A connection between extracellular matrix and hormonal signals during the development of the human fetal adrenal gland

The human adrenal cortex, involved in adaptive responses to stress, body homeostasis and secondary sexual characters, emerges from a tightly regulated development of a zone-specific secretion pattern during fetal life. Its development during fetal life is critical for the well being of pregnancy, the initiation of delivery, and even for an adequate adaptation to extra-uterine life. As early as from the sixth week of pregnancy, the fetal adrenal gland is characterized by a highly proliferative zone at the periphery, a concentric migration accompanied by cell differentiation (cortisol secretion) and apoptosis in the central androgen-secreting fetal zone. After birth, a strong reorganization occurs in the adrenal gland so that it better fulfills the newborn's needs, with aldosterone production in the external zona glomerulosa, cortisol secretion in the zona fasciculata and androgens in the central zona reticularis. In addition to the major hormonal stimuli provided by angiotensin II and adrenocorticotropin, we have tested for some years the hypotheses that such plasticity may be under the control of the extracellular matrix. A growing number of data have been harvested during the last years, in particular about extracellular matrix expression and its putative role in the development of the human adrenal cortex. Laminin, collagen and fibronectin have been shown to play important roles not only in the plasticity of the adrenal cortex, but also in cell responsiveness to hormones, thus clarifying some of the unexplained observations that used to feed controversies.

Periodic mechanical stress activates MEK1/2-ERK1/2 mitogenic signals in rat chondrocytes through Src and PLCγ1

The mitogenic effects of periodic mechanical stress on chondrocytes have been studied extensively but the mechanisms whereby chondrocytes sense and respond to periodic mechanical stress remain a matter of debate. We explored the signal transduction pathways of chondrocyte proliferation and matrix synthesis under periodic mechanical stress. In particular, we sought to identify the role of the MEK1/2-ERK1/2 signaling pathway in chondrocyte proliferation and matrix synthesis following cyclic physiologic mechanical compression. Under periodic mechanical stress, both rat chondrocyte proliferation and matrix synthesis were significantly increased (P < 0.05) and were associated with increases in the phosphorylation of Src, PLCγ1, MEK1/2, and ERK1/2 (P < 0.05). Pretreatment with the MEK1/2-ERK1/2 selective inhibitor, PD98059, and shRNA targeted to ERK1/2 reduced periodic mechanical stress-induced chondrocyte proliferation and matrix synthesis (P < 0.05), while the phosphorylation levels of Src-Tyr418 and PLCγ1-Tyr783 were not inhibited. Proliferation, matrix synthesis and phosphorylation of MEK1/2-Ser217/221 and ERK1/2-Thr202/Tyr204 were inhibited after pretreatment with the PLCγ1 inhibitor U73122 in chondrocytes in response to periodic mechanical stress (P < 0.05), while the phosphorylation site of Src-Tyr418 was not affected. Inhibition of Src activity with PP2 and shRNA targeted to Src abrogated chondrocyte proliferation and matrix synthesis (P < 0.05) and attenuated PLCγ1, MEK1/2 and ERK1/2 activation in chondrocytes subjected to periodic mechanical stress (P < 0.05). These findings suggest that periodic mechanical stress promotes chondrocyte proliferation and matrix synthesis in part through the Src-PLCγ1-MEK1/2-ERK1/2 signaling pathway, which links these three important signaling molecules into a mitogenic cascade.

Kaurene diterpene induces apoptosis in U87 human malignant glioblastoma cells by suppression of anti-apoptotic signals and activation of cysteine proteases

Gliomas are the most common and malignant primary brain tumors in humans. Studies have shown that classes of kaurene diterpene have anti-tumor activity related to their ability to induce apoptosis. We investigated the response of the human glioblastoma cell line U87 to treatment with ent-kaur-16-en-19-oic acid (kaurenoic acid, KA). We analyzed cell survival and the induction of apoptosis using flow cytometry and annexin V staining. Additionally, the expression of anti-apoptotic (c-FLIP and miR-21) and apoptotic (Fas, caspase-3 and caspase-8) genes was analyzed by relative quantification (real-time PCR) of mRNA levels in U87 cells that were either untreated or treated with KA (30, 50, or 70 µM) for 24, 48, and 72 h. U87 cells treated with KA demonstrated reduced viability, and an increase in annexin V- and annexin V/PI-positive cells was observed. The percentage of apoptotic cells was 9% for control cells, 26% for cells submitted to 48 h of treatment with 50 µM KA, and 31% for cells submitted to 48 h of treatment with 70 µM KA. Similarly, in U87 cells treated with KA for 48 h, we observed an increase in the expression of apoptotic genes (caspase-8, -3) and a decrease in the expression of anti-apoptotic genes (miR-21 and c-FLIP). KA possesses several interesting properties and induces apoptosis through a unique mechanism. Further experiments will be necessary to determine if KA may be used as a lead compound for the development of new chemotherapeutic drugs for the treatment of primary brain tumors.

