Structural and functional characterization of three DsbA Paralogues from Salmonella enterica Serovar Typhimurium

In prototypic Escherichia coli K-12 the introduction of disulfide bonds into folding proteins is mediated by the Dsb family of enzymes, primarily through the actions of the highly oxidizing protein EcDsbA. Homologues of the Dsb catalysts are found in most bacteria. Interestingly, pathogens have developed distinct Dsb machineries that play a pivotal role in the biogenesis of virulence factors, hence contributing to their pathogenicity. Salmonella enterica serovar (sv.) Typhimurium encodes an extended number of sulfhydryl oxidases, namely SeDsbA, SeDsbL, and SeSrgA. Here we report a comprehensive analysis of the sv. Typhimurium thiol oxidative system through the structural and functional characterization of the three Salmonella DsbA paralogues. The three proteins share low sequence identity, which results in several unique three-dimensional characteristics, principally in areas involved in substrate binding and disulfide catalysis. Furthermore, the Salmonella DsbA-like proteins also have different redox properties. Whereas functional characterization revealed some degree of redundancy, the properties of SeDsbA, SeDsbL, and SeSrgA and their expression pattern in sv. Typhimurium indicate a diverse role for these enzymes in virulence.

Impact of al passivation and cosputter on the structural property of β-FeSi2 for Al-Doped β-FeSi2/ n -Si(100) based solar cells application

The aluminum (Al) doped polycrystalline p-type β-phase iron disilicide (p-β-FeSi2) is grown by thermal diffusion of Al from Al-passivated n-type Si(100) surface into FeSi2 during crystallization of amorphous FeSi2 to form a p-type β-FeSi 2/n-Si(100) heterostructure solar cell. The structural and photovoltaic properties of p-type β-FeSi2/n-type c-Si structures is then investigated in detail by using X-ray diffraction, Raman spectroscopy, transmission electron microscopy analysis, and electrical characterization. The results are compared with Al-doped p-β-FeSi2 prepared by using cosputtering of Al and FeSi2 layers on Al-passivated n-Si(100) substrates. A significant improvement in the maximum open-circuit voltage (Voc) from 120 to 320 mV is achieved upon the introduction of Al doping through cosputtering of Al and amorphous FeSi2 layer. The improvement in Voc is attributed to better structural quality of Al-doped FeSi2 film through Al doping and to the formation of high quality crystalline interface between Al-doped β-FeSi2 and n-type c-Si. The effects of Al-out diffusion on the performance of heterostructure solar cells have been investigated and discussed in detail.

Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision.

The capture and retention evaluation of a stormwater gross pollutant trap design

Gross pollutant traps (GPT) are designed to capture and retain visible street waste, such as anthropogenic litter and organic matter. Blocked screens, low/high downstream tidal waters and flows operating above/below the intended design limits can hamper the operations of a stormwater GPT. Under these adverse operational conditions, a recently developed GPT was evaluated. Capture and retention experiments were conducted on a 50% scale model with partially and fully blocked screens, placed inside a hydraulic flume. Flows were established through the model via an upstream channel-inlet configuration. Floatable, partially buoyant, neutrally buoyant and sinkable spheres were released into the GPT and monitored at the outlet. These experiments were repeated with a pipe-inlet configured GPT. The key findings from the experiments were of practical significance to the design, operation and maintenance of GPTs. These involved an optimum range of screen blockages and a potentially improved inlet design for efficient gross pollutant capture/retention operations. For example, the outlet data showed that the capture and retention efficiency deteriorated rapidly when the screens were fully blocked. The low pressure drop across the retaining screens and the reduced inlet flow velocities were either insufficient to mobilise the gross pollutants, or the GPT became congested.

Maternal concern about her toddler undereating and becoming underweight and use of feeding practices - analysis of the NOURISH and SAIDI cohorts using structural equation modeling

The chubby baby who eats well is desirable in our culture. Perceived low weight gains and feeding concerns are common reasons mothers seek advice in the early years. In contrast, childhood obesity is a global public health concern. Use of coercive feeding practices, prompted by maternal concern about weight, may disrupt a child’s innate self regulation of energy intake, promoting overeating and overweight. This study describes predictors of maternal concern about her child undereating/becoming underweight and feeding practices. Mothers in the control group of the NOURISH and South Australian Infants Dietary Intake studies (n = 332) completed a self-administered questionnaire when the child was aged 12–16 months. Weight-for-age z-score (WAZ)was derived from weight measured by study staff. Mean age (SD) was 13.8 (1.3) months, mean WAZ (SD), 0.58 (0.86) and 49% were male. WAZ and two questions describing food refusal were combined in a structural equation model with four items from the Infant feeding Questionnaire (IFQ) to form the factor ‘Concern about undereating/weight’. Structural relationships were drawn between concern and IFQ factors ‘awareness of infant’s hunger and satiety cues’, ‘use of food to calm infant’s fussiness’ and ‘feeding infant on a schedule’, resulting in a model of acceptable fit. Lower WAZ and higher frequency of food refusal predicted higher maternal concern. Higher maternal concern was associated with lower awareness of infant cues (r = −.17, p = .01) and greater use of food to calm (r = .13, p = .03). In a cohort of healthy children, maternal concern about undereating and underweight was associated with practices that have the potential to disrupt self-regulation.

