Seafood is considered one of the main food allergen sources by the European Food Safety Authority (EFSA). It comprises several distinct groups of edible aquatic animals including fish and shellfish such as crustacean and mollusks. Recently the EFSA recognized the high risk of food allergy over the world and established the necessity of developing new methodologies for its control. Consequently, accurate, sensitive and fast detection methods for seafood allergy control and detection in food products are highly recommendable. In this work, we present a comprehensive review of the applications of the proteomics methodologies for the detection and quantification of seafood allergens. For that, two consecutive proteomics strategies (Discovery and Targeted Proteomics) applied for the study and control of seafood allergy are reviewed in detail. In addition, future directions and new perspectives were also provided.

The new coronavirus outbreak (COVID-19) has swept the world since December 2019 posing a global threat to all countries and communities on the planet. Information about the outbreak has been rapidly spreading on different social media platforms in unprecedented level. As it continues to spread in different countries, people tend to increasingly share information and stay up-to-date with the latest news. It is crucial to capture the discussions and conversations happening on social media to better understand human behavior during pandemics and alter possible strategies to combat the pandemic. In this work, we analyze the Arabic content of Twitter to capture the main discussed topics among Arabic users. We utilize Non-negative Matrix Factorization (NMF) to discover main issues and topics based on a dataset of Arabic tweets from early January to the end of April, and identify the most frequent unigrams, bigrams, and trigrams of the tweets. The final discovered topics are then presented and discussed which can be roughly classified into COVID-19 origin topics, prevention measures in different Arabic countries, prayers and supplications, news and reports, and finally topics related to preventing the spread of the disease such as curfew and quarantine. To our best knowledge, this is the first work addressing the issue of detecting COVID-19 related topics from Arabic tweets.

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

The objective of this study was to review the study design and preliminary results of the Recovery trial and analyze the implementability of the Recovery trial by comparing it with the European Discovery trial.Method: The study design of the Recovery trial in the latest version of protocol was described and deeply analyzed to address the issue of implementation of the trial. A comparative analysis of study design and implementation between the UK Recovery trial and the European Discovery trial was conducted following the description.Results: The Recovery trial is a pragmatic, randomized, controlled, adaptive, open-label clinical trial. The study design of the Recovery trial was reported in the ISRCTN registry, the EU Clinical Trials Register and the U.S. National Library of Medicine ClinicalTrials.gov registry. Initially published on the 13^th March 2020, the study protocol of the Recovery trial has been updated five times at the time of this writing. More than 11,000 patients have been enrolled and 80% have completed the follow-up. Thousands of health care professionals at 175 Trusts in the UK have been involved. Conclusion: The Recovery trial applies a study design to address the issue of implementation in the context of the COVID-19 pandemic and emergency. It was conceptually pragmatic with a clear vision to address the top priority: the control of mortality and rational use of scarce resources. By contrast, the Discovery trial was designed as an intellectual exercise and consequently failed to address the issue of implementation in emergency.

The urgency to develop vaccines against Covid-19 is putting pressure on the long and expensive development timelines which are normally required for development of lifesaving vaccines. There is a unique opportunity to take advantage of new technologies, smart and flexible design of clinical trials, and evolving regulatory science to speed up vaccine development against Covid-19 and transform vaccine development altogether.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide, and there is an urgent need to develop effective drugs. Although there are a large number of candidate drug compounds that may be useful for treating COVID-19, the evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds such as C646, chelerythrine chloride, canertinib, BX-795, sorafenib, sorafenib, QL-X-138, radicicol, A-443654, CGP-60474, alvocidib, mitoxantrone, QL-XII-47, geldanamycin, fluticasone, atorvastatin, quercetin, motexafin gadolinium, trovafloxacin, doxycycline, meloxicam, gentamicin, and dibromochloromethane. The screen also identified ivermectin, which was first identified as an anti-parasite drug and recently the drug was included in clinical trials for SARS-CoV-2. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

The vastness of chemical-space constrains traditional drug-discovery methods to the organic laws that are guiding the chemistry involved in filtering through candidates. Leveraging computing with machine-learning to intelligently generate compounds that meet a wide range of objectives can bring significant gains in time and effort needed to filter through a broad range of candidates. This paper details how the use of Generative-Adversarial-Networks, novel machine learning techniques to format the training dataset and the use of quantum computing offer new ways to expedite drug-discovery.

