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Heterogeneous architecture is an underlining feature of 5G, however deployment and management of HetNets in 5G scenarios is yet to be explored. Given the need to satisfy overwhelming capacity demands in 5G, mm-wave spectrum (3-300 GHz) is expected to offer a very compelling long term solution by providing additional spectrum to 5G networks. Hence, the challenge is the integration of mm-wave in heterogeneous and dense networks as well as the backward compatibility and integration with legacy 4G/3G networks. Furthermore, Cloud radio access networks (C-RAN) contribution to 5G is considered as a cost effective and energy efficient solution for dense 5G deployment. From an energy point of view, cost and energy consumption are major considerations for 5G. C-RAN and energy efficiency techniques could help in performance improvements. Although HetNets were introduced in 4G networks, their complexity has increased in 5G networks. In this paper, we will try to build a clear image of HetNets in 5G cellular networks. We consider different technologies with a special focus on mm-wave networks given its important role in 5G networks. We then address the available standards in HetNets that allow interworking and multihoming between different radio access technologies. Afterwards, we consider the virtualization of 5G HetNets and its benefits. Different resource allocation strategies in the literature are also presented for single-resource as well as for multi-resources. Finally, we give an overview of existing works addressing energy efficiency strategies in 5G networks.
The fast growth in the population density in urban areas demands more facilities and resources. To meet the needs of city development, the use of Internet of Things (IoT) devices and the smart systems is the very quick and valuable source. However, thousands of IoT devices are interconnecting and communicating with each other over the Internet results in generating a huge amount of data, termed as Big Data. To integrate IoT services and processing Big Data in an efficient way aimed at smart city is a challenging task. Therefore, in this paper, we proposed a system for smart city development based on IoT using Big Data Analytics. We use sensors deployment including smart home sensors, vehicular networking, weather and water sensors, smart parking sensor, and surveillance objects, etc. initially a four-tier architecture is proposed, which includes 1) Bottom Tier: which is responsible for IoT sources, data generations, and collections 2) Intermediate Tier-1: That is responsible for all type of communication between sensors, relays, base stations, the internet, etc. 3) Intermediate Tier 2: it is responsible for data management and processing using Hadoop framework, and 4) Top tier: is responsible for application and usage of the data analysis and results generated. The collected data from all smart system is processed at real-time to achieve smart cities using Hadoop with Spark, VoltDB, Storm or S4. We use existing datasets by various researchers including smart homes, smart parking weather, pollution, and vehicle for analysis and testing. All the datasets are replayed to test the real-time efficiency of the system. Finally, we evaluated the system by efficiency in term of throughput and processing time.
Background: The sensory impairment is a core diagnostic feature of autism spectrum disorder. However, the underlying mechanisms of symptoms across spectrum remain unknown. Deficits in eye contact are a hallmark of autism diagnostic , and figure most prominently determined by in clinical instruments knower .
The altered N-Acetyl-aspartyl glutamate (NAAG) levels found in anterior (ACC), and Posterior (PCC) cingulated cortices by 1H-MRS in individuals with ASD suggested the neuronal damage. In this sense and following our research line linked to the neuropeptide NAAG as a key mechanism underlying symptoms of ASD, arise the hypothesis of NAA-NAAG metabolism imbalance and their relationship with impairments of the imagination in autism.
Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this paper, I propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for calculating the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses if not the students have low activity should taking high attendance. This system will more efficient for monitoring students for replacing absent to activity.
The primary goal of scientific publishing is to disseminate results and, in general, advance science. However, the enormous pressure of publishing in different settings, particularly academia, influences scientific publishing to the point of becoming a survival mechanism under the 'publish or perish" paradigm. Such a paradigm distorts the main goals of scientific publishing. It might significantly impact the motivation of writing manuscripts, becoming a burden for students and investigators that might see publishing as a challenging requirement to "keep alive," but not so much as an opportunity to make contributions to advance science. Moreover, the need to survive through publishing is one of the main drivers of scientific misconduct and ethics in publishing when the primary goal is no scientific dissemination but to put anything in print. In this manuscript, we comment on the primary goals of scientific publishing along with collateral and positive benefits in academic, professional development benefits and advantages of scientific publishing well beyond a requirement not to perish. We believe that going after the right reasons and the right motivation, writing papers and publishing can be transformed into something not necessarily easy but enjoyable if seen as an opportunity to make fundamental contributions to science and transcend.