How innovation and R&D happen in the upstream oil & gas industry : insights from a global survey

Few would disagree that the upstream oil & gas industry has become more technology-intensive over the years. But how does innovation happen in the industry? Specifically, what ideas and inputs flow from which parts of the sector׳s value network, and where do these inputs go? And how do firms and organizations from different countries contribute differently to this process? This paper puts forward the results of a survey designed to shed light on these questions. Carried out in collaboration with the Society of Petroleum Engineers (SPE), the survey was sent to 469 executives and senior managers who played a significant role with regard to R&D and/or technology deployment in their respective business units. A total of 199 responses were received from a broad range of organizations and countries around the world. Several interesting themes and trends emerge from the results, including: (1) service companies tend to file considerably more patents per innovation than other types of organization; (2) over 63% of the deployed innovations reported in the survey originated in service companies; (3) neither universities nor government-led research organizations were considered to be valuable sources of new information and knowledge in the industry׳s R&D initiatives, and; (4) despite the increasing degree of globalization in the marketplace, the USA still plays an extremely dominant role in the industry׳s overall R&D and technology deployment activities. By providing a detailed and objective snapshot of how innovation happens in the upstream oil & gas sector, this paper provides a valuable foundation for future investigations and discussions aimed at improving how R&D and technology deployment are managed within the industry. The methodology did result in a coverage bias within the survey, however, and the limitations arising from this are explored.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

A critical review of structural equation modeling applications in construction research

Structural equation modeling (SEM) is a versatile multivariate statistical technique, and applications have been increasing since its introduction in the 1980s. This paper provides a critical review of 84 articles involving the use of SEM to address construction related problems over the period 1998–2012 including, but not limited to, seven top construction research journals. After conducting a yearly publication trend analysis, it is found that SEM applications have been accelerating over time. However, there are inconsistencies in the various recorded applications and several recurring problems exist. The important issues that need to be considered are examined in research design, model development and model evaluation and are discussed in detail with reference to current applications. A particularly important issue concerns the construct validity. Relevant topics for efficient research design also include longitudinal or cross-sectional studies, mediation and moderation effects, sample size issues and software selection. A guideline framework is provided to help future researchers in construction SEM applications.

Combustion analysis of microalgae methyl ester in a common rail direct injection diesel engine

In this study, the biodiesel properties and effects of blends of oil methyl ester petroleum diesel on a CI direct injection diesel engine is investigated. Blends were obtained from the marine dinoflagellate Crypthecodinium cohnii and waste cooking oil. The experiment was conducted using a four-cylinder, turbo-charged common rail direct injection diesel engine at four loads (25%, 50%, 75% and 100%). Three blends (10%, 20% and 50%) of microalgae oil methyl ester and a 20% blend of waste cooking oil methyl ester were compared to petroleum diesel. To establish suitability of the fuels for a CI engine, the effects of the three microalgae fuel blends at different engine loads were assessed by measuring engine performance, i.e. mean effective pressure (IMEP), brake mean effective pressure (BMEP), in cylinder pressure, maximum pressure rise rate, brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), heat release rate and gaseous emissions (NO, NOx,and unburned hydrocarbons (UHC)). Results were then compared to engine performance characteristics for operation with a 20% waste cooking oil/petroleum diesel blend and petroleum diesel. In addition, physical and chemical properties of the fuels were measured. Use of microalgae methyl ester reduced the instantaneous cylinder pressure and engine output torque, when compared to that of petroleum diesel, by a maximum of 4.5% at 50% blend at full throttle. The lower calorific value of the microalgae oil methyl ester blends increased the BSFC, which ultimately reduced the BTE by up to 4% at higher loads. Minor reductions of IMEP and BMEP were recorded for both the microalgae and the waste cooking oil methyl ester blends at low loads, with a maximum of 7% reduction at 75% load compared to petroleum diesel. Furthermore, compared to petroleum diesel, gaseous emissions of NO and NOx, increased for operations with biodiesel blends. At full load, NO and NOx emissions increased by 22% when 50% microalgae blends were used. Petroleum diesel and a 20% blend of waste cooking oil methyl ester had emissions of UHC that were similar, but those of microalgae oil methyl ester/petroleum diesel blends were reduced by at least 50% for all blends and engine conditions. The tested microalgae methyl esters contain some long-chain, polyunsaturated fatty acid methyl esters (FAMEs) (C22:5 and C22:6) not commonly found in terrestrial-crop-derived biodiesels yet all fuel properties were satisfied or were very close to the ASTM 6751-12 and EN14214 standards. Therefore, Crypthecodinium cohnii- derived microalgae biodiesel/petroleum blends of up to 50% are projected to meet all fuel property standards and, engine performance and emission results from this study clearly show its suitability for regular use in diesel engines.

Retinal anatomy of the New Zealand kiwi: Structural traits consistent with their nocturnal behavior

Kiwi (Apteryx spp.) have a visual system unlike that of other nocturnal birds, and have specializations to their auditory, olfactory and tactile systems. Eye size, binocular visual fields and visual brain centers in kiwi are proportionally the smallest yet recorded among birds. Given the many unique features of the kiwi visual system, we examined the laminar organization of the kiwi retina to determine if they evolved increased light sensitivity with a shift to a nocturnal niche or if they retained features of their diurnal ancestor. The laminar organization of the kiwi retina was consistent with an ability to detect low light levels similar to that of other nocturnal species. In particular, the retina appeared to have a high proportion of rod photoreceptors compared to diurnal species, as evidenced by a thick outer nuclear layer, and also numerous thin photoreceptor segments intercalated among the conical shaped cone photoreceptor inner segments. Therefore, the retinal structure of kiwi was consistent with increased light sensitivity, although other features of the visual system, such as eye size, suggest a reduced reliance on vision. The unique combination of a nocturnal retina and smaller than expected eye size, binocular visual fields and brain regions make the kiwi visual system unlike that of any bird examined to date. Whether these features of their visual system are an evolutionary design that meets their specific visual needs or are a remnant of a kiwi ancestor that relied more heavily on vision is yet to be determined.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.