Quality evaluation of commercially available instant mango drinks powder in local market of Bangladesh

The upward trend of consumption of processed food must not dim the demand of taking healthy and safe food among population. Thus, six popular commercial brands of instant mango drinks powder of Bangladesh were targeted to investigate some quality parameters (proximate compositions, mineral contents and bioactive compounds). Mineral contents and bioactive compounds of instant mango drinks powder were determined by using biochemical analyzer and UV-visible spectrophotometer, respectively. Results of proximate analysis showed that moisture content, ash content, fiber content, and carbohydrate content of different brands of instant mango drinks powder ranged from 0.21 to 0.25%, 0.45 to 0.55%, 0.10 to 0.40%, and 98.83 to 99.21%, respectively, whereas energy value ranged from 395.32 to 396.84 Kcal/100g. Sodium, potassium, calcium, chloride, phosphorus, iron and vitamin-C were also determined, which showed the significant different (p<0.05) values among different brands. Total anthocyanin content (TAC), Total flavonoid content (TFC), Total phenolic content (TPC), Antioxidant capacity were determined as bioactive compounds. Results of bioactive compounds analysis also showed that the samples were significantly different (p<0.05). Although, the quality varied from brand to brand, but all the samples could be good source of vitamin-C, carbohydrate and energy. Furthermore, health concerning issues can be improved by focusing the bioactive compounds of commercially available instant drinks powder. Int. J. Agril. Res. Innov. Tech. 10(2): 54-58, December 202

Privacy preserving recommender systems

The recommender systems help users find suitable and interesting products and contents from the huge amount of information that are available in the internet. There are various types of recommender systems available which have been providing recommendation services to users. For example Collaborative Filtering (CF) based recommendations, Content based (CB) recommendations, context aware recommendations and so on. Despite the fact that these recommender systems are very useful to solve the information overload problem by filtering interesting information, they suffer from huge privacy issues. In order to generate user personalized recommendations, the recommendation service providers need to acquire the information related to attributes, preferences, experiences as well as demands, which are related to users' confidential information. Usually the more information available to the service providers, the more accurate recommendations can be generated. However, the service providers are not always trustworthy to share personal information for recommendation purposes since they may cause serious privacy threats to users' privacy by leaking them to other parties or providing false recommendations. Therefore the user information must be protected prior to share them to any third party service provider to ensure the privacy of users. To overcome the privacy issues of recommender systems several techniques have been proposed which can be categorized into decentralization, randomization and secure computations based approaches. In decentralization based approach, the central service providers are removed and the main controls of recommendation services are given to participant users. The main issue with this kind of approach is that to generate recommendations, the users need to be dependant to other users' availability in online services. If any user becomes offline, her information can not be used in the system. The randomization based techniques add noises to users data to obfuscate them from learning the true information. However the main issue is that adding noise affects recommendation accuracy. On the contrary, the secure computations preserve user information while providing accurate recommendations. In this thesis we preserve user privacy by means of encrypting user information, specifically their ratings and other related information using homomorphic encryption based techniques to provide recommendations based on the encrypted data. The main advantage of homomorphic encryption based technique is that it is semantically secure and computationally it is hard to distinguish the true information from the given ciphertext. Using the homomorphic based encryption tools and techniques we build different privacy preserving protocols for different types of recommendation approaches by analyzing their privacy requirements and challenges. More specifically, we focus on different key recommendation techniques and differentiate them into centralized and partitioned dataset based recommendation techniques. From available recommendation techniques, we found that some of the existing and popular recommendation techniques like user based recommendation, item based recommendation and context aware recommendation can be grouped into centralized recommendation approach. In partitioned dataset based recommendation, the user information can be partitioned into different organizations and these organizations can collaborate with each other by gathering sufficient information in order to provide accurate recommendations without revealing their own confidential information. After categorizing the recommendation techniques we analyze the problems and requirements in terms of privacy preservation. Then for each type of recommendation approach, we develop the privacy preserving protocols to generate recommendations taking their specific privacy requirements and challenges into consideration. We also investigate the problems and limitations of existing privacy preserving recommendations and found that the current solutions suffer from huge computation and communication overhead as well as privacy of users. In the thesis we identify the related problems and solve the issues using our proposed privacy preserving protocols. As an overall idea, our proposed recommendation protocols work as follows. The users encrypt their ratings using homomorphic encryption and send them to service providers. We assume the service providers are semi honest but curious, they follow the protocol but at the same time try to find new information from the available data. The service provider has the ability to perform homomorphic operations and it performs certain computations over encrypted data without learning any true information and returns the results to the query users who ask for recommendations. The system models of our privacy preserving protocols for different recommendation techniques differ from each other because of their different privacy requirements. The proposed privacy preserving protocols are tested on various real world datasets. Based on the application areas of different recommendation approaches our gathered datasets are also different such as movie rating, social network, checkin information for different locations and quality of service of web services. For each proposed privacy preserving protocols we also present the privacy analysis and describe how the system can perform the computations without leaking the private information of users. The experimental and privacy analysis of our proposed privacy preserving protocols for different types of recommendation techniques show that they are private as well as practical

