New Metallophthalocyanines Bearing 2-Methylimidazole Moieties—Potential Photosensitizers against Staphylococcus aureus

Newly developed tetra- and octasubstituted methimazole-phthalocyanine conjugates as potential photosensitizers have been obtained. Synthesized intermediates and final products were characterized by the MALD-TOF technique and various NMR techniques, including 2D methods. Single-crystal X-ray diffraction was used to determine the crystal structures of dinitriles. The studied phthalocyanines revealed two typical absorption bands—the Soret band and the Q band. The most intense fluorescence was observed for octasubstituted magnesium(II) phthalocyanine in DMF (Φ_FL = 0.022). The best singlet oxygen generators were octasubstituted magnesium(II) and zinc(II) phthalocyanines (Φ_∆ 0.56 and 0.81, respectively). The studied compounds presented quantum yields of photodegradation at the level between 10⁻⁵ and 10⁻⁶. Due to their low solubility in a water environment, the liposomal formulations were prepared. Within the studied group, octasubstituted zinc(II) phthalocyanine at the concentration of 100 µM activated with red light showed the highest antibacterial activity against S. aureus equal to a 5.68 log reduction of bacterial growth.     

Structural similarity-based object tracking in multimodality surveillance videos

This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance. Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers using real-world surveillance video sequences from multimodal (infrared and visible) cameras.     

Safe reuse in modelling language engineering using model subtyping with OCL constraints

Software and Systems Modeling - Low-code software development promises rapid delivery of software cloud applications by employing domain-specific languages (DSLs), requiring minimal traditional...     

Investigations on the polymorphism of K4CaSi6O15 at elevated temperatures

In the present study, single crystals and polycrystalline material of K₄CaSi₆O₁₅ were prepared from solid-state reactions between stoichiometric mixtures of the corresponding oxides/carbonates. Heat capacity (C_p) measurements above room temperature using a differential scanning calorimeter indicated that two thermal effects occurred at approximately T₁ = 462 K and T₂ = 667 K, indicating the presence of structural phase transitions. The standard third-law entropy of K₄CaSi₆O₁₅ was determined from low-temperature C_p’s measured by relaxation calorimetry using a Physical Properties Measurement System and amounts to S°(298K) = 524.3 ± 3.7 J·mol⁻¹·K⁻¹. For the 1^st transition, the enthalpy change ΔH^tr1 = 1.48 kJ·mol and the entropy change ΔS^tr1 = 3.25 J·mol⁻¹·K⁻¹, whereas ΔH^tr2 = 3.33 kJ·mol⁻¹ and ΔS^tr2 = 5.23 J·mol⁻¹·K⁻¹ were determined for the 2^nd transition. The compound was further characterized by in-situ single-crystal X-ray diffraction between ambient temperature and 1063 K. At 773 K, the high-temperature phase stable above T₂ has the following basic crystallographic data: monoclinic symmetry, space group P2₁/c, a = 6.9469(4) Å, b = 9.2340(5) Å, c = 12.2954(6) Å, β = 93.639(3)°, V = 787.13(7) ų, Z = 2. It belongs to the group of interrupted framework silicates and is based on tertiary (Q³-type) [SiO₄]-tetrahedra. Together with the octahedrally coordinated Ca-cations, a three-dimensional mixed polyhedral network structure is formed, in which the remaining K-ions provide charge balance by occupying voids within the net. The intermediate temperature modification stable between T₁ and T₂ shows a (3+2)-dimensional incommensurately modulated structure that is characterized by the following q-vectors: q₁ = (0.057, 0.172, 0.379), q₂ = (-0.057, 0.172, -0.379). The crystal structures of the high- and the previously studied ambient temperature polymorph (space group Pc) are topologically equivalent and show a group-subgroup relationship. The index of the low- in the high-symmetry group is six and involves both, losses in translation as well as point group symmetry. The distortion is based on shifts of the different atom species and tilts of the 4- and 6-fold coordination polyhedra. Actually, for some of the oxygen atoms, the displacements exceed 0.5 Å. A more detailed analysis of the distortions relating to both structures has been performed using mode analysis, which revealed that the primary distortion mode transforms according to the Λ₁ irreducible representation of P2₁/c. However, other modes with smaller distortion amplitudes are also involved.     

Improving Biocatalytic Properties of an Azoreductase via the N-Terminal Fusion of Formate Dehydrogenase

Azoreductases require NAD(P)H to reduce azo dyes but the high cost of NAD(P)H limits its application. Formate dehydrogenase (FDH) allows NAD(P)⁺ recycling and therefore, the fusion of these two biocatalysts seems promising. This study investigated the changes to the fusion protein involving azoreductase (AzoRo) of Rhodococcus opacus 1CP and FDH (FDH_C23S and FDH_{C23SD195QY196H}) of Candida boidinii in different positions with His-tag as the linker. The position affected enzyme activities as AzoRo activity decreased by 20-fold when it is in the N-terminus of the fusion protein. FDH_C23S+AzoRo was the most active construct and was further characterized. Enzymatic activities of FDH_C23S+AzoRo decreased compared to parental enzymes but showed improved substrate scope – accepting bulkier dyes. Moreover, pH has an influence on the stability and activity of the fusion protein because at pH 6 (pH that is suboptimal for FDH), the dye reduction decreased to more than 50 % and this could be attributed to the impaired NADH supply for the AzoRo part.     

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. We implement a hierarchical and hybrid decision-making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. The method is predestined to include large action-state spaces and an interface to process simulators in future research.     

Graph machine learning for design of high-octane fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.     

Monte Carlo simulation of terpolymerization: Optimizing the simulation and post-processing times

Monte Carlo simulations are a useful and easy way to understand a polymerization reaction process properly. However, achieving reliable results with Monte Carlo simulations can also lead to prohibitive computational times and a considerable amount of data to be processed afterward. The present study analyses the Monte Carlo simulation of a steady-state terpolymerization process to reduce the overall computational time of the simulation and the post-processing of its results. Different sorting algorithms (Bubble, Insertion, Selection, and Tim) and Python libraries (Joblib and Numba) were used. The chain composition distribution and the micro-structures resultant of different scenarios were assessed by processing the simulated mechanism results. The simulation time results indicate the Tim sorting algorithm as the best to use in the post-processing step and the Numba library as the best suited for both the simulation and the post-processing step.