scholarly journals Serotonergic and catecholaminergic (dopaminergic) oscillations in the reproductive regulation of Japanese quail

2018 ◽  
Author(s):  
Suneeta Yadav ◽  
Chandra Mohini Chaturvedi
Keyword(s):  
Phase Relation ◽  
Time Interval ◽  
Specific Time ◽  
Serotonin Synthesis ◽  
Reproductive Regulation ◽  
Dopaminergic Drug ◽  
Temporal Phase ◽  
Gonadal Activity ◽  
Active Condition ◽  
Specific Phase

AbstractSpecific temporal phase relation of serotonergic and dopaminergic oscillations alters reproductive responses in many species. Aim of the study was to confirm whether effect of serotonergic drug (5-HTP) and dopaminergic drug (L-DOPA) is due to their conversion into serotonin and dopamine respectively or other products. For this study, PCPA (p-chlorophenylalanine, a long lasting inhibitor of serotonin synthesis), DDC (Diethyldithiocarbamate, which inhibits biosynthesis of nor-adrenaline), α-MT (Methyl-p-tyrosine, an inhibitor for the conversion of tyrosine to DOPA) and DOPS (Dihydroxyphenylserine, a specific precursor for noradrenaline) were used in different groups in addition to 5-HTP and L-DOPA given at specific time interval. Reproductive responses monitored at 10 weeks post treatment indicate that gonadal activity was significantly low in HTP:DOPA (8-hr quail), HTP+PCPA:DOPA and HTP:DOPA+DDC quail compare to control (S:S). However, gonadal activity of HTP:S(HTP control), S:DOPA(DOPA control) and HTP: α-MT+DOPS was not different from S:S control and remained in active condition. These findings indicate that it is not the dose of neurotransmitter precursor drugs (5-HTP and L-DOPA) and the neurotransmitters (serotonin and dopamine itself) that cause the effect, instead it is the function of interval between the drug administration which induces or entrains specific phase relation between serotonergic and dopaminergic oscillations. Further, gonadal suppression observed in HTP:DOPA, HTP+PCPA:DOPA and HTP:DOPA+DDC group three groups is not due to injection of 5-HTP or L-DOPA (alone) but due to conversion of administered 5-HTP into serotonin and conversion of L-DOPA (administered) into dopamine; not due to their further conversion into catecholamines other than dopamine i.e. noradrenaline or adrenaline.

scholarly journals Constructing Geographic Dictionary from Streaming Geotagged Tweets

2019 ◽  
Vol 8 (5) ◽  
pp. 216 ◽  
Author(s):  
Jeongwoo Lim ◽  
Naoko Nitta ◽  
Kazuaki Nakamura ◽  
Noboru Babaguchi
Keyword(s):  
Geographic Information ◽  
Time Interval ◽  
Specific Time ◽  
Time Intervals ◽  
Text Format ◽  
Short Text ◽  
Spatial Locality ◽  
Content Similarity ◽  
Types Of Information

Geographic information, such as place names with their latitude and longitude (lat/long), is useful to understand what belongs where. Traditionally, Gazetteers, which are constructed manually by experts, are used as dictionaries containing such geographic information. Recently, since people often post about their current experiences in a short text format to microblogs, their geotagged (tagged with lat/long information) posts are aggregated to automatically construct geographic dictionaries containing more diverse types of information, such as local products and events. Generally, the geotagged posts are collected within a certain time interval. Then, the spatial locality of every word used in the collected geotagged posts is examined to obtain the local words, representing places, events, etc., which are observed at specific locations by the users. However, focusing on a specific time interval limits the diversity and accuracy of the extracted local words. Further, bot accounts in microblogs can largely affect the spatial locality of the words used in their posts. In order to handle such problems, we propose an online method for continuously update the geographic dictionary by adaptively determining suitable time intervals for examining the spatial locality of each word. The proposed method further filters out the geotagged posts from bot accounts based on the content similarity among their posts to improve the quality of extracted local words. The constructed geographic dictionary is compared with different geographic dictionaries constructed by experts, crowdsourcing, and automatically by focusing on a specific time interval to evaluate its quality.

