Assessment of EU policy: Implications for the implementation of autonomous desalination units powered by renewable resources in the Mediterranean region

Small-scale autonomous desalination systems (ADS) powered by renewable energy are ideal for implementation in arid or semi-arid regions like the Mediterranean, especially in remote coastal or inland locations. The technology has made significant progress but wide scale implementation may face administrative barriers. This paper analyses the possible barriers imposed on ADS by the Water Framework Directive, the Drinking Water Directive, the EU Water Initiative, the European Mediterranean Partnership, and the European Neighbourhood Policy. It has been found that ADS developers have to comply with unnecessary strict regulations that apply to all drinking water supplies, while the consumers and the environment are not adequately protected from possible effects of desalination. A careful review of the relevant directives and standards has to be undertaken and adapt the documents to prepare for widespread use of ADS. On the other hand, there is widespread support for ADS within EU policy, mainly in the form of support for non-conventional water resources as well as for sustainable and rural development. The ADS community has to make use of this support and form a technology platform that will convince decision makers to improve policy and institutional framework conditions and will raise general awareness on the ADS benefits.     

Transformation between flocs and aggregates in the conditioned sludge suspensions during a rotational test using a torque rheometer

Floccky tester is a typical torque rheometer used to study flocculants and flocculation characteristics of wastewater sludges. This study presents the variations in rheological, geometric characteristics of conditioned anaerobic digested sludge (ADS) to analyze the transformation between flocs and aggregates during a rotational test with a floccky rheometer. The results revealed that the network strength and resistance to shear reach their maximum values at the optimum dose of polymer zetag7557 on the basis of the area under torque-time curves of ADS. Large aggregates, even big sludge lumps, were observed as soon as the optimum polymer dose was injected into the ADS suspensions, and they continued to grow in average size as the shear duration was prolonged. Afterwards, these aggregates broke into smaller ones or were directly transformed into smaller and smaller flocs with the extension of shear application. The corresponding torque values in the torque-time curve showed similar behavior to the above variations. During the above process, the strongest network formed in conditioned ADS suspensions in which large aggregates were generated, while the ADS suspension with the largest single aggregate or the highest average size of aggregates/flocs did not assure the strongest network. The climax of fractal dimensions for conditioned ADS flocs were not coincident with that of the peak height in the torque-time curves for these ADS suspensions.     

Candidate Gene Discovery in Hereditary Colorectal Cancer and Polyposis Syndromes–Considerations for Future Studies

To discover novel high-penetrant risk loci for hereditary colorectal cancer (hCRC) and polyposis syndromes many whole-exome and whole-genome sequencing (WES/WGS) studies have been performed. Remarkably, these studies resulted in only a few novel high-penetrant risk genes. Given this observation, the possibility and strategy to identify high-penetrant risk genes for hCRC and polyposis needs reconsideration. Therefore, we reviewed the study design of WES/WGS-based hCRC and polyposis gene discovery studies (n = 37) and provide recommendations to optimize discovery and validation strategies. The group of genetically unresolved patients is phenotypically heterogeneous, and likely composed of distinct molecular subtypes. This knowledge advocates for the screening of a homogeneous, stringently preselected discovery cohort and obtaining multi-level evidence for variant pathogenicity. This evidence can be collected by characterizing the molecular landscape of tumors from individuals with the same affected gene or by functional validation in cell-based models. Together, the combined approach of a phenotype-driven, tumor-based candidate gene search might elucidate the potential contribution of novel genetic predispositions in genetically unresolved hCRC and polyposis.     

Rheological and curing properties of reactive blending products of epoxidised natural rubber and cassava starch

Abstract

Epoxidised natural rubber (ENR) has been prepared and used as a blending ingredient together with a compatibiliser for blending of natural rubber (air dry sheet, ADS) and cassava starch. Mooney viscosities of the blends were quantified at 100°C and rheological properties in terms of shear stress and shear viscosity were plotted against shear rates. The results showed that pure ENR gave a lower Mooney viscosity, shear stress, and shear viscosity than blends with cassava starch. Mooney viscosity, shear stress, and shear viscosity for the blends increased with increasing levels of starch. At the same level of cassava starch blended, the highest values of these quantities were observed for the blends with ENR. The blend of ADS with ENR as a compatibiliser showed lower values than those of ENR itself, but higher than those of ADS with the starch. The results are described in terms of the level of chemical interaction between polar groups in ENR and in cassava starch. Curing behaviour for compounds of ENR, ADS, and ADS with ENR as a compatibiliser were studied. The results found that ENR exhibited a long delay (～ 10 min) before the vulcanisation took place compared with 1 min for ADS compounds. In the curing curve for ENR, an equilibrium value at maximum torque was not found indicating that the stiffness of the ENR compounds still increased with increasing testing time until 60 min. Stiffness of the ENR compounds also increased with increasing levels of cassava starch.     

Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning

Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.     

Fragment‐Based Phenotypic Lead Discovery: Cell‐Based Assay to Target Leishmaniasis

A rapid and practical approach for the discovery of new chemical matter for targeting pathogens and diseases is described. Fragment‐based phenotypic lead discovery (FPLD) combines aspects of traditional fragment‐based lead discovery (FBLD), which involves the screening of small‐molecule fragment libraries to target specific proteins, with phenotypic lead discovery (PLD), which typically involves the screening of drug‐like compounds in cell‐based assays. To enable FPLD, a diverse library of fragments was first designed, assembled, and curated. This library of soluble, low‐molecular‐weight compounds was then pooled to expedite screening. Axenic cultures of Leishmania promastigotes were screened, and single hits were then tested for leishmanicidal activity against intracellular amastigote forms in infected murine bone‐marrow‐derived macrophages without evidence of toxicity toward mammalian cells. These studies demonstrate that FPLD can be a rapid and effective means to discover hits that can serve as leads for further medicinal chemistry purposes or as tool compounds for identifying known or novel targets.     

Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet,Classical Virtual Screening and Docking

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy.     

Hydrophobicity regulation by a facial bromine substitution in MIL-47(V) for highly selective adsorptive desulfurization

Metal organic frameworks (MOFs) have shown promising adsorptive desulfurization (ADS) capacity yet remains great challenges to maintain excellent performance in presence of water. Guided by the density function theoretical (DFT) calculations, we select bromine modified linkers with different substitution degrees to construct MIL-47-xBr to regulate the porosity and hydrophobicity. The optimized MIL-47-Br exhibits the highest saturation dibenzothiophene (DBT) capacity of 179.2 mgS g⁻¹ from the hydrous oil, surpassing the record of all ADS adsorbents. Meanwhile, the excellent selectivity, adsorption kinetics, and recycle stability endow MIL-47-Br with superior ADS performance from the commercial hydrous gasoline. With the DBT capacity decreases by only 12.3% from the commercial gasoline, a continue flow ADS is successfully achieved on the fixed-bed of MIL-47-Br column. Therefore, this facial strategy of simple substitution can fully exploit the functions of open metal sites for enhancing the task-specific selectivity, and has a promising application prospect in industrial ADS.     

Assessment of institutional and policy conditions in Turkey: implications for the implementation of autonomous desalination systems

This paper provides an overview of the existing situation in Turkey and studies how this could become more attractive for the implementation of autonomous desalination systems (ADS). The analysis covers relevant policy and programmes, legislation and administrative procedures, water prices and subsidies, institutional framework of the water sector, capacity building and general awareness. In general, the framework conditions in Turkey are relatively good. The policy direction towards decentralisation will favour ADS. Many programmes offer support for water supply and renewable energy and there are simplified administrative procedures. On the other hand, water policy favours traditionally centralised conventional solutions. The awareness about ADS is very low and the research in the field still restricted. In this paper, concrete recommendations have been developed that suggest simple steps for the establishment of ADS in Turkey.     

A RaPID Macrocyclic Peptide That Inhibits the Formation of α-Synuclein Amyloid Fibrils

There is considerable interest in drug discovery targeting the aggregation of α-synuclein (αSyn) since this molecular process is closely associated with Parkinson's disease. However, inhibiting αSyn aggregation remains a major challenge because of its highly dynamic nature which makes it difficult to form a stable binding complex with a drug molecule. Here, by exploiting Random non-standard Peptides Integrated Discovery (RaPID) system, we identified a macrocyclic peptide, BD1, that could interact with immobilized αSyn and inhibit the formation of fibrils. Furthermore, improving the solubility of BD1 suppresses the co-aggregation with αSyn fibrils while it kinetically inhibits more effectively without change in their morphology. We also revealed the molecular mechanism of kinetic inhibition, where peptides bind to fibril ends of αSyn, thereby preventing further growth of fibrils. These results suggest that our approach for generating non-standard macrocyclic peptides is a promising approach for developing potential therapeutics against neurodegeneration.     

Optogenetics Comes of Age: Novel Inhibitory Light‐Gated Anionic Channels Allow Efficient Silencing of Neural Function

Optogenetics, the developing field of research that uses light‐switchable biochemical tools in a sophisticated technological approach to monitor or control neural function, is rapidly evolving with the discovery and development of novel microbial rhodopsins. Light‐absorbing membrane proteins, as tools for brain research, are promoting new applications within the discipline of optogenetics. Light‐gated rhodopsin ion channels with better intrinsic light sensitivity and improved resolution are needed to overcome some of the current limitations of existing molecules. The recent discovery of light‐gated inhibitory anion channels opens new opportunities for studying physiological neural processes and, at the same time, represent a powerful approach for elucidating the mechanisms of neurological and mental disorders that could benefit from this approach.     

From theory to bench experiment by computer-assisted drug design

Tight integration of computer-assisted molecular design with practical realization by medicinal chemistry will be essential for finding next-generation drugs that are optimized for multiple pharmaceutically relevant properties. ETH Zürich has established an interdisciplinary research group devoted to exploring the potential of this scientific approach by combining expertise from pharmaceutical chemistry and computer sciences. In this article, some of the group's activities and projects are presented. A current focus is on machine-learning applications aiming at hit and lead structure identification by virtual screening and de novo design. The central concept of 'adaptive fitness landscapes' is highlighted along with practical examples from drug discovery projects.     

Investigation of dissolution inhibitors for electrochemical mechanical planarization of copper using beta-alanine as a complexing agent

Dissolution inhibition capabilities of benzotriazole (BTAH) and ammonium dodecyl sulfate (ADS) are investigated, in combination with β-alanine, as a complexing agent for applications in electrochemical mechanical planarization (ECMP) of copper. Cu electrodissolution is induced by linear sweep voltammetry (LSV), and Fourier transform electrochemical impedance spectroscopy (FT-EIS) is combined with LSV to examine the relative roles of the electrolyte additives in governing the surface reactions of Cu under voltage activated conditions of ECMP. The experiments focus on the electrochemical rather than mechanical component of ECMP, and are designed to probe both the individual and combined effects of BTAH and ADS on Cu electro-dissolution in the absence of abrasion.     

Lessons in Organic Fluorescent Probe Discovery

Fluorescent probes have gained profound use in biotechnology, drug discovery, medical diagnostics, molecular and cell biology. The development of methods for the translation of fluorophores into fluorescent probes continues to be a robust field for medicinal chemists and chemical biologists, alike. Access to new experimental designs has enabled molecular diversification and led to the identification of new approaches to probe discovery. This review provides a synopsis of the recent lessons in modern fluorescent probe discovery.     

Spin labels as a tool to identify and characterize protein-ligand interactions by NMR spectroscopy

NMR spectroscopy based discovery and optimization of lead compounds for a given molecular target requires the development of methods with maximum sensitivity and robustness. It is shown here that organic nitroxide radicals ("spin labels") can be used to boost the sensitivity of NMR spectroscopic screening in drug discovery research. The concept of utilizing spin labels in NMR spectroscopy is summarized, examples for successful first-site and second-site NMR spectroscopic screens are given, and guidelines for linker design are presented.     

Integration of Multiple Analytical and Computational Tools for the Discovery of High‐Potency Enzyme Inhibitors from Herbal Medicines

Herbal medicines (HMs) are an important source of drugs. In this study, an efficient strategy integrating ultrafiltration LC–MS, microplate bioassays, and molecular docking was proposed to screen high‐potency enzyme inhibitors from HMs. Using this strategy, the structure–activity relationships (SARs) including binding‐affinity‐based SAR, enzymatic‐activity‐based SAR, and structural‐complementarity‐based SAR of compounds in an HM can be analyzed to provide abundant information for drug discovery. A prominent advantage of the approach is that it offers a multidimensional perspective to understand enzyme–ligand interactions, which could help to avoid false‐positive screening results brought by a single method. By using xanthine oxidase (XOD) as an illustrative case, two types of potent XOD inhibitors, including flavones and coumarins, were successfully screened out from an HM of Ginkgo biloba. The study is expected to set a solid foundation for multidisciplinary cooperation in drug discovery.     

Search for the most stable folds of protein chains: I. Application of a self-consistent molecular field theory to a problem of protein three-dimensional structure prediction

We present a general approach to the prediction of 3-D foldsof protein chains from their amino acid sequences. The approachis based on the use of the self-consistent molecular field theoryfor long-range interactions, the use of 1-D statistical mechanicsfor short-range interactions and on the discovery that thereis and should only be a relatively small discrete set of foldingpatterns. This makes it possible to examine the full varietyof ‘potentially stable’ folds and to determine thethermodynamically stable structure. In this paper, we give thegeneral theoretical background of the approach. The encouragingresults of the application of this approach to ß-domainsare described in another paper.     

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.