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.

Realities on the ground: Human trafficking constructs in Thailand and the implications on NGO anti-trafficking prevention strategies

This thesis is a qualitative study that examines how participating staff from Thai based non-governmental organisations interpret and construct the notion of human trafficking; and how this impacts prevention methods. The research examined the impact of different socio-cultural, political and religious ideologies on anti-trafficking prevention and programme implementation. Findings highlighted that while a 'raid and rescue' approach to human trafficking was widely recognised by donors and the media; it was not suitable or complementary to sustainable and community focused anti-trafficking models. Rather, a holistic approach that considers contextual factors and inter-agency collaboration is essential for effective anti-trafficking prevention strategies.

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.

Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis

Tuberculosis continues to be a major health challenge, warranting the need for newer strategies for therapeutic intervention and newer approaches to discover them. Here, we report the identification of efficient metabolism disruption strategies by analysis of a reactome network. Protein-protein dependencies at a genome scale are derived from the curated metabolic network, from which insights into the nature and extent of inter-protein and inter-pathway dependencies have been obtained. A functional distance matrix and a subsequent nearness index derived from this information, helps in understanding how the influence of a given protein can pervade to the metabolic network. Thus, the nearness index can be viewed as a metabolic disruptability index, which suggests possible strategies for achieving maximal metabolic disruption by inhibition of the least number of proteins. A greedy approach has been used to identify the most influential singleton, and its combination with the other most pervasive proteins to obtain highly influential pairs, triplets and quadruplets. The effect of deletion of these combinations on cellular metabolism has been studied by flux balance analysis. An obvious outcome of this study is a rational identification of drug targets, to efficiently bring down mycobacterial metabolism.

Division of the Salmonella-Containing Vacuole and Depletion of Acidic Lysosomes in Salmonella-Infected Host Cells Are Novel Strategies of Salmonella enterica To Avoid Lysosomes

Salmonella has evolved several strategies to counteract intracellular microbicidal agents like reactive oxygen and nitrogen species. However, it is not yet clear how Salmonella escapes lysosomal degradation. Some studies have demonstrated that Salmonella can inhibit phagolysosomal fusion, whereas other reports have shown that the Salmonella-containing vacuole (SCV) fuses/interacts with lysosomes. Here, we have addressed this issue from a different perspective by investigating if the infected host cell has a sufficient quantity of lysosomes to target Salmonella. Our results suggest that SCVs divide along with Salmonella, resulting in a single bacterium per SCV. As a consequence, the SCV load per cell increases with the division of Salmonella inside the host cell. This demands more investment from the host cell to counteract Salmonella. Interestingly, we observed that Salmonella infection decreases the number of acidic lysosomes inside the host cell both in vitro and in vivo. These events potentially result in a condition in which an infected cell is left with insufficient acidic lysosomes to target the increasing number of SCVs, which favors the survival and proliferation of Salmonella inside the host cell.

Impact of resistance exercise on ribosome biogenesis is acutely regulated by post-exercise recovery strategies

Muscle hypertrophy occurs following increased protein synthesis, which requires activation of the ribosomal complex. Additionally, increased translational capacity via elevated ribosomal RNA (rRNA) synthesis has also been implicated in resistance training-induced skeletal muscle hypertrophy. The time course of ribosome biogenesis following resistance exercise (RE) and the impact exerted by differing recovery strategies remains unknown. In the present study, the activation of transcriptional regulators, the expression levels of pre-rRNA, and mature rRNA components were measured through 48 h after a single-bout RE. In addition, the effects of either low-intensity cycling (active recovery, ACT) or a cold-water immersion (CWI) recovery strategy were compared. Nine male subjects performed two bouts of high-load RE randomized to be followed by 10 min of either ACT or CWI. Muscle biopsies were collected before RE and at 2, 24, and 48 h after RE. RE increased the phosphorylation of the p38-MNK1-eIF4E axis, an effect only evident with ACT recovery. Downstream, cyclin D1 protein, total eIF4E, upstream binding factor 1 (UBF1), and c-Myc proteins were all increased only after RE with ACT. This corresponded with elevated abundance of the pre-rRNAs (45S, ITS-28S, ITS-5.8S, and ETS-18S) from 24 h after RE with ACT. In conclusion, coordinated upstream signaling and activation of transcriptional factors stimulated pre-rRNA expression after RE. CWI, as a recovery strategy, markedly blunted these events, suggesting that suppressed ribosome biogenesis may be one factor contributing to the impaired hypertrophic response observed when CWI is used regularly after exercise.

