Psychological predictors of opportunistic snacking in the absence of hunger

Increased frequency of eating in the absence of homeostatic need, notably through snacking, is an important contributor to overconsumption and may be facilitated by increased availability of palatable food in the obesogenic environment. Opportunistic initiation of snacking is likely to be subject to individual differences, although these are infrequently studied in laboratory-based research paradigms. This study examined psychological factors associated with opportunistic initiation of snacking, and predictors of intake in the absence of homeostatic need. Fifty adults (mean age 34.5 years, mean BMI 23.9 kg/m2, 56% female) participated in a snack taste test in which they ate a chocolate snack to satiation, after which they were offered an unanticipated opportunity to initiate a second eating episode. Trait and behavioural measures of self control, sensitivity to reward, dietary restraint and disinhibited eating were taken. Results showed that, contrary to expectations, those who initiated snacking were better at inhibitory control compared with those who did not initiate. However, amongst participants who initiated snacking, intake (kcal) was predicted by higher food reward sensitivity, impulsivity and BMI. These findings suggest that snacking initiation in the absence of hunger is an important contributor to overconsumption. Consideration of the individual differences promoting initiation of eating may aid in reducing elevated eating frequency in at-risk individuals.

A multi-criteria analysis approach for ranking and selection of microorganisms for the production of oils for biodiesel production

Oleaginous microorganisms have potential to be used to produce oils as alternative feedstock for biodiesel production. Microalgae (Chlorella protothecoides and Chlorella zofingiensis), yeasts (Cryptococcus albidus and Rhodotorula mucilaginosa), and fungi (Aspergillus oryzae and Mucor plumbeus) were investigated for their ability to produce oil from glucose, xylose and glycerol. Multi-criteria analysis (MCA) using analytic hierarchy process (AHP) and preference ranking organization method for the enrichment of evaluations (PROMETHEE) with graphical analysis for interactive aid (GAIA), was used to rank and select the preferred microorganisms for oil production for biodiesel application. This was based on a number of criteria viz., oil concentration, content, production rate and yield, substrate consumption rate, fatty acids composition, biomass harvesting and nutrient costs. PROMETHEE selected A. oryzae, M. plumbeus and R. mucilaginosa as the most prospective species for oil production. However, further analysis by GAIA Webs identified A. oryzae and M. plumbeus as the best performing microorganisms.

Load estimation with uncertainties from opportunistic sampling data: A semiparametric approach

We consider estimating the total load from frequent flow data but less frequent concentration data. There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates that minimizes the biases and makes use of informative predictive variables. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized rating-curve approach with additional predictors that capture unique features in the flow data, such as the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and the discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. Forming this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach for two rivers delivering to the Great Barrier Reef, Queensland, Australia. One is a data set from the Burdekin River, and consists of the total suspended sediment (TSS) and nitrogen oxide (NO(x)) and gauged flow for 1997. The other dataset is from the Tully River, for the period of July 2000 to June 2008. For NO(x) Burdekin, the new estimates are very similar to the ratio estimates even when there is no relationship between the concentration and the flow. However, for the Tully dataset, by incorporating the additional predictive variables namely the discounted flow and flow phases (rising or recessing), we substantially improved the model fit, and thus the certainty with which the load is estimated.

Phyllosphere of Cotton as a Habitat for Diazotrophic Microorganisms

Positive nitrogenase activities ranging from 0.18 to 0.78 nmol of C2H4 cm−2 h−1 were detected on the leaf surfaces of different varieties of cotton (Gossypium hirsutum L. and G. herbaceum L.) plants. Beijerinckia sp. was observed to be the predominant nitrogen-fixing microorganism in the phyllosphere of these varieties. A higher level of phyllosphere nitrogen-fixing activity was recorded in the variety Varalaxmi despite a low C/N ratio in the leaf leachates. Leaf surfaces of the above variety possessed the largest number of hairy outgrowths (trichomes) which entrapped a majority of microbes. Immersion of plant roots in nutrient medium containing 32Pi led to the accumulation of label in the trichome-borne microorganisms, thereby indicating a possible transfer of nutrients from leaf to microbes via trichomes. Extrapolation of acetylene reduction values suggested that 1.6 to 3.2 kg of N ha−1 might be contributed by diazotrophs in the phyllosphere of the variety Varalaxmi during the entire growth period.