Over the last years, many organizations have been working on infrastructure to facilitate sharing and reuse of research data. This means that researchers now have ways of making their data available, but not necessarily incentives to do so. Several Research Data Alliance (RDA) working groups have been working on ways to start measuring activities around research data to provide input for new Data Level Metrics (DLMs). These DLMs are a critical step towards providing researchers with credit for their work. In this paper, I describe the outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group. The Scholix working group developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations. The Data Usage Metrics group works on a standard for the measurement and display of Data Usage Metrics. Here I explain how publishers and data repositories can contribute to and benefit from these initiatives. Together, these contributions feed into several hubs that enable data repositories to start displaying DLMs. Once these DLMs are available, researchers are in a better position to make their data count and be rewarded for their work.
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
Despite the contributions of CADD in different stages of the drug discovery pipelines and technological advances, there are challenges that need to be addressed. Table 1 outlines the grand challenges that face drug discovery using in silico methods and AI and are further commented on in this manuscript. The list of topics is not exhaustive; the selected challenges are based on the author’s opinion, and it is intended to be a reference for a continued update. Here, the challenges are organized into six sections. The first two are related to the chemical and biological relevant chemical spaces, respectively; that is, what spaces are being explored? Another section covers methodological challenges: how is being conducted the search for new and better drugs at the intersection of the relevant chemical and biological spaces? The next three sections present hurdles associated with communication and Human interaction in research teams, scientific dissemination, data sharing, and education, respectively. The last section contains the Conclusions.
The Covid-19 pandemic is having a huge global impact, especially when we talk about Internet of Things (IoT) and market analysts to analyze the impact of the pandemic on smart cities and also to understand what are the new responses, challenges, opportunities which will arise in a post-pandemic scenario. Technology is a key which has turn a city into smart city which is well connected, sustainable and resilient, where information is not just available but also findable. Therefore, the emerging technologies have created new interest in smart cities' solutions. It is one of the most promising, prominent, and challenging applications of the Internet of Things (IoT). The main goal of is to optimize city functions and promote economic growth and also improving the quality of life for citizens especially by using smart technologies and data analysis that leads to smart outcomes. The progress and advancement of the smart cities' are the results of the successful utilization of emerging technologies. With the advancement of COVID-19 pandemic, people realized how important digital connectivity. We, as a society should learn how to be ready for a crisis and the best way to be ready for the crisis is really influenced all this innovation in the digital world. This paper thoroughly studies the value of how emerging technology is used and what are the challenges and responses of technology used in smart cities for the planet earth during and post COVID pandemic.
A technology enhances a man's life in every aspect whether it is healthcare, transport, communication, education etc. During this Covid-19 pandemic, the growths of technology usage have increased enormously. There are various issues and challenges which we face during the use of any technology. Security is the most important aspect, when we are using in any digital platform during Covid-19. When we think about Cyber Security, the first thing that comes to our mind is "Cyber Crime" which is increasing immensely day by day. Cyber Security refers to the practice of ensuring the integrity, confidentiality and availability (ICA) of information. Basically Cyber security is the state or process of protecting and recovering networks, devices and programs from any type of Cyber Attack. It is comprised of an evolving set of tools, risk management approaches, technologies, training, and best practices designed to protect networks, devices, programs, and data from attacks or unauthorized access. Cyber-attacks in India have risen up to such an extent that our country ranks fourth out of the tenth targeted countries in the world. In this Covid-19 pandemic Cyber-attacks are an evolving danger to many organizations, employees and consumers in different sectors. They may be designed to access or destroy sensitive data, change any kind of data or extort money. According to a Research, more than 50% of onliners are victim of some of cybercrime every year, which includes computer viruses, malware, credit card fraud, online scams, phishing, and identity theft and so on. These crimes will lead the country to lose millions of rupees or dollars, also time and expenses to put back the things in right directions. This paper mainly focuses on the different aspects of cyber security and the challenges faced in the implementation and by using latest technologies during Covid-19. The paper also focuses on the India's legal framework for Cyber Security.
