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It has been 30 (!!) years since I began working on zebrafish pigment cells, as a postdoc in the laboratory of Prof. Christiane Nüsslein‐Volhard. There, I participated in the first large‐scale mutagenesis screen in zebrafish, focusing on pigment cell mutant phenotypes. The isolation of colourless, shady, parade and choker mutants allowed us (as a postdoc in Prof. Judith Eisen's laboratory, and then in my own laboratory at the University of Bath since 1997) to pursue my ambition to address long‐standing problems in the neural crest field. Thus, we have studied how neural crest cells choose individual fates, resulting in our recent proposal of a new, and potentially unifying, model which we call Cyclical Fate Restriction, as well as addressing how pigment cell patterns are generated. A key feature of our work in the last 10 years has been the use of mathematical modelling approaches to clarify our biological models and to refine our interpretations. None of this would have been possible without a hugely talented group of laboratory members and other collaborators from around the world—it has been, and I am sure will continue to be, a pleasure and privilege to work with you all! [ABSTRACT FROM AUTHOR]

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Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Seasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data residing in databases that vary in size, type, and provenance. Methods We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations. Results Across all databases and levels of significance, concordance ranged from 20.2 to 40.2%. Across all databases and levels of significance, the proportion of time series classified seasonal ranged from 4.9 to 88.3%. For each database and level of significance, we computed the difference between the maximum and minimum proportion of time series classified seasonal by all methods. The median within-database difference was 54.8, 34.7, and 39.8%, for p

Background: Seasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data residing in databases that vary in size, type, and provenance.Methods: We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations.Results: Across all databases and levels of significance, concordance ranged from 20.2 to 40.2%. Across all databases and levels of significance, the proportion of time series classified seasonal ranged from 4.9 to 88.3%. For each database and level of significance, we computed the difference between the maximum and minimum proportion of time series classified seasonal by all methods. The median within-database difference was 54.8, 34.7, and 39.8%, for p < 0.01, 0.05, and 0.1, respectively.Conclusion: Methods of binary seasonality classification when applied to time series derived from diagnosis codes in observational health data produce inconsistent results. The methods exhibit considerable discord within all databases, implying that the discord is a result of the difference between the methods themselves and not due to the choice of database. The results indicate that researchers relying on automated methods to assess the seasonality of time series derived from diagnosis codes in observational data should be aware that the methods are not interchangeable and thus the choice of method can affect the generalizability of their work. Seasonality determination is highly dependent on the method chosen. [ABSTRACT FROM AUTHOR]

Different mechanisms of glacial-interglacial cycles and of the current global warming are considered and discussed: Milankovitch astronomical cycles, self-organization processes in Earth's geospheres, variation of Solar activity, increased concentration of greenhouse gases. It is pointed out that the current global warming cycle has started, according to data presented in J.R. Petit, at al., (1999, 2001), thousands of years before the first machines, even very primitive, were made by people. It is pointed out also that release of CH4 gas into atmosphere is an irreversible, uncontrolled process triggered by global warming. It is hypothesized that the planetary overheated atmosphere is in a state similar to "boiling". This explains the increased number and severity of different natural catastrophes -- hurricanes, floods, droughts. It is also shown that in each cycle the cooling down of atmosphere follows the Newton's Law of cooling suggesting a clear conceptual view of the Physics of this process. Finally, a mechanism explaining sharp the rise of temperature of atmosphere at the end of each glacial period is suggested and evaluated. [ABSTRACT FROM AUTHOR]

The main objective of this study is to develop models that can forecast traffic accidents, injuries and fatalities in developing countries. Consequently, this could set the first step in enhancing traffic safety measures as well as to help planning for the future. Moreover, results of this investigation may assist decision and law makers at transportation and traffic agencies to come up with resolutions for the increasing traffic crashes and consequently loss of lives, properties and the high cost involved. The forecasting models were developed using the well-known time series analysis. Each time series model was decomposed into its components (trend, cyclical, seasonal and irregular components). In conclusion, the forecasting by time series analysis explained a significant proportion of the variability in the dependent variables (accidents, injuries and fatalities) as shown by the relatively acceptable coefficients of determination (R²). [ABSTRACT FROM AUTHOR]

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods.

Introduction: Mastalgia was the most common symptom in patients attending a breast clinic. The two most common types of mastalgia were (cyclical and non-cyclical). The common cause of cyclical was Physiological changes due to premenstrual tension syndrome fibrocystic changes or fibroadenosis and (Aberrations in the Normal Development and Involution of the breast ). Non-cyclical diseases are mainly benign rather than malignant. Solid masses (fibroadenomas), cysts, infections, abscesses, trauma, and nipple discharge (hormones) are rare diseases associated with breast cancer. The aim of this study is to study the incidence of mastalgia, types (cyclical and non-cyclical) and pathological types of mastalgia (benign and malignant pathologies)., Material and Methods: A cross-sectional descriptive study of mastalgia was carried out at the Breast Center Department of Maternity Teaching Hospital located in Erbil, from January 2014 to September 2015. A total of 150 breast pain cases (available clinical data) were studied in detail., Results: There are two types of mastalgia based on triple assessments, these are as follows, cyclical 90 cases (60%) and non-cyclical 60 cases (40%). Fibrocystic breast changes or fibroadenosis 35 cases (23.3%) and thirdly nipple discharges as part of fibrocystic (fibroadenosis) five cases (3.3%). Non-cyclical mastalgia was subdivided into benign breast pathologies, fibroadenoma 30 cases (20%), breast cyst 10 cases (6.7%), nipple discharge (hormonal) five cases (3.3%), mastitis three cases (2%) abscess two cases (1.3%), fat necrosis one case (0.7%), and malignant breast pathologies, cancer three cases (2%). Clinicopathological correlations in the current study were highly significant ( P <0.005)., Conclusion: Not all discomfort or pain can be diagnosed as mastalgia; occasionally, chest wall, referral pain, and systemic causes can mimic mastalgia. These results highlight the importance of differentiating between cyclical and non-cyclical mastalgia using clinical assessment, sonography, mammography, and blood investigations. Most mastalgia cases are benign, with cyclical mastalgia primarily related to hormonal changes during the menstrual cycle., Competing Interests: The authors declare that they have no conflicts of interest.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods. [ABSTRACT FROM AUTHOR]