Citizen Science has come up to perform analytics over the SARS-CoV-2 genome. Public GALAXY servers provide an automated platform for genomics analysis. Study includes design of GALAXY workflows for RNASEQ assembly and annotation as well as genomic variant discovery and perform analysis across four samples of SARS-CoV-2 infected humans obtained from the local population of Wuhan, China. It provides information about transcriptomics and genomic variants across the SARS-CoV-2 genome. Study can be extended to perform evolutionary and comparative study across each species of coronaviruses. Augmented and integrated study with cheminformatics and immunoinformatics will be a way forward for drug discovery and vaccine development.

Beyond their significant contribution to the dietary and industrial supplies, marine algae are considered to be a potential source of some unique metabolites with diverse health benefits. The pharmacological properties, such as antioxidant, anti-inflammatory, cholesterol homeostasis, protein clearance and anti-amyloidogenic potentials of algal metabolites endorse their protective efficacy against oxidative stress, neuroinflammation, mitochondrial dysfunction, and impaired proteostasis which are known to be implicated in the pathophysiology of neurodegenerative disorders and the associated complications after cerebral ischemia and brain injuries. As was evident in various preclinical studies, algal compounds conferred neuroprotection against a wide range of neurotoxic stressors, such as oxygen/glucose deprivation, hydrogen peroxide, glutamate, amyloid β, or 1-methyl-4-phenylpyridinium (MPP+) and, therefore, hold therapeutic promise for brain disorders. While a significant number of algal compounds with promising neuroprotective capacity have been identified over the last decades, a few of them have had access to clinical trials. However, the recent approval of an algal oligosaccharide, sodium oligomannate, for the treatment of Alzheimer's disease enlightened the future of marine algae-based drug discovery. In this review, we briefly outline the pathophysiology of neurodegenerative diseases and brain injuries for identifying the targets of pharmacological intervention, and then review the literature on the neuroprotective potentials of algal compounds along with the underlying pharmacological mechanism, and present an appraisal on the recent therapeutic advances. We also propose a rational strategy to facilitate algal metabolites-based drug development.

The purpose of this paper is to analyze market reflexivity in agricultural futures contracts with different maturities. To this end, we apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. We find market reflexivity for both storable and non-storable commodities. Reflexivity accounts for about 50 to 70 percent of the total trading activity. Differences between nearby and deferred contracts are less pronounced for non-storable than for storable commodities. We conclude that the co-existence of exogenous and endogenous price dynamics does not change qualitative characteristics of the price discovery process that have been observed earlier without consideration of market reflexivity.

Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

Around the World, the number of compound databases of natural products in the public domain is rising. This is in line with the increasing synergistic combination of natural product research and chemoinformatics. Towards this global endeavor, countries in Latin America are assembling, curating, and analyzing the contents and diversity of natural products available in their geographical regions. In this manuscript we collect and analyze the efforts that countries in Latin America have made so far to build natural product databases. We further encourage the scientific community in particular in Latin America, to continue their efforts to building quality natural product databases and, whenever possible, to make them publicly accessible. It is proposed that all compound collections could be assembled into a unified resource called LANaPD: Latin America Natural Products Database. Opportunities and challenges to build, distribute, and maintain LANaPD are also discussed

Viral diseases are one of the leading causes of morbidity and mortality in the world. Virus-specific vaccines and antiviral drugs are the most powerful tools to combat viral diseases. However, broad-spectrum antiviral agents (BSAAs, i.e. compounds targeting viruses belonging to two or more viral families) could provide additional protection of general population from emerging and re-emerging viral diseases reinforcing the arsenal of available antiviral options. Here, we reviewed discovery and development of BSAAs and summarized the information on 120 safe-in-man agents in freely accessible database (https://drugvirus.info/). Future and ongoing pre-clinical and clinical studies will increase the number of BSAAs, expand spectrum of their indications, and identify drug combinations for treatment of emerging and re-emerging viral infections as well as co-infections.

Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as Sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of Sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds.

Ubiquitin-specific protease 14 (USP14) is a member of the Deubiquitinating enzymes (DUBs) involved in disrupting the regulation of the ubiquitin-proteasome system, responsible for the degradation of impaired and misfolded proteins which is an essential mechanism in eukaryotic cells. The involvement of USP14 in cancer progression and neurodegenerative disorders has been reported. Thereof USP14 is a prime therapeutic target; hence, designing efficacious inhibitors of USP14 is central in curbing these conditions. Herein, we relied on structural bioinformatics methods incorporating molecular docking, Molecular Mechanics Generalized Born Surface Area (MM-GBSA), Molecular dynamics simulation (MD simulation) and ADME to identify potential allosteric USP14 inhibitors. A library of over 733 compounds from the PubChem repository with >90% match to the IU1 chemical structure was screened in a multi-step framework to attain prospective drug-like inhibitors. Two potential lead compounds (CID 43013232 and CID 112370349) were shown to record better binding affinity compared to IU1, but with subtle difference to IU1-47, a 10-fold potent compound when compared to IU1. The stability of the lead molecules complexed with USP14 was studied via MD simulations. The molecules were found to be stable within the binding site throughout the 50ns simulation time. Moreover, the protein-ligand interactions across the simulation run time suggest Phe331, Tyr476, and Gln197 as crucial residues for USP14 inhibition. Furthermore, in silico pharmacological evaluation revealed the lead compounds as pharmacological sound molecules. Overall, the methods deployed in this study revealed two novel candidates that may show selective inhibitory activity against USP14, which could be exploited to produce potent and harmless USP14 inhibitors.

Papillomaviruses infect the skin and mucosal surfaces of diverse animal hosts with consequences ranging from asymptomatic colonization to highly malignant epithelial cancers. Increasing evidence suggests a role for papillomaviruses in the most common cutaneous malignancy of domestic cats, squamous cell carcinoma (SCC). Using total DNA sequencing we identified a novel feline papillomavirus in a nasal biopsy taken from a cat presenting with both nasal cavity lymphoma and recurrent squamous cell carcinoma affecting the nasal planum. We designate this novel virus as Felis catus papillomavirus 6 (FcaPV6). The complete FcaPV6 7453 bp genome was similar to those of other feline papillomaviruses and phylogenetic analysis revealed that it was most closely related to FcaPV3, although was distinct enough to represent a new viral species within the genus Taupapillomavirus. Archived excisional biopsy of the SCC, taken 20 months prior to presentation, was intensely positive on p16 immunostaining. FcaPV6, amplified using virus-specific, but not consensus, PCR was the only papillomavirus detected in DNA extracted from the SCC. Conversely, renal lymphoma, sampled at necropsy two months after presentation, tested negative on FcaPV6-specific PCR. In sum, using metagenomics we demonstrate the presence of a novel feline papillomavirus in association with cutaneous squamous cell carcinoma.