Identification of genotypes resistant to blast, bacterial leaf blight, sheath blight and tungro and efficacy of seed treating fungicides against blast disease of rice

A total of 35 inbred and 13 hybrid varieties including susceptible checks were screened against the 4 major diseases of rice (blast, bacterial leaf blight, sheath blight and tungro) as well as experiments on management of blast were conducted in the rain-fed and irrigated rice ecosystems during 1999 to 2003. Results showed that none of the tested high yielding varieties (HYV) were resistant to blast, while the hybrids, sonarbangla1, aalock6201, KRH2, IR71101H, IR68877H and IR76901H, and inbreds BR12, BR15 and IR72 were moderately resistant in the irrigated rice ecosystem. On the other hand, all the varieties tested against bacterial leaf blight (BLB) and sheath blight (ShB) were moderately susceptible in the same ecosystem. The inbred varieties BR22, BR25, BRRI dhan31, BRRI dhan32, BRRI dhan33, BRRI dhan34, BRRI dhan38 and BRRI dhan39 demonstrated moderately resistant reactions but all the hybrids were moderately susceptible to BLB in the rain-fed ecosystem. Eight inbreds, predominantly, BR22, BR23, BRRI dhan27, BRRI dhan31, BRRI dhan32, BRRI dhan37, BRRI dhan38 and BRRI dhan40 were moderately resistant to tungro disease. Among the 3 fungicides tested in 2 different trials, adivistin and haydazim 50 WP (carbendazim) at the rate of 0.4% were more effective as seed-treating fungicides for the control of rice blast disease

General Strategy for Broadband Coherent Perfect Absorption and Multi-wavelength All-optical Switching Based on Epsilon-Near-Zero Multilayer Films

We propose a general, easy-to-implement scheme for broadband coherent perfect absorption (CPA) using epsilon-near-zero (ENZ) multilayer films. Specifically, we employ indium tin oxide (ITO) as a tunable ENZ material, and theoretically investigate CPA in the near-infrared region. We first derive general CPA conditions using the scattering matrix and the admittance matching methods. Then, by combining these two methods, we extract analytic expressions for all relevant parameters for CPA. Based on this theoretical framework, we proceed to study ENZ CPA in a single layer ITO film and apply it to all-optical switching. Finally, using an ITO multilayer of different ENZ wavelengths, we implement broadband ENZ CPA structures and investigate multi-wavelength all-optical switching in the technologically important telecommunication window. In our design, the admittance matching diagram was employed to graphically extract not only the structural parameters (the film thicknesses and incident angles), but also the input beam parameters (the irradiance ratio and phase difference between two input beams). We find that the multi-wavelength all-optical switching in our broadband ENZ CPA system can be fully controlled by the phase difference between two input beams. The simple but general design principles and analyses in this work can be widely used in various thin-film devicesopen

Disparities in rheumatoid arthritis disease activity according to gross domestic product in 25 countries in the QUEST–RA database

OBJECTIVE: To analyse associations between the clinical status of patients with rheumatoid arthritis (RA) and the gross domestic product (GDP) of their resident country. METHODS: The Quantitative Standard Monitoring of Patients with Rheumatoid Arthritis (QUEST-RA) cohort includes clinical and questionnaire data from 6004 patients who were seen in usual care at 70 rheumatology clinics in 25 countries as of April 2008, including 18 European countries. Demographic variables, clinical characteristics, RA disease activity measures, including the disease activity score in 28 joints (DAS28), and treatment-related variables were analysed according to GDP per capita, including 14 "high GDP" countries with GDP per capita greater than US$24,000 and 11 "low GDP" countries with GDP per capita less than US$11,000. RESULTS: Disease activity DAS28 ranged between 3.1 and 6.0 among the 25 countries and was significantly associated with GDP (r = -0.78, 95% CI -0.56 to -0.90, r(2) = 61%). Disease activity levels differed substantially between "high GDP" and "low GDP" countries at much greater levels than according to whether patients were currently taking or not taking methotrexate, prednisone and/or biological agents. CONCLUSIONS: The clinical status of patients with RA was correlated significantly with GDP among 25 mostly European countries according to all disease measures, associated only modestly with the current use of antirheumatic medications. The burden of arthritis appears substantially greater in "low GDP" than in "high GDP" countries. These findings may alert healthcare professionals and designers of health policy towards improving the clinical status of patients with RA in all countries

