Starting Time Point Research Articles

Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers.

Continuous in-home monitoring of Parkinson’s Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.

Feeding Strategies of Two-Stage Fed-Batch Cultivation Processes for Microbial Lipid Production from Sugarcane Top Hydrolysate and Crude Glycerol by the Oleaginous Red Yeast Rhodosporidiobolus fluvialis.

Microbial lipids are able to produce from various raw materials including lignocellulosic biomass by the effective oleaginous microorganisms using different cultivation processes. This study aimed to enhance microbial lipid production from the low-cost substrates namely sugarcane top hydrolysate and crude glycerol by Rhodosporidiobolus fluvialis DMKU-SP314, using two-stage fed-batch cultivation with different feeding strategies in a 3 L stirred-tank fermenter. The effect of two feeding strategies of 147.5 g/L crude glycerol solution was evaluated including pulse feeding at different starting time points (48, 24, and 72 h after initiation of batch operation) and constant feeding at different dilution rates (0.012, 0.020, and 0.033 h−1). The maximum lipid concentration of 23.6 g/L and cell mass of 38.5 g/L were achieved when constant feeding was performed at the dilution rate of 0.012 h−1 after 48 h of batch operation, which represented 1.24-fold and 1.27-fold improvements in the lipid and cell mass concentration, respectively. Whereas, batch cultivation provided 19.1 g/L of lipids and 30.3 g/L of cell mass. The overall lipid productivity increased to 98.4 mg/L/d in the two-stage fed-batch cultivation. This demonstrated that the two-stage fed-batch cultivation with constant feeding strategy has the possibility to apply for large-scale production of lipids by yeast.

The Role of Somatic Maturation on Bioimpedance Patterns and Body Composition in Male Elite Youth Soccer Players.

The purpose of this study was to examine the influence of chronological age (CA) and somatic maturation on body composition (BC) and bioimpedance parameters in male elite soccer players. BC and bioimpedance variables were measured in a sample of 249 players aged 9−18 years of age and registered in two professional Italian soccer teams. Results from segmental analysis showed transition time points where the influence of CA and somatic maturation on bioimpedance patterns and BC characteristics increased or subsided. The accelerated phases were assessed for fat free mass, total body water, and upper muscle area, with a starting time point at approximately −2.00 years from peak at velocity (YPHV), and for body cell mass, whose developmental tempo sped up around −1.00 YPHV. An increase in the rate of development was also observed close to −2.00 YPHV for phase angle (PA), although without accelerated phases. From a CA point of view, significant slope changes were found for all BC and bioimpendance variables, except for the calf muscle area. Although the starting points and the span of the accelerated phases were different, they subsided or disappeared at ~ 15 years, except for PA, whose growth waned at ~ 17 years.

Iterative Curve Fitting of the Bioheat Transfer Equation for Thermocouple-Based Temperature Estimation In Vitro and In Vivo.

Temperature measurements with thin thermocouples embedded in ultrasound fields are strongly subjected to a viscous heating artifact (VHA). The artifact contribution decays over time; therefore, it can be minimized at late temperature readings. However, previous studies have failed to demonstrate a rigorous method for determining the optimal time point at which the artifact contribution is negligible. In this study, we present an iterative processing method based on successive curve fittings using an artifact-independent model. The fitting starting point moves at each iteration until the maximum R2 indicates where the viscous heating is minimum. A solution of the bioheat transfer equation is used to account for blood perfusion, thus enabling in vivo measurements. Three T-type thermocouples with different diameters and sensitivities were assessed in an excised canine liver and in the mouse brain in vivo. We found that the artifact constitutes up to 81% ± 5% of wire thermocouple readings. The best-fit time varied in the liver samples ( n = 3 ) from 0 to 3.335 ± 0.979 s and in the mouse brain ( n = 5 ) from 0 to 0.498 ± 0.457 s at variable experimental conditions, which clearly demonstrates the need of the method for finding the appropriate starting time point of the fit. This study introduces a statistical method to determine the best time to fit a curve that can back-estimate temperature in tissues under ultrasound exposure using thermocouples. This method allows temperature evaluation in vivo and in vitro during a validation and safety assessment of a wide range of therapeutic and diagnostic ultrasound modalities.