Hospital-related fears and coping strategies in 4-6-year-old children

There is only little information available on the 4-6-year-old child s hospital-related fears, and on the coping with such fears, as expressed by the children themselves. However, previous data collected from parents and hospital personnel indicate that hospitalization is an anxiety-producing experience for young children. The purpose of this study was to describe the experience of hospital-related fears and the experience of coping with hospital-related fears of 4-6-year-old children. The aim of this study was to form a descriptive model of the subjective experience of hospital-related fears and coping strategies of 4-6-year old children. The data were collected by interviewing 4-6-year-old children from a hospital and kindergarten settings in Finland from 2004 to 2006. Ninety children were interviewed in order to describe the hospital-related fear and the experience of fear, and 89 to describe their coping with the fear and the experience of coping. The children were chosen through purposive sampling. The data were gathered by semi-structured interview, supported by pictures. The data about hospital-related fears and on strategies for coping with hospital-related fears were reviewed by qualitative and quantitative methods. The experience of hospital-related fears and coping with these fears were analyzed using Colaizzi s Method of Phenomenological Analysis. The results revealed that more than 90 % of the children said they were afraid of at least one thing in hospital. Most of the fears could be categorized as nursing interventions, fears of being a patient, and fears caused by the developmental stage of the child. Children interviewed in the hospital expressed substantially more fears than children interviewed in kindergarten. Children s meanings of hospital-related fears were placed into four main clusters: 1) insecurity, 2) injury, 3) helplessness, 4) and rejection. The results also showed that children have plenty of coping strategies, to deal with their fears, especially such strategies in which the children themselves play an active role. Most often mentioned coping strategies were 1) the presence of parents and other family members, 2) the help of the personnel, 3) positive images and humour, 4) play, and 5) the child s own safety toy. The children interviewed in the hospital mentioned statistically significantly more often play, positive imagination and humour as their coping strategy than children interviewed in kindergarten. The meaning of coping with hospital fears consisted of six clusters: pleasure, security, care, understanding the meaning of the situation participating, and protecting oneself. Being admitted to a hospital is an event which may increase the fears of a 4-6-year-old child. Children who have personal experience of being admitted to a hospital describe more fears than healthy children in kindergarten. For young children, hospital-related fear can be such a distressing experience that it reflects on their feelings of security and their behaviour. Children can sometimes find it difficult to admit their fear. Children need the help of adults to express their hospital-related fears, the objects of the fears, and to cope with the fears. Personnel should be aware of children s fears and support them in the use of coping strategies. In addition to the experiences of security and care, pre-school-aged children need active coping strategies that they can use themselves, regardless of the presence of the parents or nurses. Most of all, children need the possibility to play and experience pleasure. Children can also be taught coping strategies which give them an active, positive role.

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.

Care for care: Studio REV's strategies of engagement

The artists at Studio REV-, along with their allies in the broader non-profit sector, address domestic workers’ rights in the United States. As a social practice art project, NannyVan works to improve how information about domestic rights is disseminated to these workers, whether nannies, elder caregivers or others. As part of a larger project named CareForce, the NannyVan project shows an ethics of care by using design traces as tactics and transversal methods as strategies.

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.

A Petri net model for evaluating packet buffering strategies in a network processor

Previous studies have shown that buffering packets in DRAM is a performance bottleneck. In order to understand the impediments in accessing the DRAM, we developed a detailed Petri net model of IP forwarding application on IXP2400 that models the different levels of the memory hierarchy. The cell based interface used to receive and transmit packets in a network processor leads to some small size DRAM accesses. Such narrow accesses to the DRAM expose the bank access latency, reducing the bandwidth that can be realized. With real traces up to 30% of the accesses are smaller than the cell size, resulting in 7.7% reduction in DRAM bandwidth. To overcome this problem, we propose buffering these small chunks of data in the on chip scratchpad memory. This scheme also exploits greater degree of parallelism between different levels of the memory hierarchy. Using real traces from the internet, we show that the transmit rate can be improved by an average of 21% over the base scheme without the use of additional hardware. Further, the impact of different traffic patterns on the network processor resources is studied. Under real traffic conditions, we show that the data bus which connects the off-chip packet buffer to the micro-engines, is the obstacle in achieving higher throughput.

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.

Energy–aware forwarding strategies for delay tolerant networking (DTN) routing protocols

This research addresses efficient use of the available energy in resource constrained mobile sensor nodes to prevent early depletion of the battery and maximize the packet delivery rate. This research contributes two energy-aware enhancement strategies to improve the network lifetime and delivery probability for energy constrained applications in the delay-tolerant networking environment.