Metabolic Responses to Carbohydrate Ingestion during Exercise: Associations between Carbohydrate Dose and Endurance Performance

Carbohydrate (CHO) ingestion during exercise lasting less than three hours improves endurance exercise performance but there is still debate about the optimal dose. We utilised stable isotopes and blood metabolite profiles to further examine metabolic responses to CHO (glucose only) ingestion in the 20–64 g·h−1 range, and to determine the association with performance outcome. In a double-blind, randomized cross-over design, male cyclists (n = 20, mean ± SD, age 34 ± 10 years, mass 75.8 ± 9 kg, peak power output 394 ± 36 W, VO2max 62 ± 9 mL·kg−1·min−1) completed four main experimental trials. Each trial involved a two-hour constant load ride (185 ± 25 W) followed by a time trial, where one of three CHO beverages, or a control (water), were administered every 15 min, providing 0, 20, 39 or 64 g CHO·h−1. Dual glucose tracer techniques, indirect calorimetry and blood analyses were used to determine glucose kinetics, exogenous CHO oxidation (EXO), endogenous CHO and fat oxidation; and metabolite responses. Regression analysis revealed that total exogenous CHO oxidised in the second hour of exercise, and suppression of serum NEFA concentration provided the best prediction model of performance outcome. However, the model could only explain ~19% of the variance in performance outcome. The present data demonstrate that consuming ~40 g·h−1 of CHO appears to be the minimum ingestion rate required to induce metabolic effects that are sufficient to impact upon performance outcome. These data highlight a lack of performance benefit and few changes in metabolic outcomes beyond an ingestion rate of 39 g·h−1. Further work is required to explore dose-response effects of CHO feeding and associations between multiple metabolic parameters and subsequent performance outcome

Direct Imaging of Protein Organization in an Intact Bacterial Organelle Using High-Resolution Atomic Force Microscopy

The function of bioenergetic membranes is strongly influenced by the spatial arrangement of their constituent membrane proteins. Atomic force microscopy (AFM) can be used to probe protein organization at high resolution, allowing individual proteins to be identified. However, previous AFM studies of biological membranes have typically required that curved membranes are ruptured and flattened during sample preparation, with the possibility of disruption of the native protein arrangement or loss of proteins. Imaging native, curved membranes requires minimal tip–sample interaction in both lateral and vertical directions. Here, long-range tip–sample interactions are reduced by optimizing the imaging buffer. Tapping mode AFM with high-resonance-frequency small and soft cantilevers, in combination with a high-speed AFM, reduces the forces due to feedback error and enables application of an average imaging force of tens of piconewtons. Using this approach, we have imaged the membrane organization of intact vesicular bacterial photosynthetic “organelles”, chromatophores. Despite the highly curved nature of the chromatophore membrane and lack of direct support, the resolution was sufficient to identify the photosystem complexes and quantify their arrangement in the native state. Successive imaging showed the proteins remain surprisingly static, with minimal rotation or translation over several-minute time scales. High-order assemblies of RC-LH1-PufX complexes are observed, and intact ATPases are successfully imaged. The methods developed here are likely to be applicable to a broad range of protein-rich vesicles or curved membrane systems, which are an almost ubiquitous feature of native organelles

Proteorhodopsin overproduction enhances the long-term viability of Escherichia coli

Genes encoding the photoreactive protein proteorhodopsin (PR) have been found in a wide range of marine bacterial species, reflecting the significant contribution that PR makes to energy flux and carbon cycling in ocean ecosystems. PR can also confer advantages to enhance the ability of marine bacteria to survive periods of starvation. Here, we investigate the effect of heterologously produced PR on the viability of Escherichia coli. Quantitative mass spectrometry shows that E. coli, exogenously supplied with the retinal cofactor, assembles as many as 187,000 holo-PR molecules per cell, accounting for approximately 47% of the membrane area; even cells with no retinal synthesize ∼148,000 apo-PR molecules per cell. We show that populations of E. coli cells containing PR exhibit significantly extended viability over many weeks, and we use single-cell Raman spectroscopy (SCRS) to detect holo-PR in 9-month-old cells. SCRS shows that such cells, even incubated in the dark and therefore with inactive PR, maintain cellular levels of DNA and RNA and avoid deterioration of the cytoplasmic membrane, a likely basis for extended viability. The substantial proportion of the E. coli membrane required to accommodate high levels of PR likely fosters extensive intermolecular contacts, suggested to physically stabilize the cell membrane and impart a long-term benefit manifested as extended viability in the dark. We propose that marine bacteria could benefit similarly from a high PR content, with a stabilized cell membrane extending survival when those bacteria experience periods of severe nutrient or light limitation in the oceans

From Monochrome to Technicolor: Simple Generic Approaches to Multicomponent Protein Nanopatterning Using Siloxanes with Photoremovable Protein-Resistant Protecting Groups.

We show that sequential protein deposition is possible by photodeprotection of films formed from a tetraethylene-glycol functionalized nitrophenylethoxycarbonyl-protected aminopropyltriethoxysilane (NPEOC-APTES). Exposure to near-UV irradiation removes the protein-resistant protecting group, and allows protein adsorption onto the resulting aminated surface. The protein resistance was tested using proteins with fluorescent labels and microspectroscopy of two-component structures formed by micro- and nanopatterning and deposition of yellow and green fluorescent proteins (YFP/GFP). Nonspecific adsorption onto regions where the protecting group remained intact was negligible. Multiple component patterns were also formed by near-field methods. Because reading and writing can be decoupled in a near-field microscope, it is possible to carry out sequential patterning steps at a single location involving different proteins. Up to four different proteins were formed into geometric patterns using near-field lithography. Interferometric lithography facilitates the organization of proteins over square cm areas. Two-component patterns consisting of 150 nm streptavidin dots formed within an orthogonal grid of bars of GFP at a period of ca. 500 nm could just be resolved by fluorescence microscopy

Decision Problems for Nash Equilibria in Stochastic Games

We analyse the computational complexity of finding Nash equilibria in stochastic multiplayer games with $\omega$-regular objectives. While the existence of an equilibrium whose payoff falls into a certain interval may be undecidable, we single out several decidable restrictions of the problem. First, restricting the search space to stationary, or pure stationary, equilibria results in problems that are typically contained in PSPACE and NP, respectively. Second, we show that the existence of an equilibrium with a binary payoff (i.e. an equilibrium where each player either wins or loses with probability 1) is decidable. We also establish that the existence of a Nash equilibrium with a certain binary payoff entails the existence of an equilibrium with the same payoff in pure, finite-state strategies.Comment: 22 pages, revised versio

YASA: yet another time series segmentation algorithm for anomaly detection in big data problems

Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection

On the Reliability of Meta-Analytic Reviews

The article addresses the issue of intercoder reliability in meta-analyses. The current practice of reporting a single, mean intercoder agreement score in meta-analytic research leads to systematic bias and overestimates the true reliability. An alternative approach is recommended in which average intercoder agreement scores or other reliability statistics are calculated within clusters of coded variables. These clusters form a hierarchy in which the correctness of coding decisions at a given level of the hierarchy is contingent on decisions made at higher levels. Two separate studies of intercoder agreement in meta-analysis are presented to assess the validity of the model.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67840/2/10.1177_0193841X9301700303.pd

Combining support vector machines and segmentation algorithms for efficient anomaly detection: a petroleum industry application

Proceedings of: International Joint Conference SOCO’14-CISIS’14-ICEUTE’14, Bilbao, Spain, June 25th–27th, 2014, ProceedingsAnomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.This work was partially funded by CNPq BJT Project 407851/2012-7 and CNPq PVE Project 314017/2013-

Service innovations: A depersonalisation research unit progress report

Depersonalisation was described clinically over 100 years ago, yet there has been little research into this interesting but distressing psychiatric disorder. The symptom of depersonalisation can occur alone or in the context of other psychiatric and neurological illnesses and is characterised by the experience of detachment from one's senses and the outside environment, and may be present for several years without remission. Two years after the establishment of the depersonalisation research unit at the Maudsley Hospital, London, we report on current neurobiological and clinical research findings, including functional magnetic resonance imaging, psychophysiology and neuroendocrinology and progress regarding the development of effective treatments

Can forest management based on natural disturbances maintain ecological resilience?

Given the increasingly global stresses on forests, many ecologists argue that managers must maintain ecological resilience: the capacity of ecosystems to absorb disturbances without undergoing fundamental change. In this review we ask: Can the emerging paradigm of natural-disturbance-based management (NDBM) maintain ecological resilience in managed forests? Applying resilience theory requires careful articulation of the ecosystem state under consideration, the disturbances and stresses that affect the persistence of possible alternative states, and the spatial and temporal scales of management relevance. Implementing NDBM while maintaining resilience means recognizing that (i) biodiversity is important for long-term ecosystem persistence, (ii) natural disturbances play a critical role as a generator of structural and compositional heterogeneity at multiple scales, and (iii) traditional management tends to produce forests more homogeneous than those disturbed naturally and increases the likelihood of unexpected catastrophic change by constraining variation of key environmental processes. NDBM may maintain resilience if silvicultural strategies retain the structures and processes that perpetuate desired states while reducing those that enhance resilience of undesirable states. Such strategies require an understanding of harvesting impacts on slow ecosystem processes, such as seed-bank or nutrient dynamics, which in the long term can lead to ecological surprises by altering the forest's capacity to reorganize after disturbance