Successful Resection of a Mycotic Aneurysm of the Superior Mesenteric Artery

AbstractAneurysm of the superior mesenteric artery is rare. More than 50% are mycotic. An aneurysm at this site ruptures easily and is difficult to manage. Here, we report a 49-year-old man with a mycotic aneurysm of the superior mesenteric artery, which was successfully resected, with revascularization from the infrarenal aorta using a retrograde vein graft

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy

S-allylcysteine Improves Blood Flow Recovery and Prevents Ischemic Injury by Augmenting Neovasculogenesis.

Studies suggest that a low level of circulating human endothelial progenitor cells (EPCs) is a risk factor for ischemic injury and coronary artery disease (CAD). Consumption of S-allylcysteine (SAC) is known to prevent CAD. However, the protective effects of SAC on the ischemic injury are not yet clear. In this study, we examined whether SAC could improve blood flow recovery in ischemic tissues through EPC-mediated neovasculogenesis. The results demonstrate that SAC significantly enhances the neovasculogenesis of EPCs in vitro. The molecular mechanisms for SAC enhancement of neovasculogenesis include the activation of Akt/endothelial nitric oxide synthase signaling cascades. SAC increased the expression of c-kit, β-catenin, cyclin D1, and Cyclin-dependent kinase 4 (CDK4) proteins in EPCs. Daily intake of SAC at dosages of 0.2 and 2 mg/kg body weight significantly enhanced c-kit protein levels in vivo. We conclude that dietary consumption of SAC improves blood flow recovery and prevents ischemic injury by inducing neovasculogenesis in experimental models

Customer Behavior Survery for Cultural and Creative Park in Taiwan

Cultural and Creative Park is a recreational campus which usually consists of exhibition, gallery, show room, movie theater, and multi-function facilities to provide the cultural activities. Besides, in the Cultural and Creative Park, restaurants, coffee shops, bookstores, gift shops, and other business units are nearby. How to improve the customer experience in the Cultural and Creative Park is an important research question for the managerial division to promote culture industries. In this research, the questionnaires were developed and performed in one of creative park in Taipei, Taiwan to study customer behavior. This paper addresses the survey result and the insights revealed from the survey

Canine Hepatic Carcinoma: Diagnoses and Treatments Via Global State-of-the-Art Approach and Traditional Chinese Veterinary Medicine

This chapter discusses effective diagnostics and treatment of canine hepatic carcinoma (CHC), where state-of-the-art global technologies are complemented by traditional Chinese veterinary medicine (TCVM). The biokinetic Ga-67 model of CHC is proposed to clarify the Ga-67 metabolic mechanism among various organs. It is aimed at identifying the best routine for detecting the metastatic or primary CHC and substantiating the optimal further treatment. The routine examination of CHC can be performed via Ga-67 nuclear examination or MRI, biological index, X-ray, and abdominal ultrasound. The available methods of animal cancer treatment imply separate or combined application of surgery, radiation therapy, and chemotherapy targeted at the particular cancer cells. However, there is also a general concern on the quality of life of pets/canine patients. This leaves enough space to the TCVM (including acupuncture and famous herbal drugs) with a long application history in Asia and growing usage as alternative treatment in other regions. However, its current applications to domestic animals/pets suffering from carcinomas are based on individual expert opinions, while there are no outlined veterinary treatment strategies and guidelines for clinical practice in this field. A comprehensive combination of state-of-the-art global technologies and TCVM is considered instrumental in curing canine hepatic carcinoma