Protoflavones, a rare group of natural flavonoids with a non-aromatic B-ring, are best known of their antitumor properties. The protoflavone B-ring is a versatile moiety that may be explored for other pharmacological purposes, but common cytotoxicity of these compounds is a limitation to such efforts. Protoapigenone was previously found to be active against the lytic cycle of Epstein-Barr virus (EBV). Further, the 5-hydroxyflavone moiety is a known pharmacophore against HIV-integrase. The aim of this work was to prepare a series of less cytotoxic protoflavone analogs, and to study their antiviral activity against HIV and EBV. Twenty-seven compounds including 18 new derivatives were prepared from apigenin through oxidative de-aromatization and subsequent continuous-flow hydrogenation, deuteration, and/or 4′-oxime formation. One compound was active against HIV at the micromolar range, and 3 compounds showed significant activity against the EBV lytic cycle at the medium-low nanomolar range. Among these, protoapigenone 1′-O-isopropyl ether (6) was identified as a promising lead due to its 73-times selectivity of its antiviral over its cytotoxic effect, which exceeds that of protoapigenone by 2.4-times. Our results open new opportunities to design new, potent and safe anti-EBV agents based on the natural protoflavone moiety.

Background: Iranian traditional medicine is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified terminology has been proposed for the concepts of this medicine up to the present time. Considering the extensive use of concepts in the numerous textbooks written by the scholars over centuries, comprehending the totality of the terminology is obviously a very challenging task. To resolve this issue and overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.Methods: Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of Mizaj. The steps followed to accomplish this task included (1) compiling the list of classes for Mizaj; (2) arranging the classes in taxonomy; (3) determining object properties and their cardinalities; (4) specifying annotation properties including codes, labels, synonyms, and definitions for each concept; (5) reviewing the fields pertaining to Mizaj of all monographs in Makhzan al-Advieh. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry. Results: Mizaj ontology was constructed with a final inclusion of 105 classes, three object properties, and 1078 axioms in the Iranian Traditional Medicine General Ontology database, IrGO, freely available at http://jafarilab.com/irgo/. An indented tree view and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in the UNaProd database to create a knowledge-base of Mizaj. Conclusion: We constructed an ontology-based knowledge base of ITM concepts of Mizaj in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. Extending IrGO will bridge the gap between traditional and conventional schools of medicine and help guide future research on new treatment options.

Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

Pseudomonas aeruginosa is an emerging opportunistic pathogen responsible for cystic fibrosis and nosocomial infections. In addition, empirical treatments are become inefficient due to their multiple-antibiotic resistance and extensive colonizing ability. Quorum sensing (QS) plays a vital role in the regulation of virulence factors in P. aeruginosa. Attenuation of virulence by QS inhibition could be an alternative and effective approach to control infections. Therefore, we sought to discover new QS inhibitors (QSIs) against LasR receptor in P. aeruginosa using chemoinformatics. Initially, a structure-based high-throughput virtual screening was performed using the LasR active site that identified 61404 relevant molecules. E-pharmacophore (ADAHH) screening of these molecules rendered 72 QSI candidates. In standard-precision docking, only 7 compounds were found as potential QSIs due to their higher binding affinity to LasR receptor (-7.53 to -10.32 kcal/mol compared to -7.43 kcal/mol of native ligands). The ADMET properties of these compounds were suitable to be QSIs. Later, extra-precision docking and binding energy calculation suggested ZINC19765885 and ZINC72387263 as the most promising QSIs. The dynamic simulation of the docked complexes showed good binding stability and molecular interactions. The current study suggested that these two compounds could be used in P. aeruginosa QS inhibition to combat bacterial infections.

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have got interest following the recent publication of their pharmacologically beneficial effects. Recently it comes out that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered but, unfortunately, none of them is in the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches were already performed for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. 14,492 compounds were retrieved from this database and filtered through a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 79–245 nM. ADMET properties prediction were performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives; from these analyses, three molecules resulted as excellent candidates for becoming new drugs.

