Assessing the performance of the Asian/Pacific islander identification algorithm to infer Hmong ethnicity from electronic health records in California.

OBJECTIVE:This study assesses the performance of the North American Association of Central Cancer Registries Asian/Pacific Islander Identification Algorithm (NAPIIA) to infer Hmong ethnicity. DESIGN AND SETTING:Analyses of electronic health records (EHRs) from 1 January 2011 to 1 October 2015. The NAPIIA was applied to the EHR data, and self-reported Hmong ethnicity from a questionnaire was used as the gold standard. Sensitivity, specificity, positive (PPV) and negative predictive values (NPVs) were calculated comparing the source data ethnicity inferred by the algorithm with the self-reported ethnicity from the questionnaire. PARTICIPANTS:EHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed. RESULTS:The NAPIIA had a sensitivity of 78%, a specificity of 99.9%, a PPV of 96% and an NPV of 99%. The prevalence of Hmong population in the sample was 3.9%. CONCLUSION:The high sensitivity of the NAPIIA indicates its effectiveness in detecting Hmong ethnicity. The applicability of the NAPIIA to a multitude of Asian subgroups can advance Asian health disparity research by enabling researchers to disaggregate Asian data and unmask health challenges of different Asian subgroups

Pengaruh Suplementasi Saccharomyces Cerevisiae sebagai Probiotik dalam Ransum Berbasis Pakan Lokal terhadap Performans dan Kecernaan Nutrisi pada Babi Lokal Fase Starter

The long term purposes of the study are to change the mind and the custom of pigs farmers from using restaurant or household wastes to using local feeds enriched with yeast (Saccharomyces cerevisiae). The short term or special purpose of the study are to find out the cheaper alternative feeds for pigs and information of using yeast to improve feeds quality and pigs productivity. The study with those purposes was carried out off farm by supplementing yeast into low quality pig feeds (crude protein/CP ≤ 16%) of local weaned pigs composed of: corn meal, rice brand, soybean/tofu extract and unused fish meal. 12 local weaned pigs were fed 4 treatment diets based on block design of 4 treatments with 3 blocks design procedure. The 4 treatment feeds were formulated as : R0 (commercial diet/551); R1 (basal feed + 2% yeast of daily requirement); R2 (basal feed + 4% yeast of daily requirement); and R3 (basal feed + 6% yeast of daily requirement). Feed intake, daily weight gain, feeds conversion efficiency, protein, and crude fibre digestibility were studied in the study. Statistical analysis showed that the effect of the treatments is not significant (P>0.05) on all variables studied. Supplementation yeast of 6% is the best treatment performing the highest result of most variable studied. The conclusion drawn is that supplementing yeast up to 6% could improve performance of weaned pigs fed low quality feed and perform the similar result with feeding commercial feed (551). It is suggested to use yeast up to 6% in the diet and further research including widen range and high level of yeast supplementation could be done

What drives foreign direct investment: the role of language, geographical distance, information flows and technological similarity

This paper sheds new light on the impact of linguistic and technological similarities between countries on foreign direct investment (FDI), using an extended gravity model. The model includes technological commonality, as measured by the aggregate production of intellectual property, at the country level. An analysis of 71,309 pairs of FDI relationships, from 2000 to 2012, showed that language is positively associated with a high level of FDI. Technological differences do impede the flow of FDI between countries, and information flow is crucial for large flows of FDI. Information flow diminishes the negative impact of distance.info:eu-repo/semantics/acceptedVersio

Lactobacillus rhamnosus GG enhances intestinal mucus barrier upon Escherichia coli infection

INTRODUCTION: Foodborne infection is a major food safety issue in both developing and developed countries. The use of probiotics has been emerging as a tool for the control of foodborne infections and gastrointestinal disorders such as diarrhea. Our study aims to investigate potential protective effects of probiotics on intestinal epithelial cells against invasion of foodborne pathogens and to elucidate the mechanisms of such effects. We hypothesize that Lactobacillus rhamnosus GG (LGG) can either be used as prevention against or as an antagonist of foodborne pathogens, and such protective effects can be conferred through the upregulation of mucins in the intestinal epithelial cells …postprin

Multispectral pattern recognition reveals a diversity of clinical signs in intermediate age-related macular degeneration

PURPOSE. To develop a proof-of-concept, computational method for the quantification and classification of fundus images in intermediate age-related macular degeneration (AMD). METHODS. Multispectral, unsupervised pattern recognition was applied to 184 fundus images from 10 normal and 36 intermediate AMD eyes. The imaging results of preprocessed, grayscale images from three modalities (infrared, green, and fundus autofluorescence scanning laser ophthalmoscopy) were automatically classified into various clusters sharing a common spectral signature, using a k-means clustering algorithm. Class separability was calculated by using transformed divergence (DT). The classification results for large drusen, pigmentary abnormalities, and areas unaffected by AMD were compared against three expert observers for concordance, and to calculate sensitivity and specificity. RESULTS. Multispectral, unsupervised pattern recognition successfully identified a finite number of AMD-specific, statistically separable signatures in eyes with intermediate AMD. By using a correct classification criterion of >83% for identical clusters and a total of 1693 expert annotations, the sensitivity and specificity of multispectral pattern recognition for the detection of AMD lesions was 74% and 98%, respectively. Large drusen and pigmentary abnormalities were correctly classified in 75% and 68% of instances, respectively. CONCLUSIONS. We describe herein a novel approach for the classification of multispectral images in intermediate AMD. Automated classification of intermediate AMD, using multispectral pattern recognition, has moderate sensitivity and high specificity, when compared against clinical experts. The methods described may have a future role in AMD screening or monitoring

Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure

Emerging role of iron oxide nanoparticles in the diagnostic imaging of pancreatic cancer: a systematic review

Background/Aims: Pancreatic cancer is the fourth most common cause of cancer associated death worldwide with a five year survival rate less than 5%. The poor prognosis is mainly due to late presentation in 80% of patients and its drug resistant nature. Most diagnoses are made using contrast-enhanced computed-tomography (CT) or magnetic-resonance-imaging (MRI) which have a limited sensitivity between 76-86%. Iron oxide nanoparticles are increasingly used in the diagnostic imaging of pancreatic cancer, due their ability to selectively target tumour cells thereby increasing image resolution. The aim of this study is to identify studies investigating the use of iron oxide nanoparticles in the diagnostic imaging of pancreatic cancer. Methods: A systematic review was performed using PubMed for records up to 2015. Search terms used included "iron oxide nanoparticles", "pancreatic cancer" and "imaging". Results: A total of 16 studies were identified evaluating the use of iron oxide nanoparticles in the imaging of pancreatic cancer in-vitro and in in-vivo animal models. Eight of the studies evaluated the use of superparamagnetic-iron-oxide-nanoparticles (SPION), they showed SPION significantly decrease the T2 and T2* relaxation times of tumour tissue, providing a high sensitivity for MRI. Similar results were seen in eight studies that investigated the use of iron oxide nanoparticles conjugated to other molecules including gelatin, survivin, chemokine-receptor-4, silica-gold, endothelial-growth-factor-receptor, urokinase-receptor activator, Clostridium and a sonic-hedgehog target. Conclusion: Iron oxide nanoparticles in the form of SPION or conjugates are biocompatible and effective at targeting tumour cells and significantly attenuate MRI signals in T2-weighted images of pancreatic cancers from a range of cell lines

DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text overlap with arXiv:2008.0375

Where are we now? Reconsidering interactive text features and their role in the classification of digital books as considerate or inconsiderate

This dissertation presents an updated content features analysis on high-quality digital book versions of printed books. In a time where mobile devices (i.e., iPad, iPhone, Android phones) are ubiquitous, current research on the quality of digital books read on these devices have been sparse. With children having access to these mobile devices to play games, read digital books, listen to music, and watch shows, an updated study on the quality of digital books read on these devices is needed. Using the considerate/inconsiderate framework, the terms integral (vital or corresponding actions), incidental (additional or plausible actions), and incongruent (disparate and illogical actions) were used to describe whether the interactive media features in the 20 high-quality digital books were supportive in meaning-making. Those designations led to an evaluation of whether each digital books –as a whole—was supportive or nonsupportive of comprehension. Analysis showed that all of the high-quality printed version of digital books produced by Oceanhouse Media and two from Loud Crow Interactive were considerate (i.e., supported meaning-making for young children). Findings from this study confirm the utility of the considerate/inconsiderate framework as an analytic tool for evaluating the potential of using high-quality digital book versions of printed books for instructional practices. Furthermore, the dissertation shows how the findings from this study could inform the development of an evaluative tool for educators and researchers to identify high-quality digital books for classroom use and support the categorization of types of available digital books, respectively. Finally, findings point to the need for further research on whether the considerate/inconsiderate framework holds merit for evaluating digital books from a range of quality levels not just high-quality digital book versions of printed books