This exploratory research of data lakes in big data times is a prominent topic for both academia and industry. One of the main motivations behind is that companies need to cope with more data than ever before, and the problems of how to analyze even how to store data are becoming more and more challenging in many industries. The occurrence of the concept of a data lake to meet such big data problems is enlightening and will most likely be considered in any relevant big data strategy. This idea is still on the way to prove itself out and inevitably it gives rise to much attention as well as much criticism. Luckily, more and more positive voices towards data lakes are emerging and give highly appreciation to the concept and even propose some workable and innovative suggestions to make improvement to the practical implementation. This study introduced basic background information of data lake implementation and can give valuable suggestions and insights to practitioners. After presenting and summarizing most of the popular implementation of data lakes from data professionals, three different approaches were introduced. All of these approaches have both advantages and disadvantages, and companies need to consider their own business needs and requirements to make a wise choice.
Health psychology is a branch of psychology which studies the psychology of the patient's behaviour, the role of psychic factors in the origin and development of the diseases, the psychology of relationships between doctor, staff and patient, as well as the use of a psychological approach in their assessment and treatment in medical practice. Nowadays, researchers in Health psychology are interested in applied psychology issues conducting research on how patients are treated in hospital, how social factors influence the behaviour of an individual patient, how to motivate health workers to perform at a higher level etc. These research questions cannot be answered by lab experiments as one has to go to the field and the real life situation like the classroom etc., to find answers to the research issues mentioned above. Thus, the quasi-experimental research came into existence. Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable. It does not eliminate the problem of confounding variables, however, because it does not involve random assignment to conditions. For these reasons, quasi-experimental research is generally higher in internal validity than correlational studies but lower than true experiments. Quasi-experimental research design can be more easily implemented in natural settings and one can make direct assessment of subjects, find out the effects of a specific treatment introduced by the researcher, and while doing so the researcher can also minimise the influence of extraneous variables. In this paper, the quasi-experimental design is discussed for health psychology.
Now a day’s most of the activities and financial transactions uses internet, since internet is accessible from anywhere, perpetrator takes advantage of this and commit a crime. Cybercrime is a term used to broadly describe criminal activity in which computers or computer networks are a tool, a target, or a place of criminal activity and include everything from electronic cracking to denial of service attacks. It is also used to include traditional crimes in which computers or networks are used to enable the illicit activity. Cyber criminals take full advantage of the anonymity, secrecy, and interconnections provided by the Internet. In this paper we have tried to provide information about Cyber crime, its nature, Perpetrators, Classification of cyber crime, Reasons for its emergence, In next section of this paper we have given an information about cyber law, IT legislation in India. Further in next section we have discuses about Cyber crime scenario in India. Finally Last two sections of this paper discuss about some cyber crime cases in India and some cyber crimes and punishments related with those crime.
Natural products continue to be a significant source of active compounds [...].
- Ingeborg M Kooter
- Marit Ilves
- Mariska Gröllers-Mulderij
- Harri Alenius
More than 5% of any population suffers from asthma, and there are indications that these individuals are more sensitive to nanoparticle aerosols than the healthy population. Due to a paucity of data on the molecular-level details and mechanisms involved, we leveraged a realistic air-liquid interface model of inhalation exposure, to investigate global transcriptomic responses in reconstituted three-dimensional airway epithelia of healthy and asthmatic subjects exposed to pristine (nCuO) and carboxylated (nCuOCOOH) copper oxide nanoparticles. A dose-dependent increase in cytotoxicity (highest in asthmatic donor cells) and pro-inflammatory signalling within 24 hours, confirmed the reliability and sensitivity of the system to detect acute inhalation toxicity. Gene expression changes between nanoparticle-exposed versus air-exposed cells were investigated. Hierarchical clustering based on the expression profiles of all differentially expressed genes (DEGs), cell-death-associated DEGs (567 genes) or a subset of 48 highly overlapping DEGs, categorized all samples according to ‘exposure severity’, wherein nanoparticle surface chemistry and asthma are incorporated into the dose-response axis. For example, asthmatics exposed to low and medium dose nCuO clustered with healthy donor cells exposed to medium and high dose nCuO, respectively. Of note, a set of genes with high relevance to mucociliary clearance, were observed to distinctly differentiate asthmatic and healthy donor cells. These genes also responded differently to nCuO and nCuOCOOH nanoparticles. Additionally, because response to transition metal nanoparticle was a highly enriched Gene Ontology term (FDR 8E-13) from the subset of 48 highly overlapping DEGs, these genes may represent biomarkers to a potentially large variety of metal/metal oxide nanoparticles.
Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study.
De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencod-ers. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development .