Model prediction of color reversion of soybean oil and its quantitative relationship with oxidation under accelerated conditions

Color reversion of edible oil has seriously troubled the vegetable oil industry, but not yet been satisfactorily solved. In this study, Chroma (C*) could be an effective indicator and significantly associated with specific absorbance values of oxidized oils at 243–367 nm (P < 0.01). Effect of heating time and temperature on the color changes of soybean oil was first described by the combination of two-stage logistic and Arrhenius equations with R2 higher than 0.90. Total three equations for the start time point, end time point and duration time of color reversion were provided under accelerated conditions, respectively. The theoretically derived but not validated equations for predicting the color reversion of oils at room temperature was proposed as well. The certain relationships between the color reversion and oxidation were deduced and proved based on the kinetic analysis of free radical mechanism. Data will be expected to provide useful models for manufacturers to predict and control the color reversion of edible oils.

Transient dynamic analysis of cracked structures with multiple contact pairs using generalized HSNC

The development of efficient computational methods for cracked structures is critical in the fields of civil, mechanical, and aerospace engineering since the influence of cracks on structural dynamics can play an important role in design, prognosis, and health monitoring. The nonlinearity caused by the intermittent contact on the crack surfaces typically excludes the use of fast linear methods, without which the computation of the dynamics of complex cracked structures becomes very challenging. In this paper, an efficient computational scheme for predicting both the transient and steady-state responses of cracked structures with multiple contact pairs is introduced. The new algorithm is referred to as the generalized hybrid symbolic–numeric computational (HSNC) method. The generalized HSNC method extends the original HSNC method, which was recently developed for bilinear systems, to general piecewise-linear nonlinear systems. This work also combines the HSNC with the $$\hbox {X}-\hbox {X}_{r}$$ method, a reduced-order modeling technique for cracked structures, to efficiently predict the dynamics of complex structures with cracks. The generalized HSNC approach is based on the idea that the nonlinear response of a cracked structure with multiple contact pairs can be obtained by combining linear responses of the system in each of its linear states. These linear responses can be symbolically expressed as functions of the initial conditions at starting time points in each time range where the system behaves linearly. The transition time where the system switches from one linear state to another is found using a nonlinear optimization solver with the initial values provided by an incremental search process. The method is able to individually track status of each contact pair; therefore, it can be used to predict the dynamics of the system when the crack surfaces are not completely open or closed. Moreover, both the transient and steady-state responses of complex cracked structures under various forcing conditions can be captured by the new method. The generalized HSNC method provides a flexible computational framework that is several orders of magnitude faster than traditional numerical integration methods. The dynamics of a spring–mass system and cantilever beam models that contain one crack and multiple cracks are investigated using the proposed method.

Optimization of the alkaline comet assay for easy repair capacity quantification of oxidative DNA damage in PBMC from human volunteers using aphidicolin block

Assessment of DNA repair capacity (DRC) upon ex vivo challenge of peripheral blood mononuclear cells (PBMC) with oxidative damage inducing agents, as evaluated by the comet assay, is widely used as biomarker to assess the antioxidant status in human studies. Here, the alkaline comet assay was now optimized for easy and time saving detection of repair capacity upon oxidative stress-induced DNA damage using the DNA polymerase inhibitor aphidicolin (APC) to block repair of hydrogen peroxide (H2O2) induced DNA damage. Addition of a DMSO-containing DNA damage stop solution was found suitable to replace washing steps for H2O2 removal before APC block. Cell treatment with APC at 6 μM did not impact baseline DNA damage but could reliably block DNA repair after H2O2 challenge in both fresh and cryopreserved samples thus omitting the use of a starting time point control. Under the conditions used, frozen cells, with or without an additional 4 h rest, showed the same repair capacity as their fresh counterpart. The intra assay coefficient of variation (CV) was 3.3%. To provide proof of principle, the modified assay was applied to cryopreserved PBMC from 19 participants of a short-term Brassica diet intervention study investigating potential health promoting effects of the food intervention. Then, a 33% increase in DRC (p ≤ 0.01) could be shown in samples after intervention (mean ± SD: 5.82 ± 1) as compared to baseline (mean ± SD: 4.38 ± 1.21). Individual samples from baseline and intervention showed an inter-individual CV of 27.65% (baseline) and 17.26% (intervention). Taken together this modified comet assay protocol allows the facilitated detection of DNA repair in fresh or cryopreserved human PBMC samples with a good sensitivity and reliability and could be useful in human studies addressing the antioxidant status and repair capacity of PBMC.

