Applied biomaterials from sustainable sources

If we look back in time at the history of humanity, we can state that our generation is living an era of outstanding efficiency and progress because of globalization and global competition, even if this is resulting in the rapid depletion of energy sources and raw materials. The environmental impact of non-biodegradable plastic wastes is of increasing global concern: nowadays, imagining a world without synthetic plastics seems impossible, though their large-scale production and their extensive use have only spread since the end of the World War II. In recent years, the demand for sustainable materials has increased significantly and, with a view to circular economy, research has also focused on the enhancement and subsequent reuse of waste materials produced by industrial processing, intensive farming and the agricultural sector. Plastic polymers have been the most practical and economical solution for decades due to their low cost, prompt availability and excellent optical, mechanical and barrier properties. Biodegradable polymers could replace them in many applications, thus reducing the problems of traditional plastics disposability and the dependence on petroleum. Natural biopolymers are in fact characterized by a high biocompatibility and biodegradability and have already prompted research in the field of regenerative medicine. During my PhD, my goal was to use natural polymers from sustainable sources as raw materials to produce biomaterials, which are materials designed to interface with biological systems to evaluate, support or replace any tissue, organ, or function of the body. I focused on the use of the most abundant biopolymers in nature to produce biomaterials in the form of films, scaffolds and cements. After a complete characterization, the materials were proposed for suitable applications in different fields, from tissue engineering to cosmetics and food packaging. Some of the obtained results were published on international scientific and peer-reviewed journals.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Photoactive (supra)molecular devices in double layer membranes

Biological systems are complex and highly organized architectures governed by non-covalent interactions responsible for the regulation of essential tasks in all living organisms. These systems are a constant source of inspiration for supramolecular chemists aiming to design multicomponent molecular assemblies able to perform elaborated tasks, thanks to the role and action of the components that constitute them. Artificial supramolecular systems exploit non-covalent interactions to mimic naturally occurring events. In this context, stimuli-responsive supramolecular systems have attracted attention due to the possibility to control macroscopic effects through modifications at the nanoscale. This thesis is divided in three experimental chapters, characterized by a progressive increase in molecular complexity. Initially, the preparation and studies of liposomes functionalized with a photoactive guest such as azobenzene in the bilayer were tackled, in order to evaluate the effect of such photochrome on the vesicle properties. Subsequently, the synthesis and studies of thread-like molecules comprising an azobenzene functionality was reported. Such molecules were conceived to be intercalated in the bilayer membrane of liposomes with the aim to be used as components for photoresponsive transmembrane molecular pumps. Finally, a [3]rotaxane was developed and studied in solution. This system is composed of two crown ether rings interlocked with an axle containing three recognition sites for the macrocycles, i.e. two pH-switchable ammonium stations and a permanent triazolium station. Such molecule was designed to achieve a change in the ratio between the recognition sites and the crown ethers as a consequence of acid-base inputs. This leads to the formation of rotaxanes containing a number of recognition sites respectively larger, equal or lower than the number of interlocked rings and connected by a network of acid-base reactions.

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

Nanomechanical characterization of soft bioelectronic interfaces via modeling-informed atomic force microscopy

The field of bioelectronics involves the use of electrodes to exchange electrical signals with biological systems for diagnostic and therapeutic purposes in biomedical devices and healthcare applications. However, the mechanical compatibility of implantable devices with the human body has been a challenge, particularly with long-term implantation into target organs. Current rigid bioelectronics can trigger inflammatory responses and cause unstable device functions due to the mechanical mismatch with the surrounding soft tissue. Recent advances in flexible and stretchable electronics have shown promise in making bioelectronic interfaces more biocompatible. To fully achieve this goal, material science and engineering of soft electronic devices must be combined with quantitative characterization and modeling tools to understand the mechanical issues at the interface between electronic technology and biological tissue. Local mechanical characterization is crucial to understand the activation of failure mechanisms and optimizing the devices. Experimental techniques for testing mechanical properties at the nanoscale are emerging, and the Atomic Force Microscope (AFM) is a good candidate for in situ local mechanical characterization of soft bioelectronic interfaces. In this work, in situ experimental techniques with solely AFM supported by interpretive models for the characterization of planar and three-dimensional devices suitable for in vivo and in vitro biomedical experimentations are reported. The combination of the proposed models and experimental techniques provides access to the local mechanical properties of soft bioelectronic interfaces. The study investigates the nanomechanics of hard thin gold films on soft polymeric substrates (Poly(dimethylsiloxane) PDMS) and 3D inkjet-printed micropillars under different deformation states. The proposed characterization methods provide a rapid and precise determination of mechanical properties, thus giving the possibility to parametrize the microfabrication steps and investigate their impact on the final device.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Time correlation function in systems with two coexisting biological species