Dish Discovery via Word Embeddings on Restaurant Reviews

ABSTRACT This paper proposes a novel framework for automatic dish discovery via word embeddings on restaurant reviews. We collect a dataset of user reviews from Yelp and parse the reviews to extract dish words. Then, we utilize the processed reviews as training texts to learn the embedding vectors of words via the skip-gram model. In the paper, a nearestneighbor like score function is proposed to rank the dishes based on their learned representations. We brief some analyses on the preliminary experiments and present a web-based visualization at http://clip.csie.org/yelp/. Keywords dish discovery, word embeddings, dish-word extraction BACKGROUND With the growth of social media, corporations, such as Yelp, have accumulated a great number of user generated content (UGC). In the literature, some studies have been conducted with a perspective of finding critical information hidden in the content METHODOLOGY Copyright held by the author(s). RecSys 2016 Poster Proceedings, September 15-19, 2016, USA, Boston. Our methodology mainly consists of three parts: 1) dishword recognition, 2) word embedding learning, and 3) dish score calculation. As alluded to earlier, UGC usually incorporates a degree of noise and different language usages; therefore, extracting dish names from user reviews is a complicated task. For example, observed from the dataset, users tend not to write the full name of a dish in their reviews; instead, the last word or the last two words are often written in the reviews. To grapple with this issue, we use regular expressions (regexps) to extract dish names from the user reviews. However, this also give rise to an issue that a certain dish in a restaurant may be of the same name in other restaurants, which may induce the problem of ambiguity and lower the accuracy of matching the correct dish name. So, we attach a dish name with its restaurant name to solve the ambiguity problem. We then utilize the collection of processed reviews as training texts to learn embeddings of each word in the reviews via a continuous space language model, the skip-gram model. After the training phase, each word (including every dish) is represented by an n-dimensional vector (called the embedding of this word). Inspired by the k-nearest neighbors algorithm, we define the score for every dish d as: where , m is the total number of positive sentiment words considered, λi (i = 1, · · · , m) is a weighting parameter. In addition, si denotes the i-nearest positive sentiment words of the given dish d, and w d , ws i ∈ R n are the vector representations of the dish d and the sentiment word si, respectively. In an extreme case (1) of λm = 1 and λi = 0 for i = 1, · · · , m − 1, this score function implements the concept of the average Euclidean distance between a dish and all the positive sentiment words; while in the case (2) λ1 = 1 and λi = 0 for i = 2, · · · , m, the scored is obtained with the closest positive sentiment words to the dish. EXPERIMENTS Our preliminary experiments involve a real-world restaurant review dataset collected from Yelp Data Challenge

Body Mass Index–Mortality Relationship in Severe Hypoglycemic Patients With Type 2 Diabetes

AbstractBackgroundHypoglycemia is associated with a higher risk of death. This study analyzed various body mass index (BMI) categories and mortalities of severe hypoglycemic patients with type 2 diabetes mellitus (DM) in a hospital emergency department.MethodsThe study included 566 adults with type 2 diabetes who were admitted to 1 medical center in Taiwan between 2008 and 2009 with a diagnosis of severe hypoglycemia. Mortality data, demographics, clinical characteristics and the Charlson’s Comorbidity Index were obtained from the electronic medical records. Patients were stratified into 4 study groups as determined by the National institute of Health (NiH) and World Health organization classification for BMi, and the demographics were compared using the analysis of variance and χ2 test. Kaplan-Meier’s analysis and the Cox proportional-hazards regression model were used for mortality, and adjusted hazard ratios were adjusted for each BMi category among participants.ResultsAfter controlling for other possible confounding variables, BMI <18.5 kg/m2 was independently associated with low survival rates in the Cox regression analysis of the entire cohort of type 2 DM patients who encountered a hypoglycemic event. Compared to patients with normal BMI, the mortality risk was higher (adjusted hazard ratios = 4.9; 95% confidence interval [CI] = 2.4-9.9) in underweight patients. Infection-related causes of death were observed in 101 cases (69.2%) and were the leading cause of death.ConclusionsAn independent association was observed between BMI less than 18.5 kg/m2 and mortality among type 2 DM patient with severe hypoglycemic episode. Deaths were predominantly infection related

Quantifying sample completeness and comparing diversities among assemblages.

We develop a novel class of measures to quantify sample completeness of a biological survey. The class of measures is parameterized by an order q ≥ 0 to control for sensitivity to species relative abundances. When q = 0, species abundances are disregarded and our measure reduces to the conventional measure of completeness, that is, the ratio of the observed species richness to the true richness (observed plus undetected). When q = 1, our measure reduces to the sample coverage (the proportion of the total number of individuals in the entire assemblage that belongs to detected species), a concept developed by Alan Turing in his cryptographic analysis. The sample completeness of a general order q ≥ 0 extends Turing's sample coverage and quantifies the proportion of the assemblage's individuals belonging to detected species, with each individual being proportionally weighted by the (q − 1)th power of its abundance. We propose the use of a continuous profile depicting our proposed measures with respect to q ≥ 0 to characterize the sample completeness of a survey. An analytic estimator of the diversity profile and its sampling uncertainty based on a bootstrap method are derived and tested by simulations. To compare diversity across multiple assemblages, we propose an integrated approach based on the framework of Hill numbers to assess (a) the sample completeness profile, (b) asymptotic diversity estimates to infer true diversities of entire assemblages, (c) non‐asymptotic standardization via rarefaction and extrapolation, and (d) an evenness profile. Our framework can be extended to incidence data. Empirical data sets from several research fields are used for illustration.publishedVersionPaid Open Acces