Modeling and querying facts with period timestamps in data warehouses

Abstract In this paper, we study various ways of representing and querying fact data that are time-stamped with a time period in a data warehouse. The main focus is on how to represent the time periods that are associated with the facts in order to support convenient and efficient aggregations over time. We propose three distinct logical models that represent time periods as sets of all time points in a period (instant model), as pairs of start and end time points of a period (period model), and as atomic units that are explicitly stored in a new period dimension (period∗ model). The period dimension is enriched with information about the days of each period, thereby combining the former two models. We use four different classes of aggregation queries to analyze query formulation, query execution, and query performance over the three models. An extensive empirical evaluation on synthetic and real-world datasets and the analysis of the query execution plans reveal that the period model is the best choice in terms of runtime and space for all four query classes.

Daily long-term traffic flow forecasting based on a deep neural network

Daily traffic flow forecasting is critical in advanced traffic management and can improve the efficiency of fixed-time signal control. This paper presents a traffic prediction method for one whole day using a deep neural network based on historical traffic flow data and contextual factor data. The main idea is that traffic flow within a short time period is strongly correlated with the starting and ending time points of the period together with a number of other contextual factors, such as day of week, weather, and season. Therefore, the relationship between the traffic flow values within a given time interval and a combination of contextual factors can be mined from historical data. First, a predictor was trained using a multi-layer supervised learning algorithm to mine the potential relationship between traffic flow data and a combination of key contextual factors. To reduce training times, a batch training method was proposed. Finally, a Seattle-based case study shows that, overall, the proposed method outperforms the conventional traffic prediction method in terms of prediction accuracy.

Analogue Integrated Circuits Design-for-Testability Flow Oriented onto OBIST Strategy

Oscillation Built-In Self-Test (OBIST) strategy allows to avoid the using complex, expensive generators of input test signals during testing, and uses the oscillation frequency generated at the output of the circuit after reconfiguring into oscillator as a controlled parameter. There configuration subcircuit forms an oscillator from the original circuit in the test mode and requires an additional but insignificant area of the chip, especially against the background of stable increasing the scale of integration for the state-of-the-art integrated technologies. Selection of the efficient type of reconfiguration the original circuit into oscillator and implementation of corresponding test circuitry are the most important tasks, which as rule are solved nowadays based on experience of designers without automation and therefore restrict to wide use of the OBIST concept. The paper is mainly focused on the task of design-for-testability (DFT) automation with emphasis on OBIST strategy for analog integrated circuits (IC). The design procedures according to DFT flow are proposed. Three possible structural solutions for reconfiguration of original circuit into an oscillator are considered. The necessary conditions for stability analysis of reconfigured circuit are presented. The stage of a numerical estimating the transient time before the steady-state operation after reconfiguration of original circuit into an oscillator ensuring definition of the start time point for correct calculating the oscillation frequency is proposed. The set of rules for each structural solution for reconfiguration is prepared as the formal procedures, which can support the automation during the DFT flow. The efficiency of the proposed DFT flow is demonstrated for analog circuits, for which the reconfiguration subcircuits were obtained in automated way during design-for-testability, as well as the fault simulation has been performed. The experimental results for all cases showed the adequacy of oscillation frequency for revealing both catastrophic and parametric faults. Fault coverage for considered set of faults has consisted up to 100 %. DOI: http://dx.doi.org/10.5755/j01.itc.47.3.19753

Towards correlation-based time window selection method for motor imagery BCIs

The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.

Different Effect by Different Starting Time Point of Exercise of the Nerve Regeneration after the Peripheral Nerve Injury

Different Effect by Different Starting Time Point of Exercise of the Nerve Regeneration after the Peripheral Nerve Injury

Strong temporal planning with uncontrollable durations

Planning in real world domains often involves modeling and reasoning about the duration of actions. Temporal planning allows such modeling and reasoning by looking for plans that specify start and end time points for each action. In many practical cases, however, the duration of actions may be uncertain and not under the full control of the executor. For example, a navigation task may take more or less time, depending on external conditions such as terrain or weather.In this paper, we tackle the problem of strong temporal planning with uncontrollable action durations (STPUD). For actions with uncontrollable durations, the planner is only allowed to choose the start of the actions, while the end is chosen, within known bounds, by the environment. A solution plan must be robust with respect to all uncontrollable action durations, and must achieve the goal on all executions, despite the choices of the environment.We propose two complementary techniques. First, we discuss a dedicated planning method, that generalizes the state-space temporal planning framework, leveraging SMT-based techniques for temporal networks under uncertainty. Second, we present a compilation-based method, that reduces any STPUD problem to an ordinary temporal planning problem. Moreover, we investigate a set of sufficient conditions to simplify domains by removing some of the uncontrollability.We implemented both our approaches, and we experimentally evaluated our techniques on a large number of instances. Our results demonstrate the practical applicability of the two techniques, which show complementary behavior.