We study a stochastic lattice model describing the dynamics of coexistence of two interacting biological species. The model comprehends the local processes of birth, death, and diffusion of individuals of each species and is grounded on interaction of the predator-prey type. The species coexistence can be of two types: With self-sustained coupled time oscillations of population densities and without oscillations. We perform numerical simulations of the model on a square lattice and analyze the temporal behavior of each species by computing the time correlation functions as well as the spectral densities. This analysis provides an appropriate characterization of the different types of coexistence. It is also used to examine linked population cycles in nature and in experiment.

Sludge population optimisation: a new dimension for the control of biological wastewater treatment systems

The activated sludge comprises a complex microbiological community. The structure (what types of microorganisms are present) and function (what can the organisms do and at what rates) of this community are determined by external physico -chemical features and by the influent to the sewage treatment plant. The external features we can manipulate but rarely the influent. Conventional control and operational strategies optimise activated sludge processes more as a chemical system than as a biological one. While optimising the process in a short time period, these strategies may deteriorate the long-term performance of the process due to their potentially adverse impact on the microbial properties. Through briefly reviewing the evidence available in the literature that plant design and operation affect both the structure and function of the microbial community in activated sludge, we propose to add sludge population optimisation as a new dimension to the control of biological wastewater treatment systems. We stress that optimising the microbial community structure and property should be an explicit aim for the design and operation of a treatment plant. The major limitations to sludge population optimisation revolve around inadequate microbiological data, specifically community structure, function and kinetic data. However, molecular microbiological methods that strive to provide that data are being developed rapidly. The combination of these methods with the conventional approaches for kinetic study is briefly discussed. The most pressing research questions pertaining to sludge population optimisation are outlined. (C) 2002 Elsevier Science Ltd. All rights reserved.

Development of a novel titration and off-gas analysis (TOGA) sensor for study of biological processes in wastewater treatment systems

The development of the new TOGA (titration and off-gas analysis) sensor for the detailed study of biological processes in wastewater treatment systems is outlined. The main innovation of the sensor is the amalgamation of titrimetric and off-gas measurement techniques. The resulting measured signals are: hydrogen ion production rate (HPR), oxygen transfer rate (OTR), nitrogen transfer rate (NTR), and carbon dioxide transfer rate (CTR). While OTR and NTR are applicable to aerobic and anoxic conditions, respectively, HPR and CTR are useful signals under all of the conditions found in biological wastewater treatment systems, namely, aerobic, anoxic and anaerobic. The sensor is therefore a powerful tool for studying the key biological processes under all these conditions. A major benefit from the integration of the titrimetric and off-gas analysis methods is that the acid/base buffering systems, in particular the bicarbonate system, are properly accounted for. Experimental data resulting from the TOGA sensor in aerobic, anoxic, and anaerobic conditions demonstrates the strength of the new sensor. In the aerobic environment, carbon oxidation (using acetate as an example carbon source) and nitrification are studied. Both the carbon and ammonia removal rates measured by the sensor compare very well with those obtained from off-line chemical analysis. Further, the aerobic acetate removal process is examined at a fundamental level using the metabolic pathway and stoichiometry established in the literature, whereby the rate of formation of storage products is identified. Under anoxic conditions, the denitrification process is monitored and, again, the measured rate of nitrogen gas transfer (NTR) matches well with the removal of the oxidised nitrogen compounds (measured chemically). In the anaerobic environment, the enhanced biological phosphorus process was investigated. In this case, the measured sensor signals (HPR and CTR) resulting from acetate uptake were used to determine the ratio of the rates of carbon dioxide production by competing groups of microorganisms, which consequently is a measure of the activity of these organisms. The sensor involves the use of expensive equipment such as a mass spectrometer and requires special gases to operate, thus incurring significant capital and operational costs. This makes the sensor more an advanced laboratory tool than an on-line sensor. (C) 2003 Wiley Periodicals, Inc.

