Simultaneous Distillation Extraction of Some Volatile Flavor Components from Pu-erh Tea Samples—Comparison with Steam Distillation-Liquid/Liquid Extraction and Soxhlet Extraction

A simutaneous distillation extraction (SDE) combined GC method was constructed for determination of volatile flavor components in Pu-erh tea samples. Dichloromethane and ethyl decylate was employed as organic phase in SDE and internal standard in determination, respectively. Weakly polar DB-5 column was used to separate the volatile flavor components in GC, 10 of the components were quantitatively analyzed, and further confirmed by GC-MS. The recovery covered from 66.4%–109%, and repeatability expressed as RSD was in range of 1.44%–12.6%. SDE was most suitable for the extraction of the anlytes by comparing with steam distillation-liquid/liquid extraction and Soxhlet extraction. Commercially available Pu-erh tea samples, including Pu-erh raw tea and ripe tea, were analyzed by the constructed method. the high-volatile components, such as benzyl alcohol, linalool oxide, and linalool, were greatly rich in Pu-erh raw teas, while the contents of 1,2,3-Trimethoxylbenzene and 1,2,4-Trimethoxylbenzene were much high in Pu-erh ripe teas

Genetic variants in ADAM33 are associated with airway inflammation and lung function in COPD

BACKGROUND: Genetic factors play a role in the development and severity of chronic obstructive pulmonary disease (COPD). The pathogenesis of COPD is a multifactorial process including an inflammatory cell profile. Recent studies revealed that single nucleotide polymorphisms (SNPs) within ADAM33 increased the susceptibility to COPD through changing the airway inflammatory process and lung function. METHODS: In this paper, we investigated associations of four polymorphisms (T1, T2, S2 and Q-1) of ADAM33 as well as their haplotypes with pulmonary function and airway inflammatory process in an East Asian population of patients with COPD. RESULTS: We found that T1, T2 and Q-1 were significantly associated with the changes of pulmonary function and components of cells in sputum of COPD, and T1 and Q-1 were significantly associated with cytokines and mediators of inflammation in airway of COPD in recessive models. 10 haplotypes were significantly associated with transfer factor of the lung for carbon monoxide in the disease state, 4 haplotypes were significantly associated with forced expiratory volume in one second, and other haplotypes were associated with airway inflammation. CONCLUSIONS: We confirmed for the first time that ADAM33 was involved in the pathogenesis of COPD by affecting airway inflammation and immune response in an East Asian population. Our results made the genetic background of COPD, a common and disabling disease, more apparent, which would supply genetic support for the study of the mechanism, classification and treatment for this disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2466-14-173) contains supplementary material, which is available to authorized users

Approximate Query Service on Autonomous IoT Cameras

Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts. Elf's novelty centers on planning the camera's count actions under energy constraint. (1) Elf explores the rich action space spanned by the number of sample image frames and the choice of per-frame object counters; it unifies errors from both sources into one single bounded error. (2) To decide count actions at run time, Elf employs a learning-based planner, jointly optimizing for past and future videos without delaying result materialization. Tested with more than 1,000 hours of videos and under realistic energy constraints, Elf continuously generates object counts within only 11% of the true counts on average. Alongside the counts, Elf presents narrow errors shown to be bounded and up to 3.4x smaller than competitive baselines. At a higher level, Elf makes a case for advancing the geographic frontier of video analytics

Telemedicine in patients with obsessive–compulsive disorder after deep brain stimulation: a case series

BackgroundPatients suffering from refractory obsessive-compulsive disorder (OCD) who have undergone deep brain stimulation (DBS) surgery require repeated in-person programming visits. These sessions could be labor-intensive and may not always be feasible, particularly when in-person hospital visits are restricted. Telemedicine is emerging as a potential supplementary tool for post-operative care. However, its reliability and feasibility still require further validation due to the unconventional methods of interaction.MethodsA study was conducted on three patients with refractory OCD who had undergone DBS. Most of their programming sessions were completed via a remote programming system. These patients were recruited and monitored for a year. Changes in their clinical symptoms were assessed using the Yale-Brown Obsessive-Compulsive Scale–Second Edition (Y-BOCS-II), the Hamilton Anxiety Scale-14 (HAMA), the Hamilton Depression Scale-17 (HAMD), and the Short Form 36 Health Survey Questionnaire (SF-36). The scores from these assessments were reported.ResultsAt the last follow-up, two out of three patients were identified as responders, with their Y-BOCS-II scores improving by more than 35% (P1: 51%, P3: 42%). These patients also experienced some mood benefits. All patients observed a decrease in travel expenses during the study period. No severe adverse events were reported throughout the study.ConclusionThe group of patients showed improvement in their OCD symptoms within a 1-year follow-up period after DBS surgery, without compromising safety or benefits. This suggests that telemedicine could be a valuable supplementary tool when in-person visits are limited

Correctness, Performance, and Energy-Efficiency: Improving Software Systems That Use Machine Learning Components

An increasing number of software applications adopt machine learning (ML) components to solve real-world problems. The offering of ML cloud APIs further ease developers' burden of incorporating ML solutions, typically deep neural networks (DNNs). However, to achieve a correct, fast, and energy-efficient ML application, developers still need to carefully design its three crucial components: ML algorithm, system environment, and software context. To improve correctness, performance, and energy-efficiency of ML applications, this dissertation works on these components and makes the following contributions: First, to enhance the flexibility of neural networks, this dissertation proposes a novel neural network architecture and a customized optimizer that support anytime prediction. This design allows one neural network to generate a series of increasingly accurate outputs over time without sacrificing accuracy for flexibility. Second, this dissertation designs a run-time scheduler ALERT, which further manages system resources. ALERT holistically configures neural networks and system resources together to meet application-specific accuracy, performance, and energy-consumption constraints. It uses a probabilistic model to detect environmental volatility and makes use of the full potential of the DNN candidate set to optimize performance and satisfy constraints. Third, to understand the challenges of developing ML software, this dissertation conducts the first comprehensive study about how real-world applications are using machine learning cloud APIs. We generalize 8 anti-patterns that degrade functional, performance, or economical quality of the software. Fourth, guided by this study, we propose Keeper, a new testing framework for software systems that use machine learning APIs. Keeper automatically generates many test cases to thoroughly test every branch in the specified function and its callees. It analyzes the test runs and reports many failures, as well as potential patches, to developers