Photobiological studies of Baltic Sea phytoplankton

Phytoplankton ecology and productivity is one of the main branches of contemporary oceanographic research. Research groups in this branch have increasingly started to utilise bio-optical applications. My main research objective was to critically investigate the advantages and deficiencies of the fast repetition rate (FRR) fluorometry for studies of productivity of phytoplankton, and the responses of phytoplankton towards varying environmental stress. Second, I aimed to clarify the applicability of the FRR system to the optical environment of the Baltic Sea. The FRR system offers a highly dynamic tool for studies of phytoplankton photophysiology and productivity both in the field and in a controlled environment. The FRR metrics obtain high-frequency in situ determinations of the light-acclimative and photosynthetic parameters of intact phytoplankton communities. The measurement protocol is relatively easy to use without phases requiring analytical determinations. The most notable application of the FRR system lies in its potential for making primary productivity (PP) estimations. However, the realisation of this scheme is not straightforward. The FRR-PP, based on the photosynthetic electron flow (PEF) rate, are linearly related to the photosynthetic gas exchange (fixation of 14C) PP only in environments where the photosynthesis is light-limited. If the light limitation is not present, as is usually the case in the near-surface layers of the water column, the two PP approaches will deviate. The prompt response of the PEF rate to the short-term variability in the natural light field makes the field comparisons between the PEF-PP and the 14C-PP difficult to interpret, because this variability is averaged out in the 14C-incubations. Furthermore, the FRR based PP models are tuned to closely follow the vertical pattern of the underwater irradiance. Due to the photoacclimational plasticity of phytoplankton, this easily leads to overestimates of water column PP, if precautionary measures are not taken. Natural phytoplankton is subject to broad-waveband light. Active non-spectral bio-optical instruments, like the FRR fluorometer, emit light in a relatively narrow waveband, which by its nature does not represent the in situ light field. Thus, the spectrally-dependent parameters provided by the FRR system need to be spectrally scaled to the natural light field of the Baltic Sea. In general, the requirement of spectral scaling in the water bodies under terrestrial impact concerns all light-adaptive parameters provided by any active non-spectral bio-optical technique. The FRR system can be adopted to studies of all phytoplankton that possess efficient light harvesting in the waveband matching the bluish FRR excitation. Although these taxa cover the large bulk of all the phytoplankton taxa, one exception with a pronounced ecological significance is found in the Baltic Sea. The FRR system cannot be used to monitor the photophysiology of the cyanobacterial taxa harvesting light in the yellow-red waveband. These taxa include the ecologically-significant bloom-forming cyanobacterial taxa in the Baltic Sea.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Simple repetitive sequences in the genome: structure and functional significance

The current explosion of DNA sequence information has generated increasing evidence for the claim that noncoding repetitive DNA sequences present within and around different genes could play an important role in genetic control processes, although the precise role and mechanism by which these sequences function are poorly understood. Several of the simple repetitive sequences which occur in a large number of loci throughout the human and other eukaryotic genomes satisfy the sequence criteria for forming non-B DNA structures in vitro. We have summarized some of the features of three different types of simple repeats that highlight the importance of repetitive DNA in the control of gene expression and chromatin organization. (i) (TG/CA)n repeats are widespread and conserved in many loci. These sequences are associated with nucleosomes of varying linker length and may play a role in chromatin organization. These Z-potential sequences can help absorb superhelical stress during transcription and aid in recombination. (ii) Human telomeric repeat (TTAGGG)n adopts a novel quadruplex structure and exhibits unusual chromatin organization. This unusual structural motif could explain chromosome pairing and stability. (iii) Intragenic amplification of (CTG)n/(CAG)n trinucleotide repeat, which is now known to be associated with several genetic disorders, could down-regulate gene expression in vivo. The overall implications of these findings vis-à-vis repetitive sequences in the genome are summarized.