Comparison of inverse planning systems based on biological or physical factors: Pinnacle®, Corvus® and Monaco®

Radiotherapy (RT) is one of the most important approaches in the treatment of cancer and its performance can be improved in three different ways: through the optimization of the dose distribution, by the use of different irradiation techniques or through the study of radiobiological initiatives. The first is purely physical because is related to the physical dose distributiuon. The others are purely radiobiological because they increase the differential effect between the tumour and the health tissues. The Treatment Planning Systems (TPS) are used in RT to create dose distributions with the purpose to maximize the tumoral control and minimize the complications in the healthy tissues. The inverse planning uses dose optimization techniques that satisfy the criteria specified by the user, regarding the target and the organs at risk (OAR’s). The dose optimization is possible through the analysis of dose-volume histograms (DVH) and with the use of computed tomography, magnetic resonance and other digital image techniques.

Biological activity of well defined hydantoin derivatives on efflux pump systems of bacteria and cancer cells

A multi-resistência a antibióticos e medicamentos usados em quimioterapia é um dos grandes problemas com os quais as instituições de saúde se debatem hoje em dia. A acção provocada por bombas de efluxo é uma das suas causas. Estas bombas têm uma importância fundamental, uma vez que, ao expelirem todo o tipo de tóxicos para o exterior das células, também expelem medicamentos, fazendo com que estes não tenham o efeito desejado dentro delas. As bombas de efluxo são transportadores que se encontram nas membranas de todo o tipo de células. Existem dois grandes tipos de bombas de efluxo: as primárias e as secundárias. As primeiras conferem multi-resistência principalmente em células eucariotas, como as células do cancro em humanos, tendo como função a mediação da repulsa de substâncias tóxicas por intermédio da hidrólise de ATP. A primeira a ser descoberta e mais estudada destas bombas foi a ABCB1 que é o gene que codifica a glicoproteína-P (P de permeabilidade). Enquanto as secundárias, que são a maior fonte de multi-resistência em bactérias, promovem a extrusão de substâncias tóxicas através da força motriz de protões. Neste tipo de bombas são conhecidas quatro famílias principais, das quais uma das mais importantes é a superfamília RND, uma vez que inclui a bomba AcrAB-TolC, que é muito importante no metabolismo xenobiótico de bactérias Gramnegativas, nomeadamente a E.coli. Com o objectivo de reverter a multi-resistência, tanto em células eucariotas como procariotas, têm-se desenvolvido estratégias de combate que envolvem a descoberta de substâncias que inibam as bombas de efluxo. Assim sendo, ao longo dos tempos têm sido descobertas variadas substâncias que cumprem este objectivo. É o caso, por exemplo, dos derivados de fluoroquinolonas usados como inibidores de bombas de efluxo em bactérias ou do Tamoxifen, utilizado na terapia de pacientes com cancro da mama. Um dos grupos de substâncias estudados para o desenvolvimento de possíveis compostos que actuem como reversores de multi-resistência são os compostos derivados de hidantoínas. Estes, são conhecidos por possuírem uma grande variedade de propriedades bioquímicas e farmacológicas, sendo portanto usados para tratarem algumas doenças em humanos, como a epilepsia. Nestes, estão englobados compostos com actividade anti-convulsão que constitui a sua grande mais-valia e, dependente da substituição no anel que os constitui, uma grande variedade de outras propriedades farmacológicas como a anti-fungica, a anti-arritmica, a anti-viral, a anti-diabética ou por exemplo a antagonização de determinados receptores, como os da serotonina. Apesar de pouco usados em estudos experimentais para desenvolver substâncias anti-carcinogénicas, existem alguns estudos com este efeito. Objectivos: O presente projecto envolve o estudo de