Dear colleagues worldwide, we are glad to invite you to MOL2NET-07, International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2021. MOL2NET is an international conference formed by an association of several inter-university tansatlantic workshops or sessions. These workshops are chaired by one North America/Europe based host chairperson and one or various co-host chairpersons from other centers worldwide. The conference runs as year-round conference series with two main stages. These stages are: (1) Call for papers and (2) Post-publication reviews. In the first stage we open independent workshops in person and/or online, receive papers, do pre-publication review of papers, and perform online publication. Please, read the [MOL2NET Publication Schedule]. Consequently, submissions are open to be published asap upon acceptance in Sciforum platform almost all year at no cost for authors (year-round and free of cost publication). Depending on the schedule we also run a second stage called [MOL2NET Post-Publication Schedule]. On the Post-publication stage we close submission of papers and invite all members of committee, authors, and validated social network followers, to become post-publication reviewers. In this moment members of all workshops can interact together on brain-storming sessions making online comments each other. Headquarters and Supporters MOL2NET runs both online and/or in person workshops in host universities worldwide and at the SciForum platform maintained by the editorial MDPI, Basel, Switzerland. The idea of this multidisciplinary conference emerged from the melting pot formed as the result of multiple collaborations of professors from many centers worldwide. International Partners. Internationally, professors and researchers WWWorld Around are the main partners, co-host chairs, and/or committee members. From North America sides's researchers from Stanford Center for Biomedical Informatics Research (BMIR), Stanford University (STANFORD), USA; Center for the Study of Biological Complexity (CBDS) of Virginia Commonwealth University (VCU), USA; Miami Dade College (MDC), USA; North Dakota State University (NDSU), USA; Jackson State University (JSU), USA, and Carleton University (CARLETON), Canada, stand out. From Europes's side researchers and/or professors from European Bioinformatics Institute (EMBL-EBI) Cambridge, United Kingdom, Universität Rostock Institut für Chemie (UROSTOCK), Germany, Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay (UPS), and Conservatoire National des Arts et Métiers (CNAM), Paris, France, are the main co-host of our workshops. In addition, other institutions are co-founders and/or supporters of this conference worlwide, plz see full list on committee. Headquarters & Local Partners. The Scientific Headquarters (HQs) of this conference series are in the Faculty of Science and Technology, University of Basque Country (UPV/EHU), Basque Country, Spain. This center is also the host of some of the workshops of the conference. More specifically, the founders and strongest supporters of the conference are professors endowed by IKERBASQUE, Basque Foundation for Sciences (IKERBASQUE), professors from the Dept. of Org. Chem., Dept. of Org. & Inorg. Chem. In addition, professors from or afiliated to Basque Centre for Biophysics (BIOFISIKA) of the University of Basque Country (UPV/EHU), and Center for Cooperative Research in Biosciences (CICBIOGUNE), with ties to previous departments are also supporters of this conference. Last, act also as local co-hosting partners professors from the Department of Computer Sciences of the University of Coruña (UDC), Galicia, Spain. Please, see full committees lists [MOL2NET HONOR & SCI COMMITTEE] and [MOL2NET-IKERBASQUE Staff]. In this sense, all these and other local and international centers acts as co-hosting partners for some of the workshop sessions of this conference. MOL2NET Lemma and Topics of Interest. The acronym MOL2NET summarizes the running title of this conference: FROM MOLECULES TO NETWORKS. This lemma is inspired by the possibility of spreading the knowledge about multiple topics by promoting multidisciplinary collaborations in science with conferences including topics ranging from quantum world to large social networks. In this sense, the spirit of this conference is in consonance with the lemma EMAN TA ZABAL ZAZU (Give and extend knowledge). This anthem from the 19th century is written in Basque language (Euskera) and is used now a days as the official motto of the University of Basque Country (UPV/EHU). As results the topics of interest are wide, as is typical on multi-disciplinary sciences, and range from the study of small molecules phenomena to the behavior of large complex networks in nature and society. This include but is not limited to, Chemistry (All areas), Mathematics (Applied), Physics (Applied), Materials Science, Nanotechnology, Biology and Life Sciences (All areas), Medicine, Biomedical Engineering, Education, along with Computer Sciences, Data Analysis, Statistics, Artificial Intelligence, Deep Learning, Bioinformatics, Systems