Triassic eclogite from northern Vietnam: inferences and geological significance

The first finding of low-temperature eclogites from the Indochina region is reported. The eclogites occur along the Song Ma Suture zone in northern Vietnam, which is widely regarded as the boundary between the South China and Indochina cratons. The major lithology of the area is pelitic schist that contains garnet and phengite with or without biotite, chloritoid, staurolite and kyanite, and which encloses blocks and lenses of eclogite and amphibolite. The eclogites commonly consist of garnet, omphacite, phengite, rutile, quartz and/or epidote with secondary barroisite. Omphacite is commonly surrounded by a symplectite of Na-poor omphacite and Na-rich plagioclase. In highly retrograded domains, diopside + tremolite + plagioclase symplectites replace the primary phases. Estimated peak-pressure metamorphic conditions based on isochemical phase diagrams for the eclogites are 2.1-2.2 GPa and 600-620 degrees C, even though thermobarometric results yield higher pressure and temperature conditions (2.6-2.8 GPa and 620-680 degrees C). The eclogites underwent a clockwise P-T trajectory with a post-peak-pressure increase of temperature to a maximum of > 750 degrees C at 1.7 GPa and a subsequent cooling during decompression to 650 degrees C and 1.3 GPa, which was followed by additional cooling before close-to-isothermal decompression to similar to 530 degrees C at 0.5 GPa. The surrounding pelitic schist (garnet-chloritoid-phengite) records similar metamorphic conditions (580-600 degrees C at 1.9-2.3 GPa) and a monazite chemical age of 243 +/- 4 Ma. A few monazite inclusions within garnet and the cores of some zoned monazite in garnet-phengite schist record an older thermal event (424 +/- 15 Ma). The present results indicate that the Indochina craton was deeply (> 70 km) subducted beneath the South China craton in the Triassic. The Silurian cores of monazite grains may relate to an older non-collisional event in the Indochina craton.

Deregulation of miR-126 expression in colorectal cancer pathogenesis and its clinical significance

In this study, we investigated the expression profiles and clinicopathological significance of miR-126 in large cohort of patients with colorectal cancers as well the cellular repercussions of miR-126 in colon cancer cells along with its targets in-vitro. Down regulation of miR-126 expression was associated with histological subtypes, peri-neural tumour infiltration, microsatellite instability and pathological staging of colorectal cancers (p<0.05). Low miR-126 expression was also associated with poorer survival in patients with colorectal cancer. Analysis of matched tissues from the same patient revealed that approximately 70% of the tested patients had similar levels of expression of miR-126 in primary cancer and cancer metastases in both lymph node and distant metastases. In addition, induced overexpression of miR-126 showed reduced cell proliferation, increased apoptosis and decreased accumulation of cells in the G0-G1 phase of the colon cancer cells. Furthermore, SW480(+miR-126) cells showed reduced BCL-2 and increased P53 protein expression. To conclude, deregulation of miR-126 in colorectal cancer at the tissue and cellular levels as well as its correlation with various clinicopathological parameters confirm the cancer suppressive role of miR-126 in colorectal cancer.

High P-T granulite relicts from the Imjingang belt, South Korea:Tectonic significance

This study presents a detailed description on crustal metamorphic signatures of garnet-clinopyroxene-quartz-rutile-bearing high P-T granulites, Samgot unit, Imajingang belt, northwestern Korean Peninsula that formed during Permo-Triassic regional metamorphism related to the amalgamation of East Asian continental fragments. Lenses and blocks of high P-T granulites and garnet-bearing leucosomes occur within mafic metamorphic rocks (mainly amphibolites). The mafic blocks comprise relicts of granoblastic garnet and clinopyroxene with medium-grained quartz and rutile. These relict mineral assemblages are confined to local micro-domains and constitute remnants of peak metamorphism. Plagioclase and amphibole form only as retrograde phases in medium ton coarse-grained moats that rim grain boundaries between relict peak mineral assemblages. This microstructure represents the reaction between garnet, clinopyroxene, quartz and rutile in the presence of melt to form amphibole, plagioclase and titanite with minor biotite. The leucosome domains consist of euhedral garnets within the quartz-K feldspar-plagioclase (granitic) matrix, probably representing peritectic garnet growth along with melting. The rare earth element (REE) composition of minerals also support the peritectic garnet growth with a positive Eu/Eu* (positive Eu anomaly), while the relict garnet shows a slight negative anomaly typical for high-grade granulites. The peak-metamorphic conditions calculated from thermodynamic modeling and compositional isopleths indicate a temperature around c. 900 degrees C at a pressure around c. 20 kbar. The present P-T path indicates a clear multi-stage decompression history with initial decompression and cooling followed by a stage of decompression during hydration possibly during Late Triassic exhumation. The results from this study together with the presence of eclogites from the Hongsung area suggest that the Imjingang area and the western Gyeonggi massif likely resided at crustal levels deeper than those of the eastern and southern part of the Gyeonggi massif. (C) 2009 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