A survey of trainee specialists experiences at the University of Cape Town (UCT): Impacts of race and gender

<p>Abstract</p> <p>Background</p> <p>Efforts to redress racial and gender inequalities in the training of medical specialists has been a central part of a dedicated programme in the Faculty of Health Sciences at the University of Cape Town (UCT). This study aimed to describe trends in race and gender profiles of postgraduate students in medical specialties (registrars) from 1999 to 2006 and to identify factors affecting recruitment and retention of black and female trainees.</p> <p>Method</p> <p>Review of faculty databases for race and gender data from 1999 to 2006. Distribution of an anonymous self-administered questionnaire to all registrars in 2005/2006.</p> <p>Results</p> <p>The percentage of African registrars doubled from 10% to 19% from 1999 to beyond 2002. The percentages of Africans, Coloureds and Indians rose steadily from 26% to 46% from 1999 to 2005, as did that of women from 27% to 44%. The institution's perceived good reputation, being an alumnus and originating from Cape Town were common reasons for choosing UCT for training. A quarter of respondents reported knowledge of a friend who decided against studying at UCT for reasons which included anticipated racial discrimination. Black respondents (23%), particularly African (50%), were more likely to describe registrarship at UCT as unwelcoming than white respondents (12%). Specific instances of personal experience of discrimination were uncommon and not associated with respondents' race or gender. Registrars who had had a child during registrarship and those reporting discrimination were more likely to rate the learning and research environment as poor (Odds Ratio, 4.01; 95% CI 0.98 – 16.47 and 1.99 95% CI 0.57 – 6.97, respectively).</p> <p>Conclusion</p> <p>The proportion of black and female registrars at the University of Cape Town has increased steadily from 1999 to 2006, most likely a result of systematic equity policies and procedures adopted in the faculty during this period. The data point to a need for policies to make the institution more welcoming to diversity and for strategies to address institutional culture and mentorship, with an aim to develop examples of best practices to share within and between institutions.</p

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study.

OBJECTIVES: To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort. STUDY DESIGN: The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort. RESULTS: The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001). CONCLUSION: A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations

Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare

In modern e-healthcare systems, medical institutions can provide more reliable diagnoses by introducing Machine-Learning (ML)-based classifiers. These ML classifiers are frequently trained with huge numbers of patients data to keep updated with new diseases and changes in current disease patterns. To increase the accuracy in prediction process, Peer-to-Peer (P2P) learning systems have been explored by many stud- ies by which medical institutions can share their data with others: the more data are available, the more accurate the predictions. However, the traditional P2P network system requires much time in which the training data are shared among the nodes in the network. The system also spends much time on learning from samples where the data labels are unknown. Moreover, some nodes may perform certain compu- tations which had already been computed by other nodes, resulting in redundant computations. In this paper, in order to deal with samples having unknown data labels, we propose a Collaborative Extreme Learning Machine (CELM) with a Confidence Interval (CI), which is an enhanced version of the traditional Extreme Learning Machine (ELM). Our proposed model eliminates redundant calculations of the network nodes (the e-healthcare institutions) to improve the learning efficiency, and improves the prediction ac- curacy by considering where plausible predictions lie. The extensive experimental analysis shows that the proposed model is efficient and achieves high accuracy (up to 98%) in diagnosing clinical events by analyzing patients medical records

Quality evaluation of commercially available instant mango drinks powder in local market of Bangladesh

The upward trend of consumption of processed food must not dim the demand of taking healthy and safe food among population. Thus, six popular commercial brands of instant mango drinks powder of Bangladesh were targeted to investigate some quality parameters (proximate compositions, mineral contents and bioactive compounds). Mineral contents and bioactive compounds of instant mango drinks powder were determined by using biochemical analyzer and UV-visible spectrophotometer, respectively. Results of proximate analysis showed that moisture content, ash content, fiber content, and carbohydrate content of different brands of instant mango drinks powder ranged from 0.21 to 0.25%, 0.45 to 0.55%, 0.10 to 0.40%, and 98.83 to 99.21%, respectively, whereas energy value ranged from 395.32 to 396.84 Kcal/100g. Sodium, potassium, calcium, chloride, phosphorus, iron and vitamin-C were also determined, which showed the significant different (p<0.05) values among different brands. Total anthocyanin content (TAC), Total flavonoid content (TFC), Total phenolic content (TPC), Antioxidant capacity were determined as bioactive compounds. Results of bioactive compounds analysis also showed that the samples were significantly different (p<0.05). Although, the quality varied from brand to brand, but all the samples could be good source of vitamin-C, carbohydrate and energy. Furthermore, health concerning issues can be improved by focusing the bioactive compounds of commercially available instant drinks powder