scholarly journals Training Temporal Word Embeddings with a Compass

2019 ◽  
Vol 33 ◽  
pp. 6326-6334 ◽  
Author(s):  
Valerio Di Carlo ◽  
Federico Bianchi ◽  
Matteo Palmonari
Keyword(s):  
State Of The Art ◽  
Word Meaning ◽  
Time Interval ◽  
Word Embeddings ◽  
Specific Time ◽  
Training Process ◽  
Text Corpora ◽  
Corpus Size ◽  
Generate Vector ◽  
Vector Representations

Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been proposed to generate vector representations of words that embed their meaning during a specific time interval. However, the training process used in these approaches is complex, may be inefficient or it may require large text corpora. As a consequence, these approaches may be difficult to apply in resource-scarce domains or by scientists with limited in-depth knowledge of embedding models. In this paper, we propose a new heuristic to train temporal word embeddings based on the Word2vec model. The heuristic consists in using atemporal vectors as a reference, i.e., as a compass, when training the representations specific to a given time interval. The use of the compass simplifies the training process and makes it more efficient. Experiments conducted using state-of-the-art datasets and methodologies suggest that our approach outperforms or equals comparable approaches while being more robust in terms of the required corpus size.

scholarly journals Local Aggregation Scheme for Data Collection in Periodic Sensor Network

2019 ◽  
Vol 9 (2) ◽  
pp. 3583-3588
Keyword(s):  
Sensor Node ◽  
Cluster Head ◽  
Temporal Correlation ◽  
Sensor Nodes ◽  
Time Interval ◽  
Specific Time ◽  
Aggregated Data ◽  
Redundant Data ◽  
Local Aggregation ◽  
Local Outlier

Data aggregation is an important technique for data collection & aggregation in WSN where sensor nodes sense the raw data and sends the aggregated data to the sink node. In a cluster based periodic network, sensor node senses the data on a specific time interval, performs local aggregation and send aggregated data to Cluster Head (CH). Various Local aggregation algorithms are used to remove redundant data at sensor nodes but local outlier detection problem is still unsolved. Therefore, a local aggregation algorithm has been proposed which uses the temporal correlation property of WSN to eliminate redundant and local outlier data which improves the data sent ratio and data quality. Sensor measurement is collected at different time interval of a sensor, exhibits temporal correlation because measurements varies with small or same difference (δ) and measurements are treated as similar measurements. In proposed local aggregation approach, each sensor node finds similar measurements of sensors with their frequency (number of occurrence) in a specific time interval (Temporal correlation). Set having higher frequency is selected and transmitted the average values of measurements that lie in the selected set to the cluster head. If sensors don’t detect any reading between intervals it simple send a message ‘data not found’ instead of sending empty set. In this way we delete redundant and local outliers. The experimental result shows that algorithm improves the data quality and data sent ratio by eliminating redundant data and local outliers

scholarly journals EFFICIENT MONITORING OF TIME SERIES DATA USING DYNAMIC ALERTING

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Time Series ◽  
Data Centers ◽  
Time Series Data ◽  
Series Data ◽  
Time Interval ◽  
Specific Time ◽  
Specific Data ◽  
Cloud Data ◽  
Data Collector ◽  
Cloud Data Centers

Network and Cloud Data Centers generate a lot of data every second, this data can be collected as a time series data. A time series is a sequence taken at successive equally spaced points in time, that means at a particular time interval to a specific time, the values of specific data that was taken is known as a data of a time series. This time series data can be collected using system metrics like CPU, Memory, and Disk utilization. The TICK Stack is an acronym for a platform of open source tools built to make collection, storage, graphing, and alerting on time series data incredibly easy. As a data collector, the authors are using both Telegraf and Collectd, for storing and analyzing data and the time series database InfluxDB. For plotting and visualizing, they use Chronograf along with Grafana. Kapacitor is used for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the system admin.

