The antigen 43 structure reveals a molecular velcro-like mechanism of autotransporter-mediated bacterial clumping

Aggregation and biofilm formation are critical mechanisms for bacterial resistance to host immune factors and antibiotics. Autotransporter (AT) proteins, which represent the largest group of outer-membrane and secreted proteins in Gram-negative bacteria, contribute significantly to these phenotypes. Despite their abundance and role in bacterial pathogenesis, most AT proteins have not been structurally characterized, and there is a paucity of detailed information with regard to their mode of action. Here we report the structure–function relationships of Antigen 43 (Ag43a), a prototypic self-associating AT protein from uropathogenic Escherichia coli. The functional domain of Ag43a displays a twisted L-shaped β-helical structure firmly stabilized by a 3D hydrogen-bonded scaffold. Notably, the distinctive Ag43a L shape facilitates self-association and cell aggregation. Combining all our data, we define a molecular “Velcro-like” mechanism of AT-mediated bacterial clumping, which can be tailored to fit different bacterial lifestyles such as the formation of biofilms.

Stem-like cells with luminal progenitor phenotype survive castration in human prostate cancer

Castration is the standard therapy for advanced prostate cancer (PC). Although this treatment is initially effective, tumors invariably relapse as incurable, castration-resistant PC (CRPC). Adaptation of androgen-dependent PC cells to an androgen-depleted environment or selection of pre-existing,CRPC cells have been proposed as mechanisms of CRPC development. Stem cell (SC)-like PC cells have been implicated not only as tumor initiating/maintaining in PC but also as tumor-reinitiating cells in CRPC. Recently, castration-resistant cells expressing the NK3 homeobox 1 (Nkx3-1) (CARNs), the other luminal markers cytokeratin 18 (CK18) and androgen receptor (AR), and possessing SC properties, have been found in castrated mouse prostate and proposed as the cell-of-origin of CRPC. However, the human counterpart of CARNs has not been identified yet. Here, we demonstrate that in the human PC xenograft BM18, preexisting SC-like and neuroendocrine (NE) PC cells are selected by castration and survive as totally quiescent. SClike BM18 cells, displaying the SC markers aldehyde dehydrogenase 1A1 or NANOG, coexpress the luminal markers NKX3-1, CK18, and a low level of AR (ARlow) but not basal or NE markers. These CR luminal SC-like cells, but not NE cells, reinitiate BM18 tumor growth after androgen replacement. The ARlow seems to mediate directly both castration survival and tumor reinitiation. This study identifies for the first time in human PC SC-/CARN-like cells that may represent the cell-of-origin of tumor reinitiation as CRPC. This finding will be fundamental for refining the hierarchy among human PC cancer cells and may have important clinical implications.

A biomarker panel to discriminate between systemic inflammatory response syndrome and sepsis and sepsis severity

Introduction: In this study, we report on initial efforts to discover putative biomarkers for differential diagnosis of a systemic inflammatory response syndrome (SIRS) versus sepsis; and different stages of sepsis. In addition, we also investigated whether there are proteins that can discriminate between patients who survived sepsis from those who did not. Materials and Methods: Our study group consisted of 16 patients, of which 6 died and 10 survived. We daily measured 28 plasma proteins, for the whole stay of the patients in the ICU. Results: We observed that metalloproteinases and sE-selectin play a role in the distinction between SIRS and sepsis, and that IL-1, IP-10, sTNF-R2 and sFas appear to be indicative for the progression from sepsis to septic shock. A combined measurement of MMP-3, -10, IL-1, IP-10, sIL-2R, sFas, sTNF-R1, sRAGE, GM-CSF, IL-1 and Eotaxin allows for a good separation of patients that survived from those that died (mortality prediction with a sensitivity of 79% and specificity of 86%). Correlation analysis suggests a novel interaction between IL-1a and IP-10. Conclusion: The marker panel is ready to be verified in a validation study with or without therapeutic intervention.

Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy

We present an approach for the inspection of vertical pole-like infrastructure using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures, such as light and power distribution poles, is a time consuming, dangerous and expensive task with high operator workload. To address these issues, we propose a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. We adopt an Image based Visual Servoing (IBVS) technique using only two line features to stabilise the vehicle with respect to a pole. Visual, inertial and sonar data are used, making the approach suitable for indoor or GPS-denied environments. Results from simulation and outdoor flight experiments demonstrate the system is able to successfully inspect and circumnavigate a pole.

Data as an asset : what the upstream oil & gas industry can learn about ‘big data’ from companies like Facebook

The upstream oil & gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data”—that is, the ability to apply more sophisticated types of analytical tools to information in a way that extracts new insights or creates new forms of value—is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil & gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This paper examines existing data management practices in the upstream oil & gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the Big Data revolution. The comparison shows that, in companies that are leading the Big Data revolution, data is regarded as a valuable asset. The presented evidence also shows, however, that this is usually not true within the oil & gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how upstream oil & gas companies could potentially extract more value from data, and concludes with a series of specific technical and management-related recommendations to this end.

Moderate physical activity level as a protective factor against metabolic syndrome in middle-aged and older women

Aims and objectives To investigate whether physical activity is a protective factor against metabolic syndrome in middle-aged and older women. Background Socio-demographic and lifestyle behaviour factors contribute to metabolic syndrome. To minimise the risk of metabolic syndrome, several global guidelines recommend increasing physical activity level. However, only limited research has investigated the relationship between physical activity levels and metabolic syndrome in middle-aged and older women after adjusting for socio-demographic and lifestyle behaviour factors. Design Cross-sectional design. Methods A convenience sample of 326 middle-aged and older women was recruited. Metabolic syndrome was confirmed according to the National Cholesterol Education Program, Adult Treatment Panel III guidelines, and physical activity levels were measured by the International Physical Activity Questionnaire. Results The sample had a mean age of 60•9 years, and the prevalence of metabolic syndrome was 43•3%. Postmenopausal women and women with low socioeconomic status (low-education background, without personal income and currently unemployed) had a significantly higher risk of developing metabolic syndrome. After adjusting for significant socio-demographic and lifestyle behaviour factors, the women with moderate or high physical activity levels had a significantly lower (OR = 0•10; OR = 0•11, p < 0•001) risk of metabolic syndrome and a lower risk for each specific component of metabolic syndrome, including elevated fasting plasma glucose (OR = 0•29; OR = 0•26, p = 0•009), elevated blood pressure (OR = 0•18; OR = 0•32, p = 0•029), elevated triglycerides (OR = 0•41; OR = 0•15, p = 0•001), reduced high-density lipoprotein (OR = 0•28; OR = 0•27, p = 0•004) and central obesity (OR = 0•31; OR = 0•22, p = 0•027). Conclusions After adjusting for socio-demographic and lifestyle behaviour factors, physical activity level was a significant protective factor against metabolic syndrome in middle-aged and older women. Higher physical activity levels (moderate or high physical activity level) reduced the risk of metabolic syndrome in middle-aged and older women. Relevance to clinical practice Appropriate strategies should be developed to encourage middle-aged and older women across different socio-demographic backgrounds to engage in moderate or high levels of physical activity to reduce the risk of metabolic syndrome.

Method of symptom assessment influences cognitive, affective and somatic post-concussion-like symptom base rates

Primary objective: To investigate whether assessment method influences the type of post-concussion-like symptoms. Methods and procedures: Participants were 73 Australian undergraduate students (Mage = 24.14, SD = 8.84; 75.3% female) with no history of mild traumatic brain injury (mTBI). Participants reported symptoms experienced over the previous 2 weeks in response to an open-ended question (free report), mock interview and standardized checklist (British Columbia Post-concussion Symptom Inventory; BC-PSI). Main outcomes and results: In the free report and checklist conditions, cognitive symptoms were reported significantly less frequently than affective (free report: p < 0.001; checklist: p < 0.001) or somatic symptoms (free report: p < 0.001; checklist: p = 0.004). However, in the mock structured interview condition, cognitive and somatic symptoms were reported significantly less frequently than affective symptoms (both p < 0.001). No participants reported at least one symptom from all three domains when assessed by free report, whereas most participants did so when symptoms were assessed by a mock structured interview (75%) or checklist (90%). Conclusions: Previous studies have shown that the method used to assess symptoms affects the number reported. This study shows that the assessment method also affects the type of reported symptoms.