Biology, and Complex Networks Sciences. We suggest you to download the template to write your communications for conference workshops [MOL2NET 2021 Template.doc]. Whoever, some other workshops of the series have their custom templates. Please, read carefully the [Instructions to authors] about publication model, copyright, authors responsibilities, etc. MOL2NET WWWorkshops (Sessions). You can participate both online or in person (face-to-face) in some of the workshops we organize in different universities worldwide (see sessions in the conference menu). Publication of all communications of the workshops will be online via the platform SciForum. We welcome proposals for organization of workshops in different universities. Please, do not hesitate to contact conference chairperson and/or scientific committee presidents. See also a list of workshops/sessions running this year. 01. CHEMBIOMOL-07: Chem. Biol. & Med. Chem. Workshop, Bilbao-Rostock, Germany, Galveston, USA, 2021 02. NANOBIOMAT-07: Nanotech. & Mat. Sci. Workshop, Bilbao, Spain-Jackson & Fargo, USA, 2021. 03. USEDAT-07: USA-Europe Data Analysis Training Program Workshop, Cambridge, UK-Bilbao, Spain-Miami, USA, 2021 04. CHEMINFONC-03: North-Carolina Chemoinformatics Workshop, Chape Hill-Durham, USA, 2021 07. NIXMSM-07: North-Ibero-American Exp., Model., and Simul. Methods Workshop, Valencia, Bilbao, Spain-Miami, USA, 2021 06. BIOMEDIT-02; ITCs & Biomol. Biomed. Eng. Workshop, New Orleans, USA, 2021 07. MODECO-06: Molec. Diversity & Ecosystems, Puyo, Ecuador-Porto, Portugal-Paris, France, EPA, USA, 2021 08. IWMEDIC-08: North-Ibero-Am. Int. Work. Chem., Bio., and Med. Info., Coruña, Spain-Carleton, Canada-Standford, USA, 2021 09. TECHLAWSCI-05: PANELFIT & NKL Tech. Law. & Sci. Challenges, Bilbao, Spain, Halden, Norway, Baltimore, USA, 2021 10. CHEMXEDIT-05, Chem. Exp., Edu., & Info. Tech., Bilbao, Spain-Paris, France-Texas, USA, 2021 MOL2NET-MDPI JCR Journals IssuesIn parallel, the members of committees and/or authors are encouraged to edit special issues for different journals of the editorial MDPI (http://www.mdpi.com/). The special issue is now in call for papers, submissions are welcome in a posteriori, in parallel, or totally independently from the conference. Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. In order to send a proposal of associated workshop and/or special issue contact the chairperson of the conference Prof. González-Díaz H. IIKERBASQUE Prof., University of the Basque Country (UPV/EHU), Biscay, Basque Country, Spain. Email: email@example.com. Please, check here [MOL2NET-MDPI JCR Journal Issues] details about the special issues, titles, editors, and a list of past and present special issues and selected papers associated to our conference. Thank You For Your Support!!! MOL2NET Steering Committee Contact Prof. González-Díaz H., IKERBASQUE Professor, https://orcid.org/0000-0002-9392-2797, Email: firstname.lastname@example.org (1) Dept. of Organic and Inorganic Chemistry and Basque Center for Biophysics, University of the Basque Country UPV/EHU , 48940, Leioa, Biscay, Spain. (2) IKERBASQUE, Basque Foundation for Science , 48011, Bilbao, Biscay, Spain.
Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemo-informatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.
The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term ‘epi-informatics’, the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure–epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery
Motivation Machine-learning scoring functions have been found to outperform standard scoring functions for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. Results Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein-ligand complexes and build machine-learning scoring functions with improved predictions of binding affinity. ECIF are a set of protein−ligand atom-type pair counts that take into account each atom’s connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein-ligand affinities (pKd / pKi). The models were evaluated in terms of “scoring power” on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. Availability Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF.
Inhibitors of DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug discovery. They are also chemical tools to understand the biochemistry of epigenetic processes. Herein, we report five distinct inhibitors of DNMT1 characterized in enzymatic inhibition assays that did not show activity with DNMT3B. It was concluded that the dietary component theaflavin is an inhibitor of DNMT1. Two additional novel inhibitors of DNMT1 are the approved drugs glyburide and panobinostat. The DNMT1 enzymatic inhibitory activity of panobinostat, a known pan inhibitor of histone deacetylases, agrees with experimental reports of its ability to reduce DNMT1 activity in liver cancer cell lines. Molecular docking of the active compounds with DNMT1, and re-scoring with the recently developed extended connectivity interaction features approach, led to an excellent agreement between the experimental IC50 values and docking scores.
Ensuring the safe and responsible use of nanotechnologies and nanoscale materials is imperative to maximize consumer confidence and drive commercialization of nano-enabled products that underpin innovation and advances in every industrial sector [...]
Toxicogenomics approaches are increasingly used to gain mechanistic insight into the toxicity of engineered nanomaterials (ENMs). These emerging technologies have been shown to aid the translation of in vitro experimentation into relevant information on real-life exposures. Furthermore, integrating multiple layers of molecular alteration can provide a broader understanding of the toxicological insult. While there is growing evidence of the immunotoxic effects of several ENMs, the mechanisms are less characterized, and the dynamics of the molecular adaptation of the immune cells are still largely unknown. Here, we hypothesized that a multi-omics investigation of dynamic dose-dependent (DDD) molecular alterations could be used to retrieve relevant information concerning possible long-term consequences of the exposure. To this end, we applied this approach on a model of human macrophages to investigate the effects of rigid multi-walled carbon nanotubes (rCNTs). THP-1 macrophages were exposed to increasing concentrations of rCNTs and the genome-wide transcription and gene promoter methylation were assessed at three consecutive time points. The results suggest dynamic molecular adaptation with a rapid response in the gene expression and contribution of DNA methylation in the long-term adaptation. Moreover, our analytical approach is able to highlight patterns of molecular alteration in vitro that are relevant for the pathogenesis of pulmonary fibrosis, a known long-term effect of rCNTs exposure in vivo.
Despite considerable efforts, the properties that drive the cytotoxicity of engineered nanomaterials (ENMs) remain poorly understood. Here, the authors inverstigate a panel of 31 ENMs with different core chemistries and a variety of surface modifications using conventional in vitro assays coupled with omics-based approaches. Cytotoxicity screening and multiplex-based cytokine profiling reveals a good concordance between primary human monocyte-derived macrophages and the human monocyte-like cell line THP-1. Proteomics analysis following a low-dose exposure of cells suggests a nonspecific stress response to ENMs, while microarray-based profiling reveals significant changes in gene expression as a function of both surface modification and core chemistry. Pathway analysis highlights that the ENMs with cationic surfaces that are shown to elicit cytotoxicity downregulated DNA replication and cell cycle responses, while inflammatory responses are upregulated. These findings are validated using cell-based assays. Notably, certain small, PEGylated ENMs are found to be noncytotoxic yet they induce transcriptional responses reminiscent of viruses. In sum, using a multiparametric approach, it is shown that surface chemistry is a key determinant of cellular responses to ENMs. The data also reveal that cytotoxicity, determined by conventional in vitro assays, does not necessarily correlate with transcriptional effects of ENMs.
G protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. This data is also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., difficulting the analysis of these data bases placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to carry out new predictive and experimental studies of selective 5-HT2AR agonist, antagonist or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this dataset. We analyzed 343,738 unique compounds with 812,072 endpoints (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56,202/58,653 positive outcomes (Sensitivity = 95.8%) and 470,230/550,401 control cases (Specificity = 85.4%) in training series. The model also predicted correctly 18,732/19,549 (95.8%) of positive outcomes and 156,739/183,469 (85.4%) of cases in external validation series. In order to illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, TCB-2, DOI, DOB, altanserin, pimavanserin and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 out of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were carried out experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.
Long-term effects of Covid-19 disease are still poorly understood. However, similarities between the responses to SARS-CoV-2 and certain nanomaterials suggest fibrotic pulmonary disease as a concern for public health in the next future. Cross-talk between nanotoxicology and other relevant disciplines can help us to deploy more effective Covid-19 therapies and management strategies.
The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we apply data integration methods to develop a Unified Knowledge Space (UKS) and use it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially interesting drugs targeting proteins involved in multiple steps of the host response to the virus.