bombas de efluxo primárias e secundárias, em células eucariotas e procariotas, respectivamente. Em bactérias, foram usados quatro modelos experimentais: Staphylococcus aureus ATCC 25923, Enterococcus faecalis ATCC 29212, E. coli AG 100 e Salmonella Enteritidis NCTC 13349. Em células de cancro foram usadas, células T de linfoma de rato parentais e células T de linfoma de rato transfectadas com o gene humano MDR-1. O principal objectivo deste estudo foi a pesquisa de novos moduladores de bombas de efluxo presentes em bactérias e células do cancro, tentando assim contribuir para o desenvolvimento de novos agentes farmacológicos que consigam reverter a multi-resistência a medicamentos. Assim sendo foram testados trinta compostos derivados de hidantoínas: SZ-2, SZ-7, LL-9, BS-1, JH-63, MN-3, TD-7k, GG-5k, P3, P7, P10, P11, RW-15b, AD-26, RW-13, AD-29, KF-2, PDPH-3, Mor-1, KK-XV, Thioam-1, JHF-1, JHC-2, JHP-1, Fur-2, GL-1, GL-7, GL-14, GL-16, GL-18. Como forma de atingir estes objectivos, a actividade biológica dos trinta compostos derivados de hidantoínas foi avaliada nas quatro estirpes de bactérias da seguinte forma: foram determinadas as concentrações mínimas inibitórias dos trinta compostos como forma de definir as concentrações em que os compostos seriam utilizados. Os compostos foram posteriormente testadas com um método fluorométrico de acumulação de brometo de etídeo, que é um substrato comum em bombas de efluxo bacterianas, desenvolvido por Viveiros et al. A actividade biológica dos compostos derivados de hidantoínas nas células de cancro foi demonstrada por diferentes métodos. O efeito anti-proliferativo e citotóxico dos trinta compostos foi avaliado nas células T de linfoma de rato transfectadas com o gene humano MDR-1 pelo método de thiazolyl de tetrazólio (MTT). Como o brometo de etídeo também é expelido pelos transportadores ABC, estes compostos foram posteriormente testados com um método fluorométrico de acumulação de brometo de etídeo desenvolvido por Spengler et al nos dois diferentes tipos de células eucariotas. Resultados: A maioria dos compostos derivados de hidantoínas foi eficaz na modulação de bombas de efluxo, nas duas estirpes de bactérias Gram-negativas e nos dois diferentes tipos de células T de linfoma. Em contraste com estes resultados, nas duas estirpes de células Gram-positivas, a maioria dos compostos tiveram pouco efeito na inibição de bombas de efluxo ou até nenhum, em muitos dos casos. De uma maneira geral os melhores compostos nas diferentes estirpes de bactérias foram: Thioam-1, SZ-2, P3, Rw-15b, AD-26, AD-29, GL-18, GL-7, KF-2, SZ-7, MN-3, GL-16 e GL- 14. Foram portanto estes os compostos que provocaram maior acumulação de brometo de etídeo, inibindo assim com maior eficácia as bombas de efluxo. No presente estudo, a maioria dos compostos conseguiu inibir a resistência provocada pela bomba de efluxo ABCB1, tanto nas células parentais bem como nas células que sobre-expressam esta bomba, causando a acumulação de brometo de etídeo dentro das células. As células que sobreexpressam a bomba ABCB1 foram posteriormente testadas com citometria de fluxo que é a técnica padrão para pesquisa de inibidores de bombas de efluxo. Os compostos que foram mais efectivos na inibição da bomba ABCB1, causando assim maior acumulação de brometo de etídeo nas células que sobre-expressam esta bomba foram: PDPH-3, GL-7, KK-XV, AD-29, Thioam-1, SZ-7, KF-2, MN-3, RW-13, LL-9, P3, AD-26, JH-63 e RW- 15b. Este facto não corroborou totalmente os resultados da citometria de fluxo uma vez que os moduladores que provocaram maior inibição da bomba ABCB1 foram o MN-3, JH-63 e o BS-1, sendo que o último não foi seleccionado como um bom composto usando o método fluorométrico de acumulação de brometo de etídeo. Conclusão: Os compostos derivados de hidantoínas testados tiveram maior efeito nas estirpes de bactérias Gram-negativas do que nas Gram-positivas. Relativamente às células eucariotas, as estruturas mais activas apresentam substituintes aromáticos bem como alguns fragmentos aminicos terciários.

Biological effects of acrylic engineered particulate-systems

Polymeric particulate-systems are of great relevance due to their possible biomedical applications, among them as carriers for the nano- or microencapsulation of drugs. However, due to their unique specific properties, namely small size range, toxicity issues must be discarded before allowing its use on health-related applications. Several polymers, as poly(methyl methacrylate) (PMMA), have proved to be suitable for the preparation of particulate-systems. However, a major drawback of its use refers to incomplete drug release from particles matrix. Recent strategies to improve PMMA release properties mention the inclusion of other acrylic polymers as Eudragit (EUD) on particles formulation. Though PMMA and EUD are accepted by the FDA as biocompatible, their safety on particle composition lacks sufficient toxicological data. The main objective of this thesis was to evaluate the biological effects of engineered acrylic particulate-systems. Preparation, physicochemical characterization and in vitro toxicity evaluation were assessed on PMMA and PMMA-EUD (50:50) particles. The emulsification-solvent evaporation methodology allowed the preparation of particles with spherical and smooth surfaces within the micrometer range (±500 nm), opposing surface charges and different levels of hydrophobicity. It was observed that particles physicochemical properties (size and charge) were influenced by biological media composition, such as serum concentration, ionic strength or pH. In what concerns to the in vitro toxicological studies, particle cellular uptake was observed on different cell lines (macrophages, osteoblasts and fibroblasts). Cytotoxicity effects were only found after 72 h of cells exposure to the particles, while no oxidative damage was observed neither on osteoblasts nor fibroblasts. Also, no genotoxicity was found in fibroblast using the comet assay to assess DNA damage. This observation should be further confirmed with other validated genotoxicity assays (e.g. Micronucleus Assay). The present study suggests that the evaluated acrylic particles are biocompatible, showing promising biological properties for potential use as carriers in drug-delivery systems.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Entropic transport in confined media: a challenge for computational studies in biological and soft-matter systems

Transport in small-scale biological and soft-matter systems typically occurs under confinement conditions in which particles proceed through obstacles and irregularities of the boundaries that may significantly alter their trajectories. A transport model that assimilates the confinement to the presence of entropic barriers provides an efficient approach to quantify its effect on the particle current and the diffusion coefficient. We review the main peculiarities of entropic transport and treat two cases in which confinement effects play a crucial role, with the appearance of emergent properties. The presence of entropic barriers modifies the mean first-passage time distribution and therefore plays a very important role in ion transport through micro- and nano-channels. The functionality of molecular motors, modeled as Brownian ratchets, is strongly affected when the motor proceeds in a confined medium that may constitute another source of rectification. The interplay between ratchet and entropic rectification gives rise to a wide variety of dynamical behaviors, not observed when the Brownian motor proceeds in an unbounded medium. Entropic transport offers new venues of transport control and particle manipulation and new ways to engineer more efficient devices for transport at the nanoscale.

Antibody Based Diagnostic for Detection Of Vibrios and their Biological Control using Antagonistic Bacteria in Macrobrachium Rosenbergii Larval Rearing Systems

The main objective of the work undertaken here was to develop an appropriate microbial technology to protect the larvae of M.rosenbergii in hatchery from vibriosis. This technology precisely is consisted of a rapid detection system of vibrios and effective antagonistic probiotics for the management of vibrios. The present work was undertaken with the realizations that to stabilize the production process of commercial hatcheries an appropriate, comprehensive and fool proof technology is required primarily for the rapid detection of Vibrio and subsequently for its management. Nine species of Vibrio have been found to be associated with larvae of M. rosenbergii in hatchery. Haemolytic assay of the Vibrio and Aeromonas on prawn blood agar showed that all isolates of V. alginolyticus and Aeromonas sp., from moribund, necrotized larve were haemolytic and the isolates of V.cholerae, V.splendidus II, V.proteolyticus and V.fluvialis from the larvae obtained from apparently healthy larval rearing systems were non-haemolytic. Hydrolytic enzymes such as lipase, chitinase and gelatinase were widespread amongst the Vibrio and Aeromonas isolates. Dominance of V.alginolyticus among the isolates from necrotic larvae and the failure in isolating them from rearing water strongly suggest that they infect larvae and multiply in the larval body and cause mortality in the hatchery. The observation suggested that the isolate V. alginolyticus was a pathogen to the larvae of M.rosenbergii. To sum up, through this work, nine species of Vibrio and genus Aeromonas associated with M.rosenbergii larval rearing systems could be isolated and segregated based on the haemolytic activity and the antibodies (PA bs) for use in diagnosis or epidemiological studies could be produced, based on a virulent culture of V.alginolyticus. This could possibly replace the conventional biochemical tests for identification. As prophylaxis to vibriosis, four isolates of Micrococcus spp. and an isolate of Pseudomonas sp. could be obtained which could possibly be used as antagonistic probiotics in the larval rearing system of M.rosenbergii.