Evolutionary Significance of Self-acylation Property in Acyl Carrier Proteins

Acyl carrier protein is an integral component of many cellular metabolic processes. A number of studies have reported self-acylation behavior in acyl carrier proteins. Although AM exhibit high levels of similarity in their primary and tertiary structures, self-acylation behavior is restricted to only some ACPs that can be classified into two major families based on their function. The first family of ACPs is involved in polyketide biosynthesis, whereas the second family participates in fatty acid synthesis. Facilitated by the growing number of genome sequences available for analyses, large-scale phylogenetic studies were used in these studies to uncover as to how self-acylation behavior of acyl carrier proteins is linked with the evolution of metabolic pathways in organisms. These studies show that self-acylation behavior in acyl carrier proteins was lost during the course of evolution, with certain organisms and organelles viz. plastids, retaining it for specified functions. (C) 2009 IUBMB IUBMB Life, 61(8): 853-859, 2009

An adaptive decomposition method for video

Scalable video coding (SVC) is an emerging standard built on the success of advanced video coding standard (H.264/AVC) by the Joint video team (JVT). Motion compensated temporal filtering (MCTF) and Closed loop hierarchical B pictures (CHBP) are two important coding methods proposed during initial stages of standardization. Either of the coding methods, MCTF/CHBP performs better depending upon noise content and characteristics of the sequence. This work identifies other characteristics of the sequences for which performance of MCTF is superior to that of CHBP and presents a method to adaptively select either of MCTF and CHBP coding methods at the GOP level. This method, referred as "Adaptive Decomposition" is shown to provide better R-D performance than of that by using MCTF or CRBP only. Further this method is extended to non-scalable coders.

Impeding the adaptive response of prostate cancer to androgen targeted therapies with relaxin receptor antagonist

Androgen deprivation and androgen targeted therapies (ATT) are established treatments for prostate cancer (PCa). Although initially effective, ATT induces an adaptive response that leads to treatment resistance. Increased expression of relaxin-2 (RLN2) is an important alteration in the adaptive response. RLN2 has a well described role in PCa cell proliferation, adhesion and tumour growth. The objectives of this study were to develop cell models for studies of RLN2 signalling and to implement in vitro assays for evaluating the therapeutic properties of the unique RLN2 receptor (RXFP1) antagonist

Significance of a short-term test drive for prospective plug-in electric vehicle consumers: An exploratory study

The test drive is a well-known step in car buying. In the emerging plug-in electric vehicle (PEV) market, however, the influence of a pre-purchase test drive on a consumer's inclination to purchase is unknown. Policy makers and industry participants both are eager to understand what factors motivate vehicle consumers at the point-of-sale. A number of researchers have used choice models to shed light on consumer perceptions of PEVs, and others have investigated consumer change in disposition toward a PEV over the course of a trial, wherein test driving a PEV may take place over a number of consecutive days, weeks or months. However, there is little written on the impact of a short-term test drive - a typical experience at dealerships or public "ride-and-drive" events. The impact of a typical test drive, often measured in minutes of driving, is not well understood. This paper first presents a synthesis of the literature on the effect of PEV test drives as they relate to consumer disposition toward PEVs. An analysis of data obtained from an Australian case study whereby attitudinal and stated preference data were collected pre- and post- test drive at public "ride-and-drive" event held Brisbane, Queensland in March 2014 using a custom-designed iPad application. Motorists' perceptions and choice preferences around PEVs were captured, revealing the relative importance of their experience behind the wheel. Using the Australian context as a case-study, this paper presents an exploratory study of consumers' stated preferences toward PEVs both before and after a short test drive.

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.