Tristemma hirtum

In order to contribute to the fight against infectious diseases, the in vitro antibacterial activity and the antibiotic-potentiating effects of Tristemma hirtum and five other Cameroonian edible plants have been evaluated against Gram-negative multidrug-resistant (MDR) phenotypes. The microdilution method was used to evaluate the bacterial susceptibility of the extracts and their combination to common antibiotics. The phytochemical screening of the extracts was carried out according to standard methods. Phytochemical analysis of the extracts revealed the presence of alkaloids, triterpenes, steroids, and polyphenols, including flavonoids in most of the tested extracts. The entire tested extracts showed moderate (512 μg/mL ≤ MIC ≤ 2048 μg/mL) to weak (MIC > 2048 μg/mL) antibacterial activities against the tested bacteria. Furthermore, extracts of leaf of Tristemma hirtum and pericarps of Raphia hookeri (at their MIC/2 and MIC/4) strongly potentiated the activities of all antibiotics used in the study, especially those of chloramphenicol (CHL), ciprofloxacin (CIP), kanamycin (KAN), and tetracycline (TET) against 70% (7/10) to 100% (10/10) of the tested MDR bacteria, with the modulating factors ranging from 2 to 128. The results of this study suggest that extracts from leaves of Tristemma hirtum and pericarps of Raphia hookeri can be sources of plant-derived products with antibiotic modifying activity

Syzygium jambos

The present study was designed to evaluate the antibacterial activities of methanol extracts of bark and leaves of Syzygium jambos, as well as their synergistic effects with selected antibiotics against drug-resistant Gram-positive and Gram-negative bacteria. The crude extracts were subjected to qualitative phytochemical screening; broth microdilution method was used for antibacterial assays. Phytochemical studies indicate that leaves and bark extracts contained polyphenols, anthraquinones, tannins, and steroids. Extract of the leaves was active against all the 26 strains of Staphylococcus aureus and all the 21 strains of Gram-negative bacteria tested, within the minimum inhibitory concentration (MIC) range of 32–512 μg/mL. The lowest MIC value of 32 μg/mL was obtained with extract of the leaves against Staphylococcus aureus MRSA9 strain. In Gram-negative bacteria, the lowest MIC value of 64 μg/mL was also obtained against Enterobacter aerogenes EA294 and Klebsiella pneumoniae K24 strains. Against S. aureus strains, antibiotic-modulating activity of extracts at MIC/2 towards more than 70% of the tested strains was obtained when leaves and bark extracts were tested in association with chloramphenicol (CHL). This was also the case when leaves extract was combined with CHL, kanamycin (KAN), tetracycline (TET), and erythromycin (ERY) and when bark extract was combined with ciprofloxacin (CIP), TET, and ERY against Gram-negative bacteria. In conclusion, this study demonstrated that Syzygium jambos has antibacterial and antibiotic-modulating activities

Unlocking the power of big data in new product development

This study explores how big data can be used to enable customers to express unrecognised needs. By acquiring this information, managers can gain opportunities to develop customer-centred products. Big data can be defined as multimedia-rich and interactive low-cost information resulting from mass communication. It offers customers a better understanding of new products and provides new, simplified modes of large-scale interaction between customers and firms. Although previous studies have pointed out that firms can better understand customers’ preferences and needs by leveraging different types of available data, the situation is evolving, with increasing application of big data analytics for product development, operations and supply chain management. In order to utilise the customer information available from big data to a larger extent, managers need to identify how to establish a customer-involving environment that encourages customers to share their ideas with managers, contribute their know-how, fiddle around with new products, and express their actual preferences. We investigate a new product development project at an electronics company, STE, and describe how big data is used to connect to, interact with and involve customers in new product development in practice. Our findings reveal that big data can offer customer involvement so as to provide valuable input for developing new products. In this paper, we introduce a customer involvement approach as a new means of coming up with customer-centred new product development

Geometric Resonances in Bose-Einstein Condensates with Two- and Three-Body Interactions

