Modified DBSCAN Algorithm for Microscopic Image Analysis of Wood

The analysis of the intern anatomy of wood samples for species identification is a complex task that only experts can perform accurately. Since there are not many experts in the world and their training can last decades, there is great interest in developing automatic processes to extract high-level information from microscopic wood images. The purpose of this work was to develop algorithms that could provide meaningful information for the classification process. The work focuses on hardwoods, which have a very diverse anatomy including many different features. The ray width is one of such features, with high diagnostic value, which is visible on the tangential section. A modified distance function for the DBSCAN algorithm was developed to identify clusters that represent rays, in order to count the number of cells in width. To test both the segmentation and the modified DBSCAN algorithms, 20 images were manually segmented, obtaining an average Jaccard index of 0.66 for the segmentation and an average index M=0.78 for the clustering task. The final ray count had an accuracy of 0.91. (c) 2019, Springer Nature Switzerland AG

3D Refuse-derived Fuel Particle Tracking-by-Detection Using a Plenoptic Camera System

Multiple particle tracking-by-detection is a widely investigated issue in image processing. The paper presents approaches to detecting and tracking various refuse-derived fuel particles in a industrial environment using a plenoptic camera system, which is able to yield 2D gray value information and 3D point clouds with noticeable fluctuations. The presented approaches, including an innovative combined detection method and a post-processing framework for multiple particle tracking, aim at making the most of the acquired 2D and 3D information to deal with the fluctuations of the measuring system. The proposed novel detection method fuses the captured 2D gray value information and 3D point clouds, which is superior to applying single information. Subsequently, the particles are tracked by the linear Kalman filter and 2.5D global nearest neighbor (GNN) and joint probabilistic data association (JPDA) approach, respectively. As a result of several inaccurate detection results caused by the measuring system, the initial tracking results contain faulty and incomplete tracklets that entail a post-processing process. The developed post-processing approach based merely on particle motion similarity benefits a precise tracking performance by eliminating faulty tracklets, deleting outliers, connecting tracklets, and fusing trajectories. The proposed approaches are quantitatively assessed with manuelly labeled ground truth datasets to prove their availability and adequacy as well. The presented combined detection method provides the highest F 1 -score, and the proposed post-processing framework enhances the tracking performance significantly with regard to several recommended evaluation indices

Computer vision-based wood identification: a review

Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.info:eu-repo/semantics/publishedVersio

Receptor dimer stabilization By hierarchical plasma membrane microcompartments regulates cytokine signaling

The interaction dynamics of signaling complexes is emerging as a key determinant that regulates the specificity of cellular responses. We present a combined experimental and computational study that quantifies the consequences of plasma membrane microcompartmentalization for the dynamics of type I interferon receptor complexes. By using long-term dual-color quantum dot (QD) tracking, we found that the lifetime of individual ligand-induced receptor heterodimers depends on the integrity of the membrane skeleton (MSK), which also proved important for efficient downstream signaling. By pair correlation tracking and localization microscopy as well as by fast QD tracking, we identified a secondary confinement within ~300-nm-sized zones. A quantitative spatial stochastic diffusion-reaction model, entirely parameterized on the basis of experimental data, predicts that transient receptor confinement by the MSK meshwork allows for rapid reassociation of dissociated receptor dimers. Moreover, the experimentally observed apparent stabilization of receptor dimers in the plasma membrane was reproduced by simulations of a refined, hierarchical compartment model. Our simulations further revealed that the two-dimensional association rate constant is a key parameter for controlling the extent of MSK-mediated stabilization of protein complexes, thus ensuring the specificity of this effect. Together, experimental evidence and simulations support the hypothesis that passive receptor confinement by MSK-based microcompartmentalization promotes maintenance of signaling complexes in the plasma membrane

A first order phase transition mechanism underlies protein aggregation in mammalian cells

The formation of misfolded protein aggregates is a hallmark of neurodegenerative diseases. The aggregate formation process exhibits an initial lag phase when precursor clusters spontaneously assemble. However, most experimental assays are blind to this lag phase. We develop a quantitative assay based on super-resolution imaging in fixed cells and light sheet imaging of living cells to study the early steps of aggregation in mammalian cells. We find that even under normal growth conditions mammalian cells have precursor clusters. The cluster size distribution is precisely that expected for a so-called super-saturated system in first order phase transition. This means there exists a nucleation barrier, and a critical size above which clusters grow and mature. Homeostasis is maintained through a Szilard model entailing the preferential clearance of super-critical clusters. We uncover a role for a putative chaperone (RuvBL) in this disassembly of large clusters. The results indicate early aggregates behave like condensates. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).National Institutes of Health (U.S.) (Grant DP2CA195769