Efficient surface modification of biomaterial to prevent biofilm formation and the attachment of microorganisms

Biomaterials play a fundamental role in disease management and the improvement of health care. In recent years, there has been a significant growth in the diversity, function, and number of biomaterials used worldwide. Yet, attachment of pathogenic microorganisms onto biomaterial surfaces remains a significant challenge that substantially undermines their clinical applicability, limiting the advancement of these systems. The emergence and escalating pervasiveness of antibiotic-resistant bacterial strains makes the management of biomaterial-associated nosocomial infections increasingly difficult. The conventional post-operative treatment of implant-caused infections using systemic antibiotics is often marginally effective, further accelerating the extent of antimicrobial resistance. Methods by which the initial stages of bacterial attachment and biofilm formation can be restricted or prevented are therefore sought. The surface modification of biomaterials has the potential to alleviate pathogenic biofouling, therefore preventing the need for conventional antibiotics to be applied.

Detection of microorganisms using biosensors-a smarter way towards detection techniques.

Along with useful microorganisms, there are some that cause potential damage to the animals and plants. Detection and identification of these harmful organisms in a cost and time effective way is a challenge for the researchers. The future of detection methods for microorganisms shall be guided by biosensor, which has already contributed enormously in sensing and detection technology. Here, we aim to review the use of various biosensors, developed by integrating the biological and physicochemical/mechanical properties (of tranducers), which can have enormous implication in healthcare, food, agriculture and biodefence. We have also highlighted the ways to improve the functioning of the biosensor.

Do SMEs cluster around innovation activities? Discovering active, incremental and opportunistic innovators

Do SMEs cluster around different types of innovation activities? Are there patterns of SME innovation activities? To investigate we develop a taxonomy of innovation activities in SMEs using a qualitative study, followed by a survey. First, based upon our qualitative research and literature review we develop a comprehensive list of innovation activities SMEs typically engage in. We then conduct a factor analysis to determine if these activities can be combined into factors. We identify three innovation activity factors: R&D activities, incremental innovation activities and cost innovation activities. We use these factors to identify three clusters of firms engaging in similar innovation activities: active innovators, incremental innovators and opportunistic innovators. The clusters are enriched by validating that they also exhibit significant internal similarities and external differences in their innovation skills, demographics, industry segments and family business ownership. This research contributes to innovation and SME theory and practice by identifying SME clusters based upon their innovation activities.

Biosynthesis of coenzyme Q in microorganisms

Incorporation of mevalonate-2-C14, acetate-1-C14, and formate-C14 into the lipids of microorganisms was studied. In the case of four bacteria tested—Agrobacterium tumefaciens, Azotobacter vinelandii, Escherichia coli, and a Pseudomonas species—the various homologues of coenzyme Q present were not labeled with any of the tracers used, although significant amounts of radioactivity were present in the lipids. Both acetate and mevalonate were incorporated into coenzyme Q and sterol of the moulds, Aspergillus niger, Neurospora crassa, Penicillium chrysogenum, and Gibberella fujickuroi, and a yeast, Torulopsis utilis. Mevalonate was incorporated into the side chain but not the ring, whereas acetate was incorporated into both. It appears that the mevalonate pathway for the synthesis of coenzyme Q is operative only in those organisms which also contain other isoprene compounds such as sterol and carotene.

OPSM - Opportunistic Power Save mode for Infrastructure IEEE 802.11 WLAN

We focus on the energy spent in radio communication by the stations (STAs) in an IEEE 802.11 infrastructure WLAN. All the STAs are engaged in web browsing, which is characterized by a short file downloads over TCP, with short duration of inactivity or think time in between two file downloads. Under this traffic, Static PSM (SPSM) performs better than CAM, since the STAs in SPSM can switch to low power state (sleep) during think times while in CAM they have to be in the active state all the time. In spite of this gain, performance of SPSM degrades due to congestion, as the number of STAs associated with the access point (AP) increases. To address this problem, we propose an algorithm, which we call opportunistic PSM (OPSM). We show through simulations that OPSM performs better than SPSM under the aforementioned TCP traffic. The performance gain achieved by OPSM over SPSM increases as the mean file size requested by the STAs or the number of STAs associated with the AP increases. We implemented OPSM in NS-2.33, and to compare the performance of OPSM and SPSM, we evaluate the number of file downloads that can be completed with a given battery capacity and the average time taken to download a file.

Providing quality of service via opportunistic splitting using stochastic approximation

We consider the problem of wireless channel allocation to multiple users. A slot is given to a user with a highest metric (e.g., channel gain) in that slot. The scheduler may not know the channel states of all the users at the beginning of each slot. In this scenario opportunistic splitting is an attractive solution. However this algorithm requires that the metrics of different users form independent, identically distributed (iid) sequences with same distribution and that their distribution and number be known to the scheduler. This limits the usefulness of opportunistic splitting. In this paper we develop a parametric version of this algorithm. The optimal parameters of the algorithm are learnt online through a stochastic approximation scheme. Our algorithm does not require the metrics of different users to have the same distribution. The statistics of these metrics and the number of users can be unknown and also vary with time. Each metric sequence can be Markov. We prove the convergence of the algorithm and show its utility by scheduling the channel to maximize its throughput while satisfying some fairness and/or quality of service constraints.

Opportunistic splitting for scheduling via stochastic approximation

We consider the problem of scheduling a wireless channel among multiple users. A slot is given to a user with a highest metric (e.g., channel gain) in that slot. The scheduler may not know the channel states of all the users at the beginning of each slot. In this scenario opportunistic splitting is an attractive solution. However this algorithm requires that the metrics of different users form independent, identically distributed (iid) sequences with same distribution and that their distribution and number be known to the scheduler. This limits the usefulness of opportunistic splitting. In this paper we develop a parametric version of this algorithm. The optimal parameters of the algorithm are learnt online through a stochastic approximation scheme. Our algorithm does not require the metrics of different users to have the same distribution. The statistics of these metrics and the number of users can be unknown and also vary with time. We prove the convergence of the algorithm and show its utility by scheduling the channel to maximize its throughput while satisfying some fairness and/or quality of service constraints.

Electrochemical aspects of leaching of ocean nodules in the presence and absence of microorganisms

instead of using chemical-reducing agents to facilitate the reduction and dissolution of manganese and iron oxide in the ocean nodule, electrochemical reduction based on two approaches, namely, cathodic polarization and galvanic interaction, can also be considered as attractive alternatives. Galvanic leaching of ocean nodules in the presence of pyrite and pyrolusite for complete recovery of Cu, Ni and Co has been discussed. The key for successful and efficient dissolution of copper, nickel and cobalt from ocean nodules depends on prior reduction of the manganese and ferric oxides with which the above valuable nonferrous metals are interlocked. Polarization studies using a slurry electrode system indicated that maximum dissolution of iron and manganese due to electrochemical reduction occurred at negative DC potentials of -600 mV (SCE) and -1400 mV (SCE). The present work is also relevant to galvanic bioleaching of ocean nodules using autotrophic microorganisms, such as Thiobacillus ferrooxidans and T thiooxidans, which resulted in significant dissolution of copper, nickel and cobalt at the expense of microbiologically generated acids. Various electrochemical and biochemical mechanisms are outlined and the electroleaching and galvanic processes so developed are shown to yield almost complete dissolution of all metal values. (C) 2002 Elsevier Science B.V. All rights reserved.

Opportunistic scheduling of wireless links

We consider the problem of scheduling of a wireless channel (server) to several queues. Each queue has its own link (transmission) rate. The link rate of a queue can vary randomly from slot to slot. The queue lengths and channel states of all users are known at the beginning of each slot. We show the existence of an optimal policy that minimizes the long term (discounted) average sum of queue lengths. The optimal policy, in general needs to be computed numerically. Then we identify a greedy (one step optimal) policy, MAX-TRANS which is easy to implement and does not require the channel and traffic statistics. The cost of this policy is close to optimal and better than other well-known policies (when stable) although it is not throughput optimal for asymmetric systems. We (approximately) identify its stability region and obtain approximations for its mean queue lengths and mean delays. We also modify this policy to make it throughput optimal while retaining good performance.