in patients with diabetes. Nineteen patients with diabetes (age 52.5 ± 14.7 years, duration of diabetes 26.0 ± 13.8 years) and 19 healthy controls underwent assessment of symptoms and signs of neuropathy, quantitative sensory testing, autonomic nerve function, neurophysiology, intra-epidermal nerve fibre density (IENFD) and corneal confocal microscopy (CCM) to quantify corneal nerve fibre density (CNFD), branch density (CNBD) and fibre length (CNFL). Mean follow-up was 6.5 years. Glycated haemoglobin (
p
= 0.04), low-density lipoprotein-cholesterol (LDL-C) (
p
= 0.0009) and urinary albumin creatinine ratio (
p
< 0.0001) improved. Neuropathy symptom profile (
p
= 0.03), neuropathy disability score (
p
= 0.04), vibration perception threshold (
p
= 0.02), cold perception threshold (
p
= 0.006), CNFD (
p
= 0.03), CNBD (
p
< 0.0001), CNFL (
p
< 0.0001), IENFD (
p
= 0.04), sural (
p
= 0.02) and peroneal motor nerve conduction velocity (
p
= 0.03) deteriorated significantly. Change (∆) in CNFL correlated with ∆CPT (
p
= 0.006) and ∆Expiration/Inspiration ratio (
p
= 0.002) and ∆IENFD correlated with ∆CNFD (
p
= 0.005), ∆CNBD (
p
= 0.02) and ∆CNFL (
p
= 0.01). This study shows worsening of diabetic neuropathy across a range of neuropathy measures, especially CCM, despite an improvement in HbA1c and LDL-C. It further supports the utility of CCM as a rapid, non-invasive surrogate measure of diabetic neuropathy.
Journal Article
Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital
Background
Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes.
Methods
In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC).
Results
Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58).
Conclusion
With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.
Journal Article