Welcome From Chairs Dear colleagues worldwide, we are glad to invite you to MOL2NET-05, International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2019. MOL2NET is a year-round conference series with multiple associated workshops worldwide running and open to submissions almost all the year, please read [Workshops Schedule]. The topics of interest include, but are not limited to, Chemistry (All areas). We suggest you to download the [MOL2NET Template.doc] of [USEDAT Template.doc] to write your communications for conference or training school wokshops. Whoever, some other workshops of the series have their custom templates. Please, read carefully the [Instructions to authors] about publication model, copyright, authors responsibilities, etc. Headquarters and Supporters MOL2NET runs both online and/or in person workshops in host universities worldwide and at the SciForum platform maintained by the editorial MDPI, Basel, Switzerland. The Scientific Headquarters (HQs) of this conference series are in the Faculty of Science and Technology, University of Basque Country (UPV/EHU), Biscay. However, the idea of this multidisciplinary conference emerged from the melting pot formed as the result of multiple collaborations of professors from many centers worldwide. Locally, the founders and strongest supporters of the conference are professors endowed by IKERBASQUE, Basque Foundation for Sciences, professors from the two departments
Background Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural properties of compounds and phenotypic effects of an exposure. Results Here, we propose a computational strategy that integrates molecular descriptors and MOA information to better explain the mechanisms underlying biological endpoints of interest. By applying our methodology, we obtained a statistically robust and validated model to predict the binding affinity to human serum albumin. Our model is also able to provide new venues for the interpretation of the chemical-biological interactions. Conclusion Our observations suggest that integrated quantitative models of structural and MOA-activity relationships are promising complementary tools in the arsenal of strategies aiming at developing new safe- and useful-by-design compounds.
Background: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high. Results: Wedevelopedanapproachthatexploitstheintrinsichierarchicalstructureofseveralfunctionalannotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne. Conclusion: FunMappOneisaR-shinygraphicaltoolthattakesininputmultiplelistsofhumanormousegenes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results.
Background Omics technologies have been widely applied in toxicology studies to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow the study of the molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis cannot identify dynamic (time-dependent) events of dose responsiveness. Results We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log fold-change, TinderMIX fits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify whether the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure. Conclusions To showcase the TinderMIX method, we analysed 2 drugs from the Open TG-GATEs dataset, namely, cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action of each drug and compared them. Our analysis highlights that different time- and dose-integrated point of departure recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent mechanism of action.
Combining complex networks analysis methods with machine learning (ML) algorithms have become a very useful strategy for the study of complex systems in applied sciences. Noteworthy, the structure and function of such systems can be studied and represented through the above-mentioned approaches, which range from small chemical compounds, proteins, metabolic pathways, and other molecular systems, to neuronal synapsis in the brain’s cortex, ecosystems, the internet, markets, social networks, program’s development in education, social learning, etc. On the other hand, ML algorithms are useful to study large datasets with characteristic features of complex systems. In this context, we decided to launch one special issue focused on the benefits of using ML and complex network analysis (in combination or separately) to study complex systems in applied sciences. The topic of the issue is: Complex Networks and Machine Learning in Applied Sciences. Contributions to this special issue are highlighted below. The present issue is also linked to conference series, MOL2NET International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI AG, SciForum, Basel, Switzerland. At the same time, the special issue and the conference are hosts for the works published by students/tutors of the USEDAT: USA–Europe Data Analysis Training Worldwide Program.
Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (n ij ) with a considerable combination of assay conditions (c j ). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of c j . They are focused on >500 different biological activity parameters (c0), >340 different targets (c1), >6200 types of cell (c2), >120 organisms of assay (c3), and >60 assay strains (c4). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), näive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.
Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs represents a difficult task due to the large number of edges and the complex connectivity patterns. Fortunately, we can use another special type of networks to achieve this goal—Artificial Neural Networks (ANNs). Thus, ANNs could use node descriptors such as Shannon Entropies (Sh) to predict node connectivity for large datasets including complex systems such as BCN. However, the training of a high number of ANNs for BCNs is a time-consuming task. In this work, we propose the use of a method to automatically determine which ANN topology is more efficient for the BCN prediction. Since a network (ANN) is used to predict the connectivity in another network (BCN), this method was entitled Net-Net AutoML. The algorithm uses Sh descriptors for pairs of nodes in BCNs and for ANN predictors of BCNs. Therefore, it is able to predict the efficiency of new ANN topologies to predict BCNs. The current study used a set of 500,470 examples from 10 different ANNs to predict node connectivity in BCNs and 20 features. After testing five Machine Learning classifiers, the best classification model to predict the ability of an ANN to evaluate node interactions in BCNs was provided by Random Forest (mean test AUROC of 0.9991 ± 0.0001, 10-fold cross-validation). Net-Net AutoML algorithms based on entropy descriptors may become a useful tool in the design of automatic expert systems to select ANN topologies for complex biological systems. The scripts and dataset for this project are available in an open GitHub repