The complexity of mesoporous silica nanomaterials unravelled by single molecule microscopy

Mesoporous silica nanomaterials are a novel class of materials that offer a highly complex porous network with nanometre-sized channels into which a wide amount of differently sized guests can be incorporated. This makes them an ideal host for various applications for example in catalysis, chromatography and nanomedicine. For these applications, analyzing the host properties and understanding the complicated host–guest interactions is of pivotal importance. In this perspective we review some of our recent work that demonstrates that single molecule microscopy techniques can be utilized to characterize the porous silica host with unprecedented detail. Furthermore, the single molecule studies reveal sample heterogeneities and are a highly efficient tool to gain direct mechanistic insights into the host–guest interactions. Single molecule microscopy thus contributes to a thorough understanding of these nanomaterials enabling the development of novel tailor-made materials and hence optimizing their applicability significantly

Bioengineering models of cell signaling

Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions

3D Architectural Analysis of Neurons, Astrocytes, Vasculature & Nuclei in the Motor and Somatosensory Murine Cortical Columns

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, the sheer density of filamentous processes in the neuropil significantly complicates analysis due to the difficulty of separating individual cells in a highly interconnected network of tightly woven cellular arbors. In order to solve these problems, a variety of methodologies were developed and validated for improved analysis of cortical anatomy. An enhanced immunofluorescent staining and imaging protocol was utilized to precisely locate specific functional regions within brain slices at high magnification and collect four-channel, complete cortical columns. A powerful deconvolution routine was established which collected depth variant PSFs using an optical phantom for image restoration. Fractional volume analysis (FVA) was used to provide preliminary data of the proportions of each stained component in order to statistically characterize the variability within and between the functional regions in a depth-dependent and depth-independent manner. Finally, using machine learning techniques, a supervised learning model was developed that could automatically classify neuronal and astrocytic nuclei within the large cortical column datasets based on perinuclear fluorescence. These annotated nuclei were then used as seed points within their corresponding fluorescent channel for cell individualization in a highly interconnected network. For astrocytes, this technique provides the first method for characterization of complex morphology in an automated fashion over large areas without laborious dye filling or manual tracing

An integrated network of Arabidopsis growth regulators and its use for gene prioritization

Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth

Ibeacon based proximity and indoor localization system

User location can be leveraged to provide a wide range of services in a variety of indoor locations including retails stores, hospitals, airports, museums and libraries etc. The widescale proliferation of user devices such as smart phones and the interconnectivity among different entities, powered by Internet of Things (IoT), makes user device-based localization a viable approach to provide Location Based Services (LBS). Location based services can be broadly classified into 1) Proximity based services that provides services based on a rough estimate of users distance to any entity, and 2) Indoor localization that locates a user\u27s exact location in the indoor environment rather than a rough estimate of the distance. The primary requirements of these services are higher energy efficiency, localization accuracy, wide reception range, low cost and availability. Technologies such as WiFi, Radio Frequency Identification (RFID) and Ultra Wideband (UWB) have been used to provide both indoor localization and proximity based services. Since these technologies are not primarily intended for LBS, they do not fulfill the aforementioned requirements. Bluetooth Low Energy (BLE) enabled beacons that use Apple\u27s proprietary iBeacon protocol are mainly intended to provide proximity based services. iBeacons satisfy the energy efficiency, wide reception range and availability requirements of LBS. However, iBeacons are prone to noise due to their reliance on Received Signal Strength Indicator (RSSI), which drastically fluctuates in indoor environments due to interference from different obstructions. This limits its proximity detection accuracy. In this thesis, we present an iBeacon based proximity and indoor localization system. We present our two server-based algorithms to improve the proximity detection accuracy by reducing the variation in the RSSI and using the RSSI-estimated distance, rather than the RSSI itself, for proximity classification. Our algorithms Server-side Running Average and Server-side Kalman Filter improves the proximity detection accuracy by 29% and 32% respectively in contrast to Apple\u27s current approach of using moving average of RSSI values for proximity classification. We utilize a server-based approach because of the greater computing power of servers. Furthermore, server-based approach helps reduce the energy consumption of user device. We describe our cloud based architecture for iBeacon based proximity detection. We also use iBeacons for indoor localization. iBeacons are not primarily intended for indoor localization as their reliance on RSSI makes them unsuitable for accurate indoor localization. To improve the localization accuracy, we use Bayesian filtering algorithms such as Particle Filter (PF), Kalman Filter (KF), and Extended Kalman Filter (EKF). We show that by cascading Kalman Filter and Extended Kalman Filter with Particle Filter, the indoor localization accuracy can be improved by 28% and 33.94% respectively when compared with only using PF. The PF, KFPF and PFEKF algorithm on the server side have average localization error of 1.441 meters, 1.0351 meters and 0.9519 meters respectively

Supercritical phase inversion of starch-poly(e-caprolactone) for tissue engineering applications

In this work, a starch-based polymer, namely a blend of starch-poly(ε-caprolactone) was processed by supercritical assisted phase inversion process. This processing technique has been proposed for the development of 3D structures with potential applications in tissue engineering applications, as scaffolds. The use of carbon dioxide as non-solvent in the phase inversion process leads to the formation of a porous and interconnected structure, dry and free of any residual solvent. Different processing conditions such as pressure (from 80 up to 150 bar) and temperature (45 and 55°C) were studied and the effect on the morphological features of the scaffolds was evaluated by scanning electron microscopy and micro-computed tomography. The mechanical properties of the SPCL scaffolds prepared were also studied. Additionally, in this work, the in vitro biological performance of the scaffolds was studied. Cell adhesion and morphology, viability and proliferation was assessed and the results suggest that the materials prepared are allow cell attachment and promote cell proliferation having thus potential to be used in some for biomedical applications.Ana Rita C. Duarte is grateful for financial support from Fundacao para a Ciencia e Tecnologia through the grant SFRH/BPD/34994/2007

Ensuring the in vitro degradation reproducibility of powder metallurgy processed Mg 0.6Ca system

Magnesium degradation is a complex phenomenon that is too difficult to be described by a single influential parameter. Magnesium degradation is often influenced by either overtaking or overlapping factors like the cell culture medium composition, physiological conditions, impurities, and material’s internal microstructure, etc. This poses a challenge in obtaining the reproducible degradation results. Hence, in the present work, microstructural features like porosity and grain size distributions in powder metallurgy (PM) Mg-0.6Ca system were discretely evaluated for their roles in altering the specimen in vitro degradation rates. Importance was also given to the specimen impurity and mechanical properties. Based on the results, the limitations in PM processing conditions towards obtaining robust degradation results or, in other words, the material parameter thresholds to be realized for obtaining reproducible degradation profiles in PM Mg-0.6Ca specimens were put forth. Additionally, using literature evidence, the mechanisms governing pore closure and grain growth during liquid phase sintering of Mg-0.6Ca specimens from the PM processing perspective were determined. PM Mg-0.6Ca specimens were fabricated via powder blending of pure magnesium and master alloy Mg-10Ca powders. Specimens of seven different porosities, from 3% to 21%, were produced by varying sintering temperatures. Specimens with heterogeneous grain size distributions were obtained by surface modification of pure magnesium powders by means of a mechanical sieving treatment. Degradation profiles were analyzed in vitro using a semi static immersion test for 16 days under physiological conditions of 37 °C, 20% O2, 5% CO2, 95% relative humidity. Dulbecco’s modified Eagle’s medium was used as cell culture medium with Glutamax and 10% fetal bovine serum as supplements. Mechanical properties were determined using micro tensile specimens. The results indicate that low mean degradation rates (MDR 95% to ≤ 45% when falling below this value. Similarly, the pore interconnectivity sharply drops from > 95% to < 10% at this porosity, thereby enhancing the degradation reproducibility. From PM processing perspective, the sintering temperature of 570 °C is proven as beneficial to promote liquid fractions high enough to enhance specimen sinter density. The present work also showed that heterogeneous grain growth is prompted by the reduced oxide pinning effect at the grain boundaries during sintering of PM Mg-0.6Ca specimens. The heterogeneous grain growth additionally induced the formation of eutectic lamellar structure α-Mg + Mg2Ca at certain grain boundaries throughout the microstructure, which is otherwise not evident in specimens with a homogeneous grain size. Based on the literature and results of the present work, it is postulated that this eutectic structure is the major reason for a non-reproducible degradation in PM Mg-0.6Ca specimens possessing a heterogeneous grain structure. Though mechanical properties are not majorly affected, it is recommended that heterogeneous grain growth is to be avoided in PM Mg-0.6Ca specimens. The presented results also implicitly conveyed the flexibility of PM as a viable technique to design Mg-Ca materials with tailor made degradation and mechanical strengths

OCC Future and Obstacles under 5g Requirements

Telecommunications specifications of the fifth-generation (5 G) are being established to satisfy the rising demands of high-speed broadband networks (i.e., a few tens of Gigabits every second). The 5 G standard derives primarily from a rising number of subscribers and a multitude of various apps, commonly referred to as smart devices, communicating as part of Internet-of-Things (IoT) network For 5 G, a few possible developments such as millimeter waves, large multiple-input multiple-output, and small cell connectivity have appeared. While such technologies will meet 5 G specifications, attention is being given to a complementary potential wireless optical wireless communication (OWC) system. Clear light contact (VLC) as part of OWC. Among the most desirable solutions for 5 G networks and beyond are optical camera communications (OCCs). As part of future smart cities, VLC with huge frequency spectrum integrated with IoT that opens up a broad range of indoor and outdoor applications. This paper gives a description of the VLC-centric all-optical IoT and Potential implementations and issues centered on OCC under 5 G Requirement

Reactivated fault zones: kinematic complexity and fault rock spectral characterization

In the present work three main factors contributing to the overall complexity of reactivated fault zones have been investigated: i) the problematic reconstruction of polyphase brittle tectonic evolution accommodated by fault zones dissecting lithologically heterogeneous rock domains; ii) the estimate of the mechanical anisotropy associated with pre-existing planar discontinuities (i.e., metamorphic foliations and inherited faults) steering their brittle reactivation process; iii) the spectral characterization of fault zone rocks in complex fault architectures aimed at inferring the distribution of fault zone domains by means of remote sensing techniques. In order to achieve the goal of improving current understanding of these factors defining reactivated fault zone complexity, different methodologies have been applied: i) a paleostress inversion analysis that carefully considers each analyzed fault zones and the different mechanical behavior of the lithological domains they deform; ii) a bootstrapping statistical approach aimed at evaluating the homogeneity between the resulting stress tensors and identifying possible local stress perturbations; iii) a normalised slip tendency analysis that, integrated with paleostress reconstructions and detailed meso- and micro-structural observations, allows constraining the mechanical properties of pre-existing planar discontinuities; iv) a spectral features analysis of fault zone rock reflectance spectra, aimed at highlighting the correlation between variations in fault rock spectral signatures and grain size reduction related to fault comminution processes. The main results of this work highlighted that: i) polyphase brittle tectonics within lithologically heterogeneous rock domains can be efficiently unrevealed by applying a paleostress inversion combined with bootstrapping statistical analysis of the resulting reduced stress tensors; ii) normalised slip tendency analysis can be considered a reliable method to investigate and constrain the weakness of pre-existing anisotropies at a regional scale (104-103 m); iii) the grain size reduction resulting from fault-related comminution processes on mineralogically homogenous bedrocks (carbonates in this case) influences the spectral signatures of fault rock samples, which absorption feature parameters vary systematically with the grain size in the VNIR and SWIR wavelength ranges; iv) consequently, remote sensing analysis, based on fault rock reflectance spectrum variabilities due to comminution processes, has a good potential in the identification of the spatial distribution and extent of fault core and damage zone domains (i.e., characterized by different grain sizes) on mineralogically homogenous bedrocks (carbonates in this case)