A first order phase transition mechanism underlies protein aggregation in mammalian cells

The formation of misfolded protein aggregates is a hallmark of neurodegenerative diseases. The aggregate formation process exhibits an initial lag phase when precursor clusters spontaneously assemble. However, most experimental assays are blind to this lag phase. We develop a quantitative assay based on super-resolution imaging in fixed cells and light sheet imaging of living cells to study the early steps of aggregation in mammalian cells. We find that even under normal growth conditions mammalian cells have precursor clusters. The cluster size distribution is precisely that expected for a so-called super-saturated system in first order phase transition. This means there exists a nucleation barrier, and a critical size above which clusters grow and mature. Homeostasis is maintained through a Szilard model entailing the preferential clearance of super-critical clusters. We uncover a role for a putative chaperone (RuvBL) in this disassembly of large clusters. The results indicate early aggregates behave like condensates

Mechanisms of adhesive mixing for drug particle inhalation (Numerical investigation of the interplay between formulation variables)

Formulation of therapeutic dry powders for lung drug delivery via inhalation is done via adhesive mixing. In this process, micron-sized active pharmaceutical ingredient particles are blended with relatively coarse carrier particles until stable adhesive units of carrier and drug particles are formed. Inside the inhaler and upon its actuation, the turbulent kinetic energy of air stream is transferred to the bulk powder of adhesive units and consequently drug particles are dispersed into primary respirable particles. The formulation process-besides the inhaler design and the patient’s respiratory manoeuvre- is one of the three pillars that determine the overall performance of drug administration, and therefore, it needs to be genuinely understood. Despite all the recent advancements in the formulation of carrier-based dry powder inhaler, the in vitro efficiency of currently marketed inhalers is at best less than 50% of their nominal values(2017).The goal of this research is to devise a methodology to comprehend the complex nature of the adhesive mixing process for inhalation, and to optimize this process. The small temporal and spatial scales of the adhesive mixing, on one hand, and the omnipresent interplay of process variables, on the other hand, require a modeling framework and several quality-assessment tools. The underlying principle of this framework is to treat the adhesive mixture as a particulate system, whose dynamic behaviour can be modelled by applying the Newton’s laws of motion to individual particles. Several formulation variables are selected, in accordance with their significance in the process and with the capacity of the developed model, for parameter studying. These variables include the (i) adhesive properties of particles, (ii) the mixing intensity, (iii) the shape of carriers, (iv) the surface asperity of particles, and (v) the added fine particles (ternary blend). The process quality is inferred from mixing homogeneity indices, micro-scale structure of adhesive units, and the fragmentation analyses of drug agglomerates. In addition to the formulation process, simulated dispersion tests are performed in order to understand the role of the carrier surface roughness on the drug particle detachment during aerosolization. The combination of mixing energy and particle surface energies is used to map the mixing state. It is found that any imbalance between these two process variables results in poor adhesive mixtures. The non-sphericity of carrier particles is also shown to impose a noticeable difference in the breakage and adhesion pattern of drug agglomerates. In the context of formulation, the carrier surface roughness reduces the drug deposition, and in the context of dispersion, the drug detachment is found proportional to the roughness length scale. Lastly, different cases of ternary formulations are simulated and the relevance of the active site and the buffer theories are examined

Identifying Structure Transitions Using Machine Learning Methods

Methodologies from data science and machine learning, both new and old, provide an exciting opportunity to investigate physical systems using extremely expressive statistical modeling techniques. Physical transitions are of particular interest, as they are accompanied by pattern changes in the configurations of the systems. Detecting and characterizing pattern changes in data happens to be a particular strength of statistical modeling in data science, especially with the highly expressive and flexible neural network models that have become increasingly computationally accessible in recent years through performance improvements in both hardware and algorithmic implementations. Conceptually, the machine learning approach can be regarded as one that employing algorithms that eschew explicit instructions in favor of strategies based around pattern extraction and inference driven by statistical analysis and large complex data sets. This allows for the investigation of physical systems using only raw configurational information to make inferences instead of relying on physical information obtained from a priori knowledge of the system. This work focuses on the extraction of useful compressed representations of physical configurations from systems of interest to automate phase classification tasks in addition to the identification of critical points and crossover regions