We investigate geometric resonances in Bose-Einstein condensates by solving the underlying time-dependent Gross-Pitaevskii equation for systems with two- and three-body interactions in an axially-symmetric harmonic trap. To this end, we use a recently developed analytical method [Phys. Rev. A 84, 013618 (2011)], based on both a perturbative expansion and a Poincar\'e-Lindstedt analysis of a Gaussian variational approach, as well as a detailed numerical study of a set of ordinary differential equations for variational parameters. By changing the anisotropy of the confining potential, we numerically observe and analytically describe strong nonlinear effects: shifts in the frequencies and mode coupling of collective modes, as well as resonances. Furthermore, we discuss in detail the stability of a Bose-Einstein condensate in the presence of an attractive two-body interaction and a repulsive three-body interaction. In particular, we show that a small repulsive three-body interaction is able to significantly extend the stability region of the condensate.Comment: 27 pages, 13 figure

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

Enacting social transformation through occupation: A narrative literature review

Background: In occupational therapy and occupational science there is a drive to confront social and health injustices through occupation-based practices with social transformation as a goal. However, scholars acknowledge a lack of theory to support this developing area of practice. Aim: To explore how occupations have been used to enact social transformation for disadvantaged communities and to elucidate socially transformative outcomes. Methods: A narrative literature review was carried out, focussing specifically on arts-based occupations, using seven databases. Thirty-eight items were included. Results: Three overarching themes emerged: experiences related to giving voice; levels of change and arts-based occupations influence social change. Conclusions: Art forms as a means of expression can support people to make demands for change. This was true whether the art form was adopted at grass roots level, or via formalized projects run by researchers or Non-Government Organizations. Whilst personal change and small scale social change outcomes were achievable, larger scale structural change was not evident. Unintended outcomes in the form of risks to participants were reported. How and why change came about was not clearly articulated; leaving a need for further exploration of the mechanisms and contexts supporting change in the growing field of social transformation through occupation

Is Weight Loss Associated with Less Progression of Changes in Knee Articular Cartilage among Obese and Overweight Patients as Assessed with MR Imaging over 48 Months? Data from the Osteoarthritis Initiative

Purpose To investigate the association of weight loss with progression of cartilage changes at magnetic resonance (MR) imaging over 48 months in overweight and obese participants compared with participants of stable weight. Materials and Methods The institutional review boards of the four participating centers approved this HIPAA-compliant study. Included were (a) 640 participants (mean age, 62.9 years ± 9.1 [standard deviation]; 398 women) who were overweight or obese (body mass index cutpoints of 25 and 30 kg/m2, respectively) from the Osteoarthritis Initiative, with risk factors for osteoarthritis or mild to moderate radiographic findings of osteoarthritis, categorized into groups with (a) weight loss of more than 10% (n = 82), (b) weight loss of 5%-10% (n = 238), or (c) stable weight (n = 320) over 48 months. Participants were frequency-matched for age, sex, baseline body mass index, and Kellgren-Lawrence score. Two radiologists assessed cartilage and meniscus defects on right knee 3-T MR images at baseline and 48 months by using the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Progression of the subscores was compared between the weight loss groups by using multivariable logistic regression models. Results Over 48 months, adjusted mean increase of cartilage WORMS was significantly smaller in the 5%-10% weight loss group (1.6; 95% confidence interval [CI]: 1.3, 1.9; P = .002) and even smaller in the group with more than 10% weight loss (1.0; 95% CI: 0.6, 1.4; P = .001) when compared with the stable weight group (2.3; 95% CI: 2.0, 2.7). Moreover, percentage of weight change was significantly associated with increase in cartilage WORMS (β = 0.2; 95% CI: 0.02, 0.4; P = .007). Conclusion Participants who lost weight over 48 months showed significantly lower cartilage degeneration, as assessed with MR imaging; rates of progression were lower with greater weight loss. © RSNA, 2017

Characterization of large area APDs for the EXO-200 detector

EXO-200 uses 468 large area avalanche photodiodes (LAAPDs) for detection of scintillation light in an ultra-low-background liquid xenon (LXe) detector. We describe initial measurements of dark noise, gain and response to xenon scintillation light of LAAPDs at temperatures from room temperature to 169K - the temperature of liquid xenon. We also describe the individual characterization of more than 800 LAAPDs for selective installation in the EXO-200 detector.Comment: 10 pages, 17 figure

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature