Abstract: Background Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications. Methods Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach. The OrBITS deep learning analysis approach was validated against current standard assays for kinetic imaging and automated analysis of organoids. A drug screen of standard-of-care lung and pancreatic cancer treatments was also performed with the OrBITS platform and compared to the gold standard, CellTiter-Glo 3D assay. Finally, the optimal parameters and drug response metrics were identified to improve patient stratification. Results OrBITS allowed for the detection and tracking of organoids in routine extracellular matrix domes, advanced Gri3D (R)-96 well plates, and high-throughput 384-well microplates, solely based on brightfield imaging. The obtained organoid Count, Mean Area, and Total Area had a strong correlation with the nuclear staining, Hoechst, following pairwise comparison over a broad range of sizes. By incorporating a fluorescent cell death marker, infra-well normalization for organoid death could be achieved, which was tested with a 10-point titration of cisplatin and validated against the current gold standard ATP-assay, CellTiter-Glo 3D. Using this approach with OrBITS, screening of chemotherapeutics and targeted therapies revealed further insight into the mechanistic action of the drugs, a feature not achievable with the CellTiter-Glo 3D assay. Finally, we advise the use of the growth rate-based normalised drug response metric to improve accuracy and consistency of organoid drug response quantification. Conclusion Our findings validate that OrBITS, as a scalable, automated live-cell image analysis software, would facilitate the use of patient-derived organoids for drug development and therapy screening. The developed wet-lab workflow and software also has broad application potential, from providing a launching point for further brightfield-based assay development to be used for fundamental research, to guiding clinical decisions for personalized medicine.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is a challenging neoplastic disease, mainly due to the development of resistance to radio- and chemotherapy. Cold atmospheric plasma (CAP) is an alternative technology that can eliminate cancer cells through oxidative damage, as shown in vitro, in ovo, and in vivo. However, how CAP affects the pancreatic stellate cells (PSCs), key players in the invasion and metastasis of PDAC, is poorly understood. This study aims to determine the effect of an anti-PDAC CAP treatment on PSCs tissue developed in ovo using mono- and co-cultures of RLT-PSC (PSCs) and Mia PaCa-2 cells (PDAC). We measured tissue reduction upon CAP treatment and mRNA expression of PSC activation markers and extracellular matrix (ECM) remodelling factors via qRT-PCR. Protein expression of selected markers was confirmed via immunohistochemistry. CAP inhibited growth in Mia PaCa-2 and co-cultured tissue, but its effectiveness was reduced in the latter, which correlates with reduced ki67 levels. CAP did not alter the mRNA expression of PSC activation and ECM remodelling markers. No changes in MMP2 and MMP9 expression were observed in RLT-PSCs, but small changes were observed in Mia PaCa-2 cells. Our findings support the ability of CAP to eliminate PDAC cells, without altering the PSCs.

Abstract: Melanoma arises from pigment-producing cells called melanocytes located in the basal layers of the epidermis of the skin. Cytoglobin (CYGB) is a ubiquitously expressed hexacoordinated globin that is highly enriched in me­lanocytes and frequently downregulated during melanomagenesis. Previously, we showed that non-thermal plasma (NTP)-produced reactive oxygen and nitrogen species (RONS) lead to the formation of an intra­ molecular disulfide bridge that would allow CYGB to function as a redox-sensitive protein. Here, we investigate the cytotoxic effect of indirect NTP treatment in two melanoma cell lines with divergent endogenous CYGB expression levels, and we explore the role of CYGB in determining treatment outcome. Our findings are consistent with previous studies supporting that NTP cytotoxicity is mediated through the production of RONS and leads to apoptotic cell death in melanoma cells. Furthermore, we show that NTP-treated solutions elicit an antioxidant response through the activation of nuclear factor erythroid 2–related factor 2 (NRF2). The knock­ down and overexpression of CYGB respectively sensitizes and protects melanoma cells from RONS-induced apoptotic cell death. The presence of CYGB enhances heme-oxygenase 1 (HO-1) and NRF2 protein expression levels, whereas the absence impairs their expression. Moreover, analysis of the CYGB-dependent transcriptome demonstrates the tumor suppressor long non-coding RNA maternally expressed 3 (MEG3) as a hitherto unde­ scribed link between CYGB and NRF2. Thus, the presence of CYGB, at least in melanoma cells, seems to play a central role in determining the therapeutic outcome of RONS-inducing anticancer therapies, like NTP-treated solutions, possessing both tumor-suppressive and oncogenic features. Hence, CYGB expression could be of in­ terest either as a biomarker or as a candidate for future targeted therapies in melanoma.

Abstract: One of the most basic criteria in documenting rangeland use prospects is the recognition of byproducts and their economic appraisal. The current study was conducted to assess the economic indices of exploiting byproduct production in Shahindej, Northwest Iran. For this purpose, 24 by-product-generating species were selected in 114 locations that belong to 49 rangeland units. The total expected value of 24 by-product generating species yield and forage production was calculated at 44.22 USD ha(-1). The results of this study showed that the sustainability of natural resources depends on the effective participation and empowerment of local communities. Furthermore, by-product exploitation contributes significantly to the local economy and employment while also reducing grazing intensity. Overall, the findings of this study show that by-product earnings should be considered in range management schemes and comprehensive natural-area management.

Abstract: Plants have diverse leaf shapes that have evolved to adapt to the environments they have experienced over their evolutionary history. Leaf shape and leaf size can greatly influence the growth rate, competitive ability, and productivity of plants. However, researchers have long struggled to decide how to properly quantify the complexity of leaf shape. Prior studies recommended the leaf roundness index (RI = 4πA/P2) or dissection index (DI = ), where P is leaf perimeter and A is leaf area. However, these two indices merely measure the extent of the deviation of leaf shape from a circle, which is usually invalid as leaves are seldom circular. In this study, we proposed a simple measure, named the ellipticalness index (EI), for quantifying the complexity of leaf shape based on the hypothesis that the shape of any oval leaf can be regarded as a variation from a standard ellipse. 2220 leaves from nine species of Magnoliaceae were sampled to check the validity of the EI. We also tested the validity of the Montgomery equation (ME), which assumes a proportional relationship between leaf area and the product of leaf length and width, because the EI actually comes from the proportionality coefficient of the ME. We also compared the ME with five other models of leaf area. The ME was found to be the best model for calculating leaf area based on consideration of the trade-off between model fit vs. complexity, which strongly supported the robustness of the EI for describing oval leaf shape. The new index can account for both leaf shape and size, and we conclude that it is a promising method for quantifying and comparing oval leaf shapes across species in future studies.

Abstract: (Sun)Light is an abundantly available sustainable source of energy that has been used in catalyzing chemical reactions for several decades now. In particular, studies related to the interaction of light with plasmonic nanostructures have been receiving increased attention. These structures display the unique property of localized surface plasmon resonance, which converts light of a specific wavelength range into hot charge carriers, along with strong local electromagnetic fields, and/or heat, which may all enhance the reaction efficiency in their own way. These unique properties of plasmonic nanoparticles can be conveniently tuned by varying the metal type, size, shape, and dielectric environment, thus prompting a research focus on rationally designed plasmonic hybrid nanostructures. In this review, the term “hybrid” implies nanomaterials that consist of multiple plasmonic or non-plasmonic materials, forming complex configurations in the geometry and/or at the atomic level. We discuss the synthetic techniques and evolution of such hybrid plasmonic nanostructures giving rise to a wide variety of material and geometric configurations. Bimetallic alloys, which result in a new set of opto-physical parameters, are compared with core–shell configurations. For the latter, the use of metal, semiconductor, and polymer shells is reviewed. Also, more complex structures such as Janus and antenna reactor composites are discussed. This review further summarizes the studies exploiting plasmonic hybrids to elucidate the plasmonic-photocatalytic mechanism. Finally, we review the implementation of these plasmonic hybrids in different photocatalytic application domains such as H2 generation, CO2 reduction, water purification, air purification, and disinfection.

Abstract: Rational composition design of trimetallic phosphide catalysts is of significant importance for enhanced surface reaction and efficient catalytic performance. Herein, hierarchical CoxNiyFezP with precise control of stoichiometric metallic elements (x:y:z = (1-10):(1-10):1) has been synthesized, and Co1.3Ni0.5Fe0.2P, as the most optimal composition, exhibits remarkable catalytic activity (eta = 320 mV at 10 mA cm(-2)) and long-term stability (ignorable decrease after 10 h continuous test at the current density of 10 mA cm(-2)) toward oxygen evolution reaction (OER). It is found that the surface P in Co1.3Ni0.5Fe0.2P was replaced by 0 under the OER process. The density function theory calculations before and after long-term stability tests suggest the clear increasing of the density of states near the Fermi level of Co1.3Ni0.5Fe0.2P/ Co1.3Ni0.5Fe0.2O, which could enhance the OH- adsorption of our electrocatalysts and the corresponding OER performance.

Abstract: A uniform description of natural shapes and phenomena is an important goal in science. Such description should check some basic principles, related to 1) the complexity of the model, 2) how well its fits real objects, phenomena and data, and 3) ia direct connection with optimization principles and the calculus of variations. In this article, we present nine principles, three for each group, and we compare some models with a claim to universality. It is also shown that Gielis Transformations and power laws have a common origin in conic sections

Abstract: Co-digestion of organic waste and wastewater is receiving increased attention as a plausible waste management approach toward energy recovery. However, traditional anaerobic processes for co-digestion are particularly susceptible to severe organic loading rates (OLRs) under long-term treatment. To enhance technological feasi-bility, this work presented a two-stage Anaerobic Membrane Bioreactor (2 S-AnMBR) composed of a hydrolysis reactor (HR) followed by an anaerobic membrane bioreactor (AnMBR) for long-term co-digestion of food waste and kitchen wastewater. The OLRs were expanded from 4.5, 5.6, and 6.9 kg COD m- 3 d-1 to optimize biogas yield, nitrogen recovery, and membrane fouling at ambient temperatures of 25-32 degrees C. Results showed that specific methane production of UASB was 249 +/- 7 L CH4 kg-1 CODremoved at the OLR of 6.9 kg TCOD m- 3 d-1. Total Chemical Oxygen Demand (TCOD) loss by hydrolysis was 21.6% of the input TCOD load at the hydraulic retention time (HRT) of 2 days. However, low total volatile fatty acid concentrations were found in the AnMBR, indicating that a sufficiently high hydrolysis efficiency could be accomplished with a short HRT. Furthermore, using AnMBR structure consisting of an Upflow Anaerobic Sludge Blanket Reactor (UASB) followed by a side -stream ultrafiltration membrane alleviated cake membrane fouling. The wasted digestate from the AnMBR comprised 42-47% Total Kjeldahl Nitrogen (TKN) and 57-68% total phosphorous loading, making it suitable for use in soil amendments or fertilizers. Finally, the predominance of fine particles (D10 = 0.8 mu m) in the ultra -filtration membrane housing (UFMH) could lead to a faster increase in trans-membrane pressure during the filtration process.

Abstract: Groundwater fluxes in tidal marshes largely control key ecosystem functions and services, such as vegetation growth, soil carbon sequestration, and nutrient cycling. In tidal marshes restored on formerly embanked agricultural land, groundwater fluxes are often limited as compared to nearby natural marshes, as a result of historical agricultural soil compaction. To improve the functioning of restored tidal marshes, knowledge is needed on how much certain design options can optimize soil-groundwater interactions in future restoration projects. Based on measured data on soil properties and tidally induced groundwater dynamics, we calibrated and evaluated a 2D vertical model of a creek-marsh cross-section, accounting for both saturated and unsaturated groundwater flow and solute transport in a variably saturated groundwater flow model. We found that model simulations of common restoration practices such as soil amendments (increasing the depth of porous soil on top of the compact layer) and creek excavation (increasing the creek density) increase the soil aeration depth and time, the drainage depth and the solute flux, and decrease the residence time of solutes in the porewater. Our simulations indicate that increasing the depth to the compact layer from 20 cm to 40 cm, or increasing the creek density from 1 creek to 2 creeks along a 50 m marsh transect (while maintaining the total creek cross-sectional area), in both cases more than doubles the volume of water processed by the marsh soil. We discuss that this may stimulate nutrient cycling. As such, our study demonstrates that groundwater modelling can support the design of marsh restoration measures aiming to optimize groundwater fluxes and related ecosystem services.

Abstract: Gielis transformations are a generalization of Lame curves. To combine domains, we can make use of the natural alliance between Lame's work and Rvachev's R-functions. A logical next step is the extension to n-valued logic dening dierent partitions.

Abstract: Phenolic compounds can be found everywhere in our daily lives but exhibit high toxicity, low (bio)degradability and hormone-disrupting effects when they are released in the environment. It is for this reason imperative to develop detection strategies for these pollutants. A promising approach involves the use of a photoelectrochemical (PEC) sensor. In this sensor, a photosensitiser (PS) type II, which generates 1O2 under illumination, is used to oxidise phenolic compounds present in the sample. The oxidised phenols are reduced at the electrode surface leading to the generation of an electrocatalytic redox cycle. In this thesis, an in-depth understanding, through the identification of the reactive oxygen species (ROS) in the PEC sensing mechanism, is obtained. The detection strategy is optimised by choosing the PS with the highest 1O2 production and by optimising the detection parameters so that the PEC sensor can be successfully applied for the detection of phenols in industrial samples. First, it was determined that the use of highly fluorinated zinc phthalocyanine derivatives, F52PcZn and F64PcZn, as photocatalysts was optimal for the sensing of phenol due to their high 1O2 production and improved single-site isolation. However, next to 1O2, it was shown that the ROS O2•- and H2O2 were also generated in the PEC sensor. Their contribution to the photocurrent response was studied by rotating disk electrode measurements in function of the pH and applied potential. After this, the PEC detection strategy was optimised in terms of pH and applied potential for the detection of doxycycline, cefadroxil, and phenol. It was found that the use of alkaline pH-levels led to nmol L-1-level detection limits. The combination with square wave voltammetry (SWV) was, also, proposed to allow the quantification and identification of phenolic compounds in a specific sample. At last, the developed PEC and SWV sensors were applied for the measurement of phenolic compounds in industrial water samples. The PEC sensor could follow the decrease of the phenolic concentration throughout the wastewater treatment process while the SWV sensor provided the electrochemical fingerprints of these samples. The thesis concluded that the use of the PEC sensor was advantageous in the measurement of lower concentrated phenolic samples due to its high sensitivity and fast measurement time in comparison to commercial test kits.

Abstract: The nonreciprocal charge transport property which depends on the polarity of the applied current, such as the diode effect and the rectification effect, is of great importance for both theoretical research and engineering application. The nonreciprocal transport property in superconductors generally requires to break both the spatial inversion symmetry and the time-reversal symmetry, and therefore becomes one of the fundamental issues in superconductivity. Of particular interest, the superconducting diode effect, which exhibits one-way superconductivity, can potentially be applied to dissipationless diode devices, as a consequence has received extensive attention in recent years. In this Ph. D thesis, we simulate vortex dynamics with heat dissipation by numerically solving time-dependent Ginzburg-Landau equations and heat transfer equation. The nonreciprocal transport properties of the following three superconducting systems are studied. We study a superconducting film patterned with a conformal pinning array and find a giant rectification effect which is consistent with the experimental observation. In presence of the funneling effect due to the geometry of the conformal pinning array, Joule heating of the accumulating vortices creates hot spots and drives the sample to the normal state. Meanwhile, the density gradient of vortex does not match the gradient of pinning. The two mechanisms together lead to the giant rectification effect. We study the nonreciprocal charge transport property in a pinning-free superconducting nano-ring. We systematically calculate the response of the ratchet signal to various parameters in both D.C. and A.C. currents. By analyzing the vortex potential, we find that the nonreciprocal transport property is caused by the asymmetry potential barriers for vortex entry and exit. We study a superconductor/nanoscale-magnetic-dot hybrid structure. It takes advantage of the external current to control the nucleation of vortex-antivortex pairs, and can produce superconducting diode effect without applied magnetic fields. Our vortex dynamics simulation details the progress of the superconducting-normal phase transition due to motion of vortex pairs and heat dissipation. The nonreciprocal transport properties of the above three systems are all based on the broken symmetry of spatial inversion, which is caused by the anisotropic pinning array, the asymmetric geometry, and the nonuniform distribution of the magnetic field, respectively. The mechanisms we discuss in this thesis do not require special property of the materials and thus can be applied to any kinds of conventional superconductors. The present studies would provide solid theoretical basis for the future design and application of the dissipationless superconducting devices.

Abstract: Nanomaterials are materials that have at least one dimension in the nanometer length scale, which corresponds to a billionth of a meter. When three dimensions are confined to the nanometer scale, these materials are referred to as nanoparticles. These materials are of great interest since they exhibit unique physical and chemical properties that cannot be observed for bulk systems. Due to their unique and often superior properties, nanomaterials have become central in the field of electronics, catalysis, and medicine. Moreover, they are expected to be one of the most promising systems to tackle many challenges that our society is facing, such as reducing the emission of greenhouse gases and finding effective treatments for cancer. The unique properties of nanomaterials are linked to their size, shape, structure, and composition. If one is able to measure the positions of the atoms, their chemical nature, and the bonding between them, it becomes possible to predict the physicochemical properties of nanomaterials. In this manner, the development of novel nanostructures can be triggered. However, the morphology and structure of nanomaterials are highly sensitive to the conditions for relevant applications, such as elevated temperatures or intense light illumination. Furthermore, any small change in the local structure at higher temperatures or pressures may significantly modify their performance. Hence, three-dimensional (3D) characterization of nanomaterials under application-relevant conditions is important in designing them with desired functional properties for specific applications. Among different structural characterization approaches, transmission electron microscopy (TEM) is one of the most efficient and versatile tools to investigate the structure and composition of nanomaterials since it can provide atomically resolved images, which are sensitive to the local 3D structure of the investigated sample. However, TEM only provides two-dimensional (2D) images of the 3D nanoparticle, which may lead to an incomplete understanding of their structure-property relationship. The most known and powerful technique for the 3D characterization of nanomaterials is electron tomography, where the images of a nanostructured material taken from different directions are mathematically combined to retrieve its 3D structure. Although these experiments are already state-of-the-art, 3D characterization by TEM is typically performed under ultra-high vacuum conditions and at room temperature. Such conditions are unfortunately not sufficient to understand transformations during synthesis or applications of nanomaterials. This limitation can be overcome by in situ TEM where external stimuli, such as heat, gas, and liquids, can be controllably introduced inside the TEM using specialized holders. However, there are some technical limitations to successful perform 3D in situ electron tomography experiments. For example, the long acquisition time required to collect a tilt series limits this technique when one wants to observe 3D dynamic changes with atomic resolution. A solution for this problem is the estimation of the 3D structure of nanomaterials from 2D projection images acquired along a single viewing direction. For this purpose, annular dark field scanning TEM (ADF STEM) imaging mode provides a valuable tool for quantitative structural investigation of nanomaterials from single 2D images due to its thickness and mass sensitivity. For quantitative analysis, an ADF STEM image is considered as a 2D array of pixels where relative variation of pixel intensity values is proportional to the total number of atoms and the atomic number of the elements in the sample. By applying advanced statistical approaches to these images, structural information, such as the number or types of atoms, can be retrieved with high accuracy and precision. The outcome can then be used to build a 3D starting model for energy minimization by atomistic simulations, for example, molecular dynamics simulations or the Monte Carlo method. However, this methodology needs to be further evaluated for in situ experiments. This thesis is devoted to presenting robust approaches to accurately define the 3D atomic structure of nanoparticles under application-relevant conditions and understand the mechanism behind the atomic-scale dynamics in nanoparticles in response to environmental stimuli.

Abstract: MicroRNAs (miRNAs) are small oligonucleotides (18-25 bases) that play a key role in epigenetic regulation. Since the discovery of miRNAs in 1993, their role in biological processes has been extensively investigated. By now, it has been evidenced that monitoring and detection of miRNAs can improve (early) disease diagnosis. The existing diagnostic approaches have limitations such as the need for complex multistep protocols for sample preparation, analysis, data interpretation, high cost of equipment, the need for highly qualified personnel, and high laboratory standards. As opposed to this, point-of-care biosensors and chips aim to facilitate the procedure and avoid sending samples into centralized laboratories, which saves time, reduces the chance of sample degradation, and enables analysis of patient samples in remote areas, directly at home or primary and secondary care facilities (i.e. general practitioners and specialists). The latter is essential for therapy assessment and follow-up monitoring of patients with chronic diseases and cancer. Nevertheless, the development of such sensors is lagging compared to the projections of 10 – 20 years ago, mainly due to insufficient sensitivity, poor reproducibility, and the complexity of the sensors’ design reported in the research literature. This motivates the development of new detection strategies and technologies such as photoelectrochemical sensors that combine the best features of different sensing approaches. The primary concern when developing detection technologies for miRNA is the need for a highly sensitive and selective platform. This thesis explores a novel photoelectrochemical (PEC) method that is distinctive owing to its sensitive nature and simple and robust design. Firstly, we focused on the usage of recently emerging commercial gold-sputtered electrode systems for the detection of short nucleic acid with enzymatic amplification. Importantly, cleaning such electrodes is a challenge since the standard procedures known for regular disk electrodes such as polishing cannot be employed here, since it will damage the protective layer on the electrode. However, the electrodes can be washed and pretreated chemically and/or electrochemically. Thus, a procedure to effectively clean and modify the gold-sputtered electrode has been developed, resulting in high-performance gold-sputtered nucleic acid sensors. Next, the usage of molecular photosensitizers as an alternative to enzymatic amplification has been evaluated. We took advantage of the singlet oxygen production by photosensitizers upon photoexcitation, leading to a photocurrent response due to the singlet oxygen-induced (electro)chemical conversions. Following the demonstration of the detection strategy, the analytical performance of the sensing system was evaluated using magnetic beads-based nucleic acid assay on disposable electrode platforms, with a focus to enhance the sensitivity and robustness of the technique in detecting complementary nucleic acid targets. Following the fundamental evaluation of the singlet oxygen-based PEC detection of nucleic acids, we further optimized the assay and measurement parameters and employed the sensing strategy for a polymerase chain reaction-free (PCR-free) quantification of miRNAs related to prostate cancer. By successfully detecting and quantifying low-picomolar range concentrations (< 10 pM) in plasma samples from prostate cancer patients, we successfully showed the applicability of the novel sensing strategy. We have also compared and positioned the performance of our developed PEC strategy with an existing state-of-art technique, i.e. electrochemiluminescence (ECL). Our PEC strategy performed on par with ECL, both yielding low-picomolar detection limits in serum matrices, however quicker and cheaper than ECL. Owing to the versatility of this PEC technique, the final study explored its multiplexing capability. As a starting point in this branch of the research, we have investigated two possible ways for multiplexing. To perform multiple measurements at the same time, constructing calibration plots and quantifying unknown miRNA concentrations in patient samples at the same time, we have developed a high-throughput detection with 96X multi-channel electrode systems and in-house designed and constructed 96XLED illumination sources. Secondly, to detect more than one target miRNA in a single measurement, intra-vial multiplexing where the samples were analyzed for different targets in one vial was also explored. Altogether, this thesis presents the fundamentals, development and application of a novel PEC strategy for detecting short (< 25 bases) nucleic acid sequences, in particular, miRNA. With an aim to serve as a distinctive technique to function as a clinical testing platform without any need for PCR, this work adds value to the development of nucleic acid-based sensors for miRNAs and other short-stranded nucleic acid biomarkers, and benefits in the early detection of diseases like cancer.