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Histone H2B-R95A mutant identifies the pheromone pathway that signals cell cycle arrest during rapamycin response

La rapamycine est un immunosuppresseur utilisé pour traiter plusieurs types de maladies dont le cancer du rein. Son fonctionnement par l’inhibition de la voie de Tor mène à des changements dans des processus physiologiques, incluant le cycle cellulaire. Chez Saccharomyces cerevisiae, la rapamycine conduit à une altération rapide et globale de l’expression génique, déclenchant un remodelage de la chromatine. Nous proposons que les modifications des histones peuvent jouer un rôle crucial dans le remodelage de la chromatine en réponse à la rapamycine. Notre objectif principal est d’identifier d’une banque de mutants d’histone les variantes qui vont échouer à répondre à la rapamycine dans une tentative de réaliser une caractérisation des modifications d’histone critiques pour la réponse à cette drogue. Ainsi, nous avons réalisé un criblage d’une banque de mutants d’histone et identifié plusieurs mutants d‘histone dont la résistance à la rapamycine a été altérée. Nous avons caractérisé une de ces variantes d’histone, à savoir H2B, qui porte une substitution de l’alanine en arginine en position 95 (H2B-R95A) et démontré que ce mutant est extrêmement résistant à la rapamycine, et non à d’autres drogues. Des immunoprécipitations ont démontré que H2B-R95A est défectueux pour former un complexe avec Spt16, un facteur essentiel pour la dissociation de H2A et H2B de la chromatine, permetant la réplication et la transcription par les ADN et ARN polymérases, respectivement. Des expériences de ChIP-Chip et de micropuce ont démontré que l’arginine 95 de H2B est requise pour recruter Spt16 afin de permettre l’expression d’une multitude de gènes, dont certains font partie de la voie des phéromones. Des évidences seront présentées pour la première fois démontrant que la rapamycine peut activer la voie des phéromones et qu’une défectuosité dans cette voie cause la résistante à cette drogue.

Phase modulation of microwave signals using point contact Germanium signal diodes

A forward - biased point contact germanium signal diode placed inside a waveguide section along the E -vector is found to introduce significant phase shift of microwave signals . The usefulness of the arrangement as a phase modulator for microwave carriers is demonstrated. While there is a less significant amplitude modulation accompanying phase modulation , the insertion losses are found to be negligible. The observations can be explained on the basis of the capacitance variation of the barrier layer with forward current in the diode

Magnetic field-induced cluster formation and variation of magneto-optical signals in zinc-substituted ferrofluids

Fine magnetic particles (size≅100 Å) belonging to the series ZnxFe1−xFe2O4 were synthesized by cold co-precipitation methods and their structural properties were evaluated using X-ray diffraction. Magnetization studies have been carried out using vibrating sample magnetometry (VSM) showing near-zero loss loop characteristics. Ferrofluids were then prepared employing these fine magnetic powders using oleic acid as surfactant and kerosene as carrier liquid by modifying the usually reported synthesis technique in order to induce anisotropy and enhance the magneto-optical signals. Liquid thin films of these fluids were prepared and field-induced laser transmission through these films was studied. The transmitted light intensity decreases at the centre with applied magnetic field in a linear fashion when subjected to low magnetic fields and saturate at higher fields. This is in accordance with the saturation in cluster formation. The pattern exhibited by these films in the presence of different magnetic fields was observed with the help of a CCD camera and was recorded photographically.

Significance of Time Scales in Nonlinear Dynamical Analysis of Electroencephalogram Signals

We propose to show in this paper, that the time series obtained from biological systems such as human brain are invariably nonstationary because of different time scales involved in the dynamical process. This makes the invariant parameters time dependent. We made a global analysis of the EEG data obtained from the eight locations on the skull space and studied simultaneously the dynamical characteristics from various parts of the brain. We have proved that the dynamical parameters are sensitive to the time scales and hence in the study of brain one must identify all relevant time scales involved in the process to get an insight in the working of brain.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics

Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Magnetic field-induced cluster formation and variation of magneto-optical signals in zinc-substituted ferrofluids

Fine magnetic particles (sizeffi100A ˚ ) belonging to the series ZnxFe1 xFe2O4 were synthesized by cold co-precipitation methods and their structural properties were evaluated using X-ray diffraction. Magnetization studies have been carried out using vibrating sample magnetometry (VSM) showing near-zero loss loop characteristics. Ferrofluids were then prepared employing these fine magnetic powders using oleic acid as surfactant and kerosene as carrier liquid by modifying the usually reported synthesis technique in order to induce anisotropy and enhance the magneto-optical signals. Liquid thin films of these fluids were prepared and field-induced laser transmission through these films was studied. The transmitted light intensity decreases at the centre with applied magnetic field in a linear fashion when subjected to low magnetic fields and saturate at higher fields. This is in accordance with the saturation in cluster formation. The pattern exhibited by these films in the presence of different magnetic fields was observed with the help of a CCD camera and was recorded photographically

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

A Hybrid Architecture for Recognising Speech Signals in Malayalam

Speech is the primary, most prominent and convenient means of communication in audible language. Through speech, people can express their thoughts, feelings or perceptions by the articulation of words. Human speech is a complex signal which is non stationary in nature. It consists of immensely rich information about the words spoken, accent, attitude of the speaker, expression, intention, sex, emotion as well as style. The main objective of Automatic Speech Recognition (ASR) is to identify whatever people speak by means of computer algorithms. This enables people to communicate with a computer in a natural spoken language. Automatic recognition of speech by machines has been one of the most exciting, significant and challenging areas of research in the field of signal processing over the past five to six decades. Despite the developments and intensive research done in this area, the performance of ASR is still lower than that of speech recognition by humans and is yet to achieve a completely reliable performance level. The main objective of this thesis is to develop an efficient speech recognition system for recognising speaker independent isolated words in Malayalam.