Access to care and patient satisfaction for surgically-treated breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 17010-17010
Author(s):  
G. A. Porter ◽  
J. M. Murdoch ◽  
K. M. Inglis ◽  
P. J. Veugelers
Keyword(s):  
Breast Cancer ◽  
Patient Satisfaction ◽  
Short Interval ◽  
Time Interval ◽  
Specific Time ◽  
Time Intervals ◽  
Satisfaction Questionnaire ◽  
Younger Women ◽  
Natural Logarithm ◽  
Abnormal Mammogram

17010 Background: Although recent studies have described timeliness of breast cancer (BC) care and its impact on outcomes, there is little data on patient perception of timeliness. This study examined the association between clinicodemographic factors, timeliness and patient satisfaction for surgically-treated BC patients across defined intervals of diagnosis (Dx) and treatment. Methods: All patients undergoing surgery for primary BC within a single Health District over 24 months were enrolled in a prospective consecutive cohort study. A comprehensive, standardized method of ascertaining specific time intervals, including a patient interview, was used to quantify the timeliness of presentation, Dx and treatment. A validated satisfaction questionnaire was applied to patients 2 weeks after surgery, and following chemotherapy. Multiple linear regression, using the natural logarithm of the time interval as the dependant variable, was performed to examine the association of factors and satisfaction with specific time intervals. Results: Among the 519 patients in the study, 317 (61%) were screen-detected and 202 (39%) presented symptomatically. Complete satisfaction questionnaire responses were obtained in 348 (67%). The median time intervals in days (interquartile range) were: abnormal screen to Dx - 33 (21–48); symptoms to Dx 44 (23–97); Dx to surgery - 31 (22–43); surgery to adjuvant chemotherapy 63 (49–73). On multivariate analysis, the interval from presentation (either abnormal mammogram or symptoms) to Dx was 33% longer for screen-detected patients (p<0.0001) and 38% longer for patients where more than one diagnostic test was performed (p=0.009). Moderate correlation was identified between patient satisfaction and both the intervals from presentation to Dx (r2=0.212;p<0.0001) and from Dx to surgery (r2=0.262;p<0.0001). Controlling for the length of these intervals, younger women (p=0.01) and those with a Dx made via screening (p=0.004) had significantly lower satisfaction scores. Conclusions: The timeliness of care for BC involves several defined components; variations in the relatively short interval from Dx to surgery appeared to have most impact on patient satisfaction. Younger women and those diagnosed via screening were less satisfied with their access to timely care. No significant financial relationships to disclose.

scholarly journals Dynamic Reliability Evaluation of Linear Consecutive k-out-of-n0: F System With Multi-State Components

2018 ◽  
Vol 22 ◽  
pp. 01057
Author(s):  
Gökhan GÖKDERE ◽  
Mehmet GÜRCAN
Keyword(s):  
Operating Performance ◽  
Reliability Evaluation ◽  
Technical System ◽  
Time Interval ◽  
Specific Time ◽  
Time Intervals ◽  
Engineering Applications ◽  
Dynamic Reliability ◽  
Reliability Calculation ◽  
The Status

In engineering applications, analyzing a technical system vary according to the operating principles of the system. In some situations, the status of the system is a function of stresses which act on the system and cause degradation. In order to efficiently analysis the reliability of a system which operates under stress, assigning the various states to the components depending on their operating performance is very important. In this paper, we have investigated the linear consecutive k-out-of-n: F system and assigned multiple states to its components. Due to the reason, the operating performance of the components can easily be controlled. Apart from that the reliability of the system depending on the states of its components can be calculated at any time interval. In the numerical example, the states of the components and the reliability calculation of the system at specific time intervals are shown clearly.

Optimal mission duration for systems subject to shocks and internal failures

2017 ◽  
Vol 232 (1) ◽  
pp. 82-91 ◽  
Author(s):  
Maxim Finkelstein ◽  
Gregory Levitin
Keyword(s):  
Failure Probability ◽  
Premature Termination ◽  
Cost Effective ◽  
Optimal Time ◽  
Time Interval ◽  
System Failure ◽  
Repairable Systems ◽  
Specific Time ◽  
Mission Time ◽  
Mission Duration

At many instances, it is more cost-effective to terminate operation of a system than to wait for its failure or completion of a mission. Usually, completion of a mission (contract) results in an additional reward, whereas premature termination results in a penalty. However, the system failure during the mission can incur considerable expenses. As the failure probability increases with the mission time, this can make the mission completion too risky and not beneficial. This article analyzes the optimal mission duration for non-repairable systems subject to shocks and internal failures. Under certain assumptions, an optimal time of mission termination is obtained. It is shown that, if for some reason, the termination is not technically possible at this optimal time, the mission should be terminated within a specific time interval and, if this is not possible, it should not be terminated beyond this interval. Illustrative examples are presented.

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