The role of resilience and mTBI as predictors of postconcussional disorder symptomatology

Objective Resilience is 1 of several factors that are thought to contribute to outcome following mild traumatic brain injury (mTBI). This study explored the predictors of the postconcussional syndrome (PCS) symptoms that can occur following mTBI. We hypothesized that a reported recent mTBI and lower psychological resilience would predict worse reported PCS symptomatology. Method 233 participants completed the Neurobehavioral Symptom Inventory (NSI) and the Brief Resilience Scale (BRS). Three NSI scores were used to define PCS symptomatology. A total of 35 participants reported an mTBI (as operationally defined by the World Health Organization) that was sustained between 1 and 6 months prior to their participation (positive mTBI history); the remainder reported having never had an mTBI. Results Regression analyses revealed that a positive reported recent mTBI history and lower psychological resilience were significant independent predictors of reported PCS symptomatology. These results were found for the 3 PCS scores from the NSI, including using a stringent caseness criterion, p < .05. Demographic variables (age and gender) were not related to outcome, with the exception of education in some analyses. Conclusion The results demonstrate that: (a) both perceived psychological resilience and mTBI history play a role in whether or not PCS symptoms are experienced, even when demographic variables are considered, and; (b) of these 2 variables, lower perceived psychological resilience was the strongest predictor of PCS-like symptomatology.

Patient perspectives of using a web based intervention program for patients with type 2 diabetes and acute coronary syndrome

Introduction Research highlights patients with dual diagnoses of type 2 diabetes and acute coronary syndrome (ACS) have higher readmission rates and poorer health outcomes than patients with singular chronic conditions. Despite this, there is a lack of education programs targeted for this dual diagnosis population to improve self-management and decrease negative health outcomes. There is evidence to suggest that internet based interventions may improve health outcomes for patients with singular chronic conditions, however there is a need to develop an evidence base for ACS patients with comorbid diabetes. There is a growing awareness of the importance of a participatory model in developing effective online interventions. That is, internet interventions are more effective if end users’ perceptions of the intervention are incorporated in their final development prior to testing in large scale trials. Objectives This study investigated patients’ perspectives of the web-based intervention designed to promote self-management of the dual conditions in order to refine the intervention prior to clinical trial evaluation. Methods An interpretive approach with thematic analysis was used to obtain deeper understanding regarding participants’ experience when using web-application interventions for patients with ACS and type 2 diabetes. Semi-structured interviews were undertaken on a purposive sample of 30 patients meeting strict inclusion and exclusion criteria to obtain their perspectives on the program. Results Preliminary results indicate patients with dual diagnoses express more complex needs than those with a singular condition. Participants express a positive experience with the proposed internet intervention and emerging themes include that the web page is seen as easy to use and comforting as a support, in that patients know they are not alone. Further results will be reported as they become available. Conclusion The results indicate potential for patient acceptability of the newly developed internet intervention for patients with ACS and comorbid diabetes. Incorporation of patient perspectives into the final development of the intervention is likely to maximise successful outcomes of any future trials that utilise this intervention. Future quantitative evaluation of the effectiveness of the intervention is being planned.

Spectrophotometric analysis of phenols, which involves a hemin-graphene hybrid nanoparticles with peroxidase-like activity

Phenols are well known noxious compounds, which are often found in various water sources. A novel analytical method has been researched and developed based on the properties of hemin–graphene hybrid nanosheets (H–GNs). These nanosheets were synthesized using a wet-chemical method, and they have peroxidase-like activity. Also, in the presence of H2O2, the nanosheets are efficient catalysts for the oxidation of the substrate, 4-aminoantipine (4-AP), and the phenols. The products of such an oxidation reaction are the colored quinone-imines (benzodiazepines). Importantly, these products enabled the differentiation of the three common phenols – pyrocatechol, resorcin and hydroquinone, with the use of a novel, spectroscopic method, which was developed for the simultaneous determination of the above three analytes. This spectroscopic method produced linear calibrations for the pyrocatechol (0.4–4.0 mg L−1), resorcin (0.2–2.0 mg L−1) and hydroquinone (0.8–8.0 mg L−1) analytes. In addition, kinetic and spectral data, obtained from the formation of the colored benzodiazepines, were used to establish multi-variate calibrations for the prediction of the three phenol analytes found in various kinds of water; partial least squares (PLS), principal component regression (PCR) and artificial neural network (ANN) models were used and the PLS model performed best.