The earlier, the better: the effects of different administration timepoints of sorafenib in suppressing the carcinogenesis of VEGF in rats

PurposeTo investigate the optimal starting time point of sorafenib therapy in suppressing the tumor-promoting effects of VEGF up-regulation, which is frequently found after local therapy in clinical practice.MethodsVEGF was intravenously injected to imitate the evaluated expression after local tumor therapy, such as TACE. A total of 40 SD rats bearing hepatic tumors were randomly divided into four groups and sorafenib was administered at different timepoints: (A) control group: VEGF injection only; (B) initiating sorafenib 72 h prior to VEGF injection; (C) initiating sorafenib simultaneously with VEGF injection; (D) initiating sorafenib 72 h post-VEGF injection. The rate of tumor growth, median survival time, expression of VEGF, and microvessel density (MVD), as determined by immunohistochemical (IHC) examination, were compared.ResultsThe results revealed that the tumor size and median survival time were significantly different between the three sorafenib groups compared to the control group (p < 0.05). Median survival times were 19.6 ± 1.78, 31.2 ± 6.99, 27.4 ± 4.9, and 26.5 ± 4.6 days in group A, B, C, and D, respectively. Furthermore, there was a difference in statistical significance between the two sorafenib groups B and D (p = 0.04). Tumors were collected for HE staining and IHC examination. The expression levels of VEGF in B, C, and D were 42.8 ± 7.96, 71.9 ± 15.73, and 73.6 ± 13.73, and all of them were significantly lower than that in the control group (88.3 ± 13.61). Furthermore, the level of MVD was 109.2 ± 8.98 in the control group, which was significantly higher than in the three sorafenib groups (45.7 ± 16.92, 77.1 ± 16.29, and 93.6 ± 12.87, all p < 0.05).ConclusionsAccording to our results, the most suitable regimen for the administration of sorafenib is before the increased expression of VEGF, which showed a potential advantage for controlling the tumor growth and prolonging the survival time of test animal via inhibiting VEGF-receptor expression through the bifunction of VEGF, and the reduction of tumor angiogenesis.

Multistage Stochastic Programming Models for Pharmaceutical Clinical Trial Planning

Clinical trial planning of candidate drugs is an important task for pharmaceutical companies. In this paper, we propose two new multistage stochastic programming formulations (CM1 and CM2) to determine the optimal clinical trial plan under uncertainty. Decisions of a clinical trial plan include which clinical trials to start and their start times. Its objective is to maximize expected net present value of the entire clinical trial plan. Outcome of a clinical trial is uncertain, i.e., whether a potential drug successfully completes a clinical trial is not known until the clinical trial is completed. This uncertainty is modeled using an endogenous uncertain parameter in CM1 and CM2. The main difference between CM1 and CM2 is an additional binary variable, which tracks both start and end time points of clinical trials in CM2. We compare the sizes and solution times of CM1 and CM2 with each other and with a previously developed formulation (CM3) using different instances of clinical trial planning problem. The results reveal that the solution times of CM1 and CM2 are similar to each other and are up to two orders of magnitude shorter compared to CM3 for all instances considered. In general, the root relaxation problems of CM1 and CM2 took shorter to solve, CM1 and CM2 yielded tight initial gaps, and the solver required fewer branches for convergence to the optimum for CM1 and CM2.

Need for medication to complement catecholamines in smoking cessation of hardcore smokers.

