Detection and characterization of dispersive North Atlantic Right whale up calls in a shallow-water environment using a region-based active contour model

For mitigation and monitoring of endangered North Atlantic Right whales, identifying their presence from their vocalisations is the essential principle of Passive Acoustic Monitoring (PAM) system. The most common vocalisations of Right whales are characterized as frequency modulated up sweeps with duration of ~1s and a frequency range from 50 to 200 Hz. These species favour shallow waters during their seasonal migration. Such shallow-water acoustic environments act as a waveguide and cause the mono-component up call emitted by the vocalising whale to become a dispersive multimode signal with different time arrivals and relative energy at the receiver. As well as the natural variation in species vocalisation, another variation in the characteristic features extracted from dispersive calls results from the dispersion effect. In this thesis normal mode modelling was used to better understand the influence of shallow water on the parameters of Right whale up calls. Also, examples from data recorded in Cape Cod Bay shows the dispersion influence. Visual scanning indicates that 93% of the data are dispersive and the first mode in 85% of these calls is less excited than the second mode due to the dependence of mode excitation on the depth of the calling whale. The discrimination between the Northern Right whale up calls and background noise has been investigated using the Support Vector Machine classifier. To perform this task, a region-based active contour segmentation method is proposed. This approach is based on the evolution of the contour within the spectrogram image searching for the uniformity of the target object. In this work both synthesized data based on typical Right whale vocalisations and real data recorded in Cape Cod Bay are used to evaluate the proposed method. To illustrate the variation in the data caused by the channel effect we compare the descriptive statistics of the call duration using both the single mode and the multi-mode approaches. The single mode analysis was performed by extracting the frequency contour of the first mode

Systems Biology of Mitosis

In mitosis, `surveillance control mechanisms' regulate transitions across the several stages of cell division. The so called `Mitotic-Spindle-Assembly-Checkpoint (MSAC)' and `Exit-From-Mitosis (EFM)' are examples of such mechanisms. MSAC ensures the correct segregation of chromosomes by preventing cell-cycle progression until all chromosomes have made proper bipolar attachments to the mitotic spindle through their kinetochores. EFM ensures that each of the two daughter nuclei receives one copy of each chromosome. Both mechanisms are seemingly regulated by the so called `Anaphase-Promoting-Complex (APC)', bound, in turn, to either `Cdc20' or `Cdh1', which are associated regulatory proteins. APC remains inactive during metaphase. In the transition from metaphase to anaphase, and only after all chromosomes are attached, a newly formed `APC:Cdc20' complex mediates the ubiquitination and degradation of the protein `Securin'; this leads, in turn, to the activation of the protein `Separase', the dissolution of the so called `Cohesin Complex', and, eventually, to chromatid separation. `APC:Cdc20' also mediates the initial phase of `Cyclin B' proteolysis. In the transition from anaphase to telophase, APC:Cdh1 completely ubiquitinates `Cyclin B', thus inactivating a protein called `CyclinB:Cdk1-Mitotic-Kinase' and triggering the exit from mitosis. Both MSAC and EFM prevent chromosome miss-segregation and aneuploidy, and their failure eventually leads to cell death; both mechanisms have been implicated in cancer

Monitoring spindle orientation: Spindle position checkpoint in charge

Every cell division in budding yeast is inherently asymmetric and counts on the correct positioning of the mitotic spindle along the mother-daughter polarity axis for faithful chromosome segregation. A surveillance mechanism named the spindle position checkpoint (SPOC), monitors the orientation of the mitotic spindle and prevents cells from exiting mitosis when the spindle fails to align along the mother-daughter axis. SPOC is essential for maintenance of ploidy in budding yeast and similar mechanisms might exist in higher eukaryotes to ensure faithful asymmetric cell division. Here, we review the current model of SPOC activation and highlight the importance of protein localization and phosphorylation for SPOC function

The effects of board of directors' characteristics and risk management committee on the financial performance of listed banks in Iraq

Good corporate governance practices and setting up of an independent risk management committee are considered as crucial in decreasing risk for investors and enhancing performance. This study examined the effects of board of directors’ characteristics and risk management committee’s attributes on the financial performance of listed banks in Iraq. The research used secondary data obtained from the data stream and annual report of all banks listed in the Iraq stock exchange for the year 2017-2019 with 63 firm-year observations. In addition, the regression was based on panel Corrected Standard Error. The result shows that the size and the board of directors and its independence exhibit significant and negative associations the banks’ performance. However, the board of directors’ financial expertise and the independence of the risk management committee had positive yet insignificant associations with performance. This study provided suggestions for future research work and several recommendations for regulators and the Iraqi banking industry

In-Silico Modeling of the Mitotic Spindle Assembly Checkpoint

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the "Dissociation" and the "Convey" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding

Bioinformatics Analysis of the Periodicity in Proteins with Coiled-Coil Structure—Enumerating All Decompositions of Sequence Periods

A coiled coil is a structural motif in proteins that consists of at least two α-helices wound around each other. For structural stabilization, these α-helices form interhelical contacts via their amino acid side chains. However, there are restrictions as to the distances along the amino acid sequence at which those contacts occur. As the spatial period of the α-helix is 3.6, the most frequent distances between hydrophobic contacts are 3, 4, and 7. Up to now, the multitude of possible decompositions of α-helices participating in coiled coils at these distances has not been explored systematically. Here, we present an algorithm that computes all non-redundant decompositions of sequence periods of hydrophobic amino acids into distances of 3, 4, and 7. Further, we examine which decompositions can be found in nature by analyzing the available data and taking a closer look at correlations between the properties of the coiled coil and its decomposition. We find that the availability of decompositions allowing for coiled-coil formation without putting too much strain on the α-helix geometry follows an oscillatory pattern in respect of period length. Our algorithm supplies the basis for exploring the possible decompositions of coiled coils of any period length

A novel method for achieving an optimal classification of the proteinogenic amino acids

The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find “the” optimal classification, they have several shortcomings, such as the lack of efficiency and interpretability or an unnecessarily high number of discriminating features. In this study, we propose a novel method involving a repeated binary separation via a minimum amount of five features (such as hydrophobicity or volume) expressed by numerical values for amino acid characteristics. The features are extracted from the AAindex database. By simple separation at the medians, we successfully derive the five properties volume, electron–ion-interaction potential, hydrophobicity, α-helix propensity, and π-helix propensity. We extend our analysis to separations other than by the median. We further score our combinations based on how natural the separations are

An intelligent differential protection of power transformer based on artificial neural network

This paper describes the application of artificial neural network (ANN) techniques for protecting small power transformer 2 kVA (Terco type). ANN network trained according to the primary and secondary currents data under NN tool, this network performs as a function of differential protection relay. Symmetrical and unsymmetrical faults are analyzed using Matlab environment. ANN network senses the difference in the internal current of both transformer sides and sending a trip to the circuit breaker (CB) at moment of fault occurrence. All the voltages and the currents waveforms affected with the fault and the response time increased according to this technique. Finally, the trip signal and the quick disconnect time were improved according to ANN technique