Background: Ongoing molecular profiling studies enabled by advances in biomedical technologies are producing vast amounts of ‘omic’ data for early detection, monitoring, and prognosis of diverse diseases. A major common limitation is the scarcity of biological samples, necessitating integrative modeling frameworks that can make optimal use of available data for disease classification tasks. Related data sets are often available from different studies, but may have been generated using different technology platforms. Thus, there is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate the discovery of robust biomarkers that underlie disease regulatory processes. Results: In this paper, we introduce a novel framework called Knowledge Augmented Rule Learning (KARL), which incorporates two sources of knowledge, domain, and data, for pattern discovery from small and high-dimensional datasets, such as transcriptomic data. We propose KARL as a transfer rule learning framework in which knowledge of the domain is transferred to the learning process on data in order to 1) improve the reliability of the discovered patterns, and 2) study the knowledge of the domain when used along with data for modeling. In this work, we generated KARL models on gene expression datasets for five types of cancer, including brain, breast, colon, lung, and prostate. As our knowledge of the domain, we used the Ingenuity Knowledge Base (IKB) to extract genes related to hallmarks of cancer and annotated these prior relationships before learning classifiers from these datasets. Conclusions: Our results show that KARL produces, on average, rule models that are more robust classifiers than the baseline without such background knowledge, for our tasks of cancer prediction using 25 publicly available gene expression datasets. Moreover, KARL helped us learn insights about previously known relationships in these gene expression datasets, along with new relationships not input as known, to enable informed biomarker discovery for cancer prediction tasks. KARL can be applied to modeling similar data from any other domain and classification task. Future work would involve extensions to KARL to handle hierarchical knowledge to derive more general hypotheses to drive biomedicine.

Fragment-based drug discovery (FBDD) has become a major strategy to derive novel lead candidates for various therapeutic targets, as it promises efficient exploration of chemical space by employing fragment-sized (MW < 300) compounds. One of the first challenges in implementing a FBDD approach is the design of a fragment library, and more specifically, the choice of its size and individual members. A diverse set of fragments is required to maximise the chances of discovering novel hit compounds. However, the exact diversity of a certain collection of fragments remains underdefined, which hinders direct comparisons among different selections of fragments. Based on structural fingerprints, we herein introduced quantitative metrics for the structural diversity of fragment libraries. Structures of commercially available fragments were retrieved from the ZINC database, from which libraries with sizes ranging from 100 to 100,000 compounds were selected. The selected libraries were evaluated and compared quantitatively, resulting in interesting size-diversity relationships. Our results demonstrated that while library size does matter for its diversity, there exists an optimal size for structural diversity. It is also suggested that such quantitative measures can guide the design of diverse fragment libraries under different circumstances.

The discovery and settlement of the tiny and remote Easter Island (Rapa Nui) has been a classical controversy for decades. Present-day aboriginal people and their culture are undoubtedly of Polynesian origin but it has been debated whether Native Americans discovered the island before the Polynesian settlement. Until recently, the paradigm was that Easter Island was discovered and settled just once by Polynesians in their millennial-scale eastward migration across the Pacific. However, the evidence for cultivation and consumption of an American plant, the sweet potato (Ipomoea batatas), on the island before the European contact (1722 CE), even prior to the Europe-America contact (1492 CE), revived the controversy. This paper reviews the classical archaeological, ethnological and paleoecological literature on the subject and summarizes the information into four main hypotheses to explain the sweet potato enigma: the long-distance-dispersal hypothesis, the back-and-forth hypothesis, the Heyerdahl hypothesis and the newcomer’s hypothesis. These hypotheses are evaluated in light of the more recent evidence (last decade), including molecular DNA phylogeny and phylogeography of humans and associated plants and animals, physical anthropology (craniometry, dietary analysis) and new paleoecological findings. It is concluded that, with the available evidence, none of the former hypotheses may be rejected and, therefore, all possibilities remain open. For future work, it is recommended to use the multiple-working-hypothesis framework and the strong inference method of hypothesis testing, rather than the ruling theory approach, very common in Easter Island’s research.

Inspired by recent work of Fink, Reeves, Palma and Farr [1] on innovation in language, gastronomy, and technology, I study how new symbol discovery manifests itself in terms of additional "word" vocabulary being available from dictionaries generated from a finite number of symbols. Several distinct dictionary generation models are investigated using numerical simulation, with emphasis on the scaling of knowledge as dictionary generators and parameters are varied, and the role of which order the symbols are discovered in.

Koalas (Phascolarctos cinereus) are native Australian marsupials whose populations are in decline from a range of threats. Infectious diseases caused by the bacterium Chlamydia pecorum and other pathogens are of particular concern. We analysed 26 poly-A selected RNA-sequencing libraries from a data set designed to study the immune response of koalas to ocular chlamydial infection. Using virus discovery techniques, we identified the coding-complete genome sequence of a novel picorna-like virus, denoted Burpengary virus, that was most common in south-east Queensland. Notably, abundance measurements of the virus across all 26 libraries revealed an inverse relationship in prevalence with ocular disease in Koalas, suggesting that co-infection between Burpengary virus and Chlamydia pecorum is inhibited.

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context to which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico-chemical properties of the amino acids (native vs. mutant) can still have minimal effects of protein thermodynamics. However, if a mutation causes significant perturbation of either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize on the effects of mutations on two important biophysical characteristics, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical characteristics.

Mycobacteria are a large family of over 100 species, most of which do not cause diseases in humans. The majority of the mycobacterial species are referred to as nontuberculous mycobacteria (NTM), meaning they are not the causative agent of tuberculous (TB) or leprosy, i.e. Mycobacterium tuberculous complex and Mycobacterium leprae, respectively. The latter group is undoubtedly the most infamous, with TB infecting an estimated 10 million people and causing over 1.2 million deaths in 2017 alone (1). TB and leprosy also differ from NTM in that they are only transmitted from person to person and have no environmental reservoir, whereas NTM infections are commonly acquired from the environment (2) (3). It took until the 1950's for NTM to be recognised as a potential lung pathogen in people with underlying pulmonary disease and another 3 decades for NTM to be widely recognised by the medical community when NTM, particularly Mycobacterium avium complex (MAC) was recognised as the most common group of opportunistic pathogens in AIDS patients (4). This review focusses on an emerging NTM called Mycobacterium abscessus (M. abs). M. abs is a rapidly growing (RGM) NTM that is responsible for opportunistic pulmonary infections in patients with structural lung disorders such as cystic fibrosis (CF) and bronchiectasis (5), as well as a wide range of skin and soft tissue infections (SSTIs) in humans (6) (7). M. abs is a weakly staining Gram-positive mycobacterium that is neverand is, like other NTM, most often seen in soil and aquatic environments (8). The bacillus-shaped bacterium is 1-6µm long and 0.2-0.5µm in diameter, with curved ends and the presence of cord factor, or trehalose 6-6'-dimycolate, a glycolipid found in the cell wall of virulent species of mycobacteria that results in "serpentine cord" cell morphology is sometimes observed (8) (9). On solid growth medium, M. abs can display either a rough (M. abs-R) or smooth (M. abs-S) morphotype, with the rough morphotype displaying a more virulent phenotype than its smooth variant (10). The rough morphotype is characterised by irregular parallel filaments that form ridges across the colony, whereas a smooth morphology is displays a wet, smooth colony with no filaments or ridges (79). This morphology is driven by cell wall glycopeptidolipid (GPL); a loss of GPL results in the reversion from rough to smooth morphotype (80) (81). Moreover, it has been shown using human tissue culture models of infection that M. abs-R is able to persist and multiply within the host macrophage whereas M. abs-S lacks this capacity, hence its role in virulence (82). Like all other mycobacteria, M. abs are aerobic, non-motile and acid-fast organisms with a characteristically thick, lipid-rich cell wall that is hydrophobic. Due to their unusually impermeable, thick cell wall, mycobacteria are notoriously resistant to many antibiotics, disinfectants and heavy metals (11). When the genome of M. abs became available in 2009, elucidation of the resistance mechanisms of M. abs became an area of focus for scientific research, as the considerable threat it poses to public health became more apparent (12) (13) (14). In this review we will discuss how we came to understand the pathogen, how it is currently treated, as well as a discussion of drug resistance mechanisms and novel treatments currently in development.

Compound databases of natural products have a major impact on drug discovery projects and other areas of research. The number of databases in the public domain with compounds from natural origin is increasing. Several countries have initiatives in place to construct and maintain compound databases that are representative of their diversity. Examples are Brazil, France, Panama and recently Vietnam. Herein, we discuss the first version of BIOFACQUIM, a novel compound database with natural products isolated and characterized in Mexico. We discuss its construction, curation, and a complete chemoinformatic characterization of the content and coverage in chemical space. It is reported the profile of physicochemical properties, scaffold content, and diversity, as well as structural diversity based on molecular fingerprints. BIOFACQUIM is freely available.

In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine metiltransferase (G9a) and DNA metiltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity difference. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets.

We discuss further details on the concepts of “drug-likeness”, “lead-likeness”, and “natural product-likeness”. The discussion will first focus on natural products as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semi-synthetic compounds and FDA-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. We end up by presenting a tool for measuring “natural product-likeness” of compounds and a brief presentation of machine learning approaches and a binary quantitative structure-activity relationship (QSAR) for classifying drugs from non-drugs and natural compounds from non-natural ones, respectively.

Music acoustics is an interdisciplinary field and mathematics is the basis in the music art form. Music and mathematics correlation exist since the inception of music. Various philosophers, scientists, mathematicians and musicians have expressed their views about this relationship. This paper attempts to explore this association with focus on melodic pattern identification. Mathematics in Indian Classical music with raga as the basis and just intonation tuning system is discussed. Indian vocal music clips are used for different pitch estimation algorithms in the experimentation. Harmonic product spectrum and auto-correlation algorithms are tested for accurate pitch estimation. Enhanced auto-correlation function using audio segmentation is compared with other approaches for effective pitch extraction. Results indicate pitch extraction with enhanced auto-correlation function provides accurate results as compared with other approaches tested.

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. An accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100–1000 fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria results in more drugs that could be validated in the biomedical literature than the list suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

Tuberculosis (TB) is the primary cause of death by a single infectious agent; responsible for around two million deaths in 2016. A major virulence factor of TB is the ability to enter a latent or Non-Replicating Persistent (NRP) state which is presumed untreatable. Approximately, 1.7 billion people are latently infected with TB and on reactivation many of these infections are drug resistant. As the current treatment is ineffective and diagnosis remains poor, millions of people have the potential to reactivate into active TB disease. The immune system seeks to control the TB infection by containing the bacteria in a granuloma, where it is exposed to stressful anaerobic and nutrient deprived conditions. It is thought to be these environmental conditions that trigger the NRP state. A number of in vitro models have been developed that mimic conditions within the granuloma to a lesser or greater extent. These different models have all been utilised for the research of different characteristics of NRP Mycobacterium tuberculosis, however their disparity in approach and physiological relevance often results in inconsistencies and a lack of consensus between studies. This review provides a summation of the different NRP models and a critical analysis of their respective advantages and disadvantages relating to their physiological relevance.

Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5,230 new putative conotoxin transcripts that have no homologues in current reference databases. 893 of these were identified by at least 3 out of 4 models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families.

Coordinated sets of extremely numerous digital data, on a given social or economic event, are treated by Artificial Intelligence tools to obtain reasonably accurate, valuable predictions. The same approach, applied to biomedical issues, as how to choose the right drug to completely cure a given cancer patient, does not reach satisfactory results. It is the “organized biological complexity”, which requires a different systems approach, to integrate, in an Augmented Intelligence strategy, statistical computations of digital data, network construction of “omics” findings, well-designed mathematical models and new experiments in an iterative pathway to reconstruct the “logic” beneath the “organized complexity”, as shown here for Systems Metabolomics of cancer. On this basis new diagnostic approaches, able to identify precision drug treatments, as well as new discovery strategy for more effective anti-cancer drugs are described.

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling and other computational approaches that have been used to uncover natural products as inhibitors of DNA metiltransferases, a major family of epigenetic targets with significant potential for the treatment of cancer and several other diseases. Examples of these computational approaches include docking, similarity-based virtual screening, and pharmacophore modeling. It is also commented the chemoinformatic-based exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

Epistemological naturalism dismisses the notion that epistemology is a basis for the empirical sciences. In particular, it rejects the demand for a general justification of induction. Making inductive generalisations is a basic habit among humans. There is no such thing as a logic of inductive inference. The role of induction in science is heuristic; it is our way of inventing new theoretical predicates and developing theories. We discover new laws by applying inductive thinking; but this is not any kind of inference which can be evaluated as more or less rational.

Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

2017 was an excellent year in terms of new drugs (chemical entities and biologics) approved by the FDA, with a total of forty-six. In turn, one of the highlights was the number of peptides (six) included in this list. Here, the six peptides are analysed in terms of chemical structure, synthetic strategy used for their production, source, biological target, and mode of action.

Sea anemones are a remarkable source of active principles due to a decentralized venom system. Blood vessel formation or angiogenesis is a very promising target against cancer, but the few available anti-angiogenic compounds have a limited efficacy. In this study, a protein fraction was purified from tentacles of Anemonia viridis able to limit endothelial cells proliferation and vessel network formation or angiogenesis at low concentration (14 nM). The sequences in this protein fraction were determined with Edman degradation and Mass Spectrometry In Source Decay and revealed homologies with BDS sea anemones. The presence of a two turn alpha helix observed with Circular Dichroism and a trypsin activity inhibition suggested that the active principle could be a Kunitz-type inhibitor, which may interact with an integrin due to a RGD motif well exposed to the solvent as revealed by Molecular Modeling. This active principle could improve antiangiogenic therapy from existing antiangiogenic compounds binding on the VEGF.

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Arylamine N-acetyltransferases (NATs) are cytosolic enzymes, highly polymorphic, present in both eukaryotes and prokaryotes. These enzymes play an important role in the detoxification and activation of xenobiotics as well as in the synthesis of endogenous compounds. Specific NATs have been pointed out in the literature as possible therapeutic targets. In particular, the human NAT1, for the treatment of certain cancers, and the NAT from M. tuberculosis (TBNAT), for the treatment of tuberculosis. This paper describes an in silico approach to prospect and select potentially inhibitors of NAT1 and TBNAT from the Traditional Chinese Medicine (TCM) using free available tools. A library with ligands from TCM was previously screened in order to select only compounds with optimal pharmacological properties. The affinity of the selected ligands with respect to NAT enzymes was then evaluated by virtual screening (VS). Subsequently, the complexes with the best ligands were submitted to molecular dynamics (MD) simulations aiming to obtain better quality information on affinity and selectivity. The results for one specific ligand, ZINC14690579, indicated its potential for affinity and selectivity. ZINC14690579 structure may represent the discovery of a new scaffold for future development of NAT inhibitors.

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

This paper identifies a criterion for choosing the largest set of rejected hypotheses in high-dimensional data analysis where Multiple Hypothesis testing is used in exploratory research to identify significant associations among many variables. The method neither requires predetermined thresholds for level of significance, nor uses presumed thresholds for false discovery rate. The upper limit for number of rejected hypotheses is determined by finding maximum difference between expected true hypotheses and false hypotheses among all possible sets of rejected hypotheses. Methods of choosing a reasonable number of rejected hypotheses and application to non-parametric analysis of ordinal survey data are presented.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.