scholarly journals GC and Repeats Profiling along Chromosomes—The Future of Fish Compositional Cytogenomics

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 50
Author(s):  
Dominik Matoulek ◽  
Veronika Borůvková ◽  
Konrad Ocalewicz ◽  
Radka Symonová
Keyword(s):  
Banding Pattern ◽  
Sliding Window ◽  
Fish Cytogenetics ◽  
Recent Increase ◽  
Fish Chromosomes ◽  
Fish Remains ◽  
Window Approach ◽  
Tool Profiles ◽  
Cytogenetic Methods ◽  
Chromosome Level

The study of fish cytogenetics has been impeded by the inability to produce G-bands that could assign chromosomes to their homologous pairs. Thus, the majority of karyotypes published have been estimated based on morphological similarities of chromosomes. The reason why chromosome G-banding does not work in fish remains elusive. However, the recent increase in the number of fish genomes assembled to the chromosome level provides a way to analyse this issue. We have developed a Python tool to visualize and quantify GC percentage (GC%) of both repeats and unique DNA along chromosomes using a non-overlapping sliding window approach. Our tool profiles GC% and simultaneously plots the proportion of repeats (rep%) in a color scale (or vice versa). Hence, it is possible to assess the contribution of repeats to the total GC%. The main differences are the GC% of repeats homogenizing the overall GC% along fish chromosomes and a greater range of GC% scattered along fish chromosomes. This may explain the inability to produce G-banding in fish. We also show an occasional banding pattern along the chromosomes in some fish that probably cannot be detected with traditional qualitative cytogenetic methods.

scholarly journals Sliding-Window Thompson Sampling for Non-Stationary Settings

2020 ◽  
Vol 68 ◽  
pp. 311-364
Author(s):  
Francesco Trovo ◽  
Stefano Paladino ◽  
Marcello Restelli ◽  
Nicola Gatti
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Sliding Window ◽  
Upper Bounds ◽  
Decision Problems ◽  
Sequential Decision ◽  
Thompson Sampling ◽  
The Past ◽  
Real World Applications ◽  
Window Approach

Multi-Armed Bandit (MAB) techniques have been successfully applied to many classes of sequential decision problems in the past decades. However, non-stationary settings -- very common in real-world applications -- received little attention so far, and theoretical guarantees on the regret are known only for some frequentist algorithms. In this paper, we propose an algorithm, namely Sliding-Window Thompson Sampling (SW-TS), for nonstationary stochastic MAB settings. Our algorithm is based on Thompson Sampling and exploits a sliding-window approach to tackle, in a unified fashion, two different forms of non-stationarity studied separately so far: abruptly changing and smoothly changing. In the former, the reward distributions are constant during sequences of rounds, and their change may be arbitrary and happen at unknown rounds, while, in the latter, the reward distributions smoothly evolve over rounds according to unknown dynamics. Under mild assumptions, we provide regret upper bounds on the dynamic pseudo-regret of SW-TS for the abruptly changing environment, for the smoothly changing one, and for the setting in which both the non-stationarity forms are present. Furthermore, we empirically show that SW-TS dramatically outperforms state-of-the-art algorithms even when the forms of non-stationarity are taken separately, as previously studied in the literature.

A sliding-window approach for improved VMAT dose calculation accuracy

2020 ◽  
Vol 45 (3) ◽  
pp. 197-201
Author(s):  
Haitham N Alahmad ◽  
Ji-Yeon Park ◽  
Nicholas J Potter ◽  
Bo Lu ◽  
Guanghua Yan ◽  
...  
Keyword(s):  
Dose Calculation ◽  
Sliding Window ◽  
Window Approach

scholarly journals Reducing False Negative Reads in RFID Data Streams Using an Adaptive Sliding-Window Approach

Sensors ◽  
2012 ◽  
Vol 12 (4) ◽  
pp. 4187-4212 ◽  
Author(s):  
Libe Valentine Massawe ◽  
Johnson D. M. Kinyua ◽  
Herman Vermaak
Keyword(s):  
Data Streams ◽  
False Negative ◽  
Sliding Window ◽  
Rfid Data ◽  
Window Approach

scholarly journals Remember the past: A comparison of time-adaptive training schemes for non-homogeneous regression

2020 ◽  
Author(s):  
Moritz N. Lang ◽  
Sebastian Lerch ◽  
Georg J. Mayr ◽  
Thorsten Simon ◽  
Reto Stauffer ◽  
...  
Keyword(s):  
Sliding Window ◽  
Predictive Performance ◽  
Training Data ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Adaptive Training ◽  
Weather Forecasts ◽  
The Past ◽  
Training Schemes ◽  
Window Approach

<p>Non-homogeneous regression is a frequently-used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across Central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing.</p><p>The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that "remembering the past" from multiple years of training data is typically also superior to the classical sliding-window when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.</p>

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