Many smokers find it difficult to stop smoking without assistance. The antidepressants bupropion and nortriptyline can aid smoking cessation. The main aim of this study was to understand the pathophysiology of smoking cessation better based on biological backgrounds. We investigated the following biological markers for any alterations during smoking cessation in the absence of pharmacotherapy: the dopamine metabolite homovanillic acid (HVA), the noradrenaline metabolite 3-methoxy-4-hydroxyphenylglycol (MHPG) and brain-derived neurotrophic factor (BDNF). Assessment and blood sampling were performed at a baseline (the start) time point and at a critical time point during smoking cessation. Seven of 30 smokers quit during a 16-week follow-up period; these smokers were defined as remission group from tobacco dependence. The remaining 23 smokers were categorized as hardcore smokers. The smoking group was compared with 23 non-smokers matched for age and gender. We compared blood levels of biological markers in each of the three groups. The hardcore smoker group showed significant decreases in HVA and MHPG levels between baseline and the critical time point (p=0.018 and p=0.033, respectively). However, the remission from tobacco dependence group exhibited no significant changes in any of the biomarkers examined. They had lower scores on the Minnesota nicotine withdrawal scale than the hardcore smoker group (p=0.002). The hardcore smoker group had higher MHPG and BDNF levels than the non-smoker group (p=0.002 and p<0.001, respectively). Hardcore smokers experience severe nicotine withdrawal symptoms. Nicotine withdrawal is associated with catecholamine deficiency. The resulting withdrawal symptoms make quitting difficult for hardcore smokers. These hardcore smokers may require medication to compensate for the catecholamine deficit. Non-nicotinic medications such as bupropion, nortriptyline, or varenicline may be required to bolster the catecholamine deficit in hardcore smokers.

Bunch beam cooling

Electron cooling is used for damping both transverse and longitudinal oscillations of heavy particle. The cooling of bunch ion beam (with RF voltage on) is important part of experiments with inner target, ion collision system, stacking and RF manipulation. The short length of an ion bunch increases the peak luminosity, gives a start-time point for using of the time-of-flight methods and obtains a short extraction beam pulse. This article describes the review of last experiments with electron cooling carried out on the CSRm, CSRe (China) and COSY (Germany) storage rings. The accumulated experience may be used for the project of electron cooler on 2.5 MeV (NICA) and 0.5 MeV HIAF for obtaining high luminosity, depressing beam-beam effects and RF manipulation.

Real-time viability and apoptosis kinetic detection method of 3D multicellular tumor spheroids using the Celigo Image Cytometer.

The development of three-dimensional (3D) multicellular tumor spheroid models for cancer drug discovery research has increased in the recent years. The use of 3D tumor spheroid models may be more representative of the complex in vivo tumor microenvironments in comparison to two-dimensional (2D) assays. Currently, viability of 3D multicellular tumor spheroids has been commonly measured on standard plate-readers using metabolic reagents such as CellTiter-Glo® for end point analysis. Alternatively, high content image cytometers have been used to measure drug effects on spheroid size and viability. Previously, we have demonstrated a novel end point drug screening method for 3D multicellular tumor spheroids using the Celigo Image Cytometer. To better characterize the cancer drug effects, it is important to also measure the kinetic cytotoxic and apoptotic effects on 3D multicellular tumor spheroids. In this work, we demonstrate the use of PI and caspase 3/7 stains to measure viability and apoptosis for 3D multicellular tumor spheroids in real-time. The method was first validated by staining different types of tumor spheroids with PI and caspase 3/7 and monitoring the fluorescent intensities for 16 and 21 days. Next, PI-stained and nonstained control tumor spheroids were digested into single cell suspension to directly measure viability in a 2D assay to determine the potential toxicity of PI. Finally, extensive data analysis was performed on correlating the time-dependent PI and caspase 3/7 fluorescent intensities to the spheroid size and necrotic core formation to determine an optimal starting time point for cancer drug testing. The ability to measure real-time viability and apoptosis is highly important for developing a proper 3D model for screening tumor spheroids, which can allow researchers to determine time-dependent drug effects that usually are not captured by end point assays. This would improve the current tumor spheroid analysis method to potentially better identify more qualified cancer drug candidates for drug discovery research. © 2017 International Society for Advancement of Cytometry.

Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization

The no idle permutation flow shop scheduling problem (NIPFSP) is a popular NP-hard combinatorial optimization problem, which exists in several real world production processes. This study proposes a novel hybrid estimation of the distribution algorithm and cuckoo search (CS) algorithm (HEDA_CS) to solve the NIPFSP with the total tardiness criterion minimization. The problem model is built on the basis of the starting and ending time point of each job. A discrete solution representation method is applied in HEDA_CS to increase the operation efficiency. A novel probability matrix build method is also designed within the knowledge of the processing time matrix. The partially-mapped crossover operation works effectively during the CS phase. A suitable knowledge-based local search is also designed in the HEDA_CS to balance the exploitation and exploration. Finally, many simulations based on the new hard Ruiz benchmarks are conducted. Computational results demonstrate the effectiveness of the proposed HEDA_CS.

Position in the second stage of labour for women with epidural anaesthesia.

Position in the second stage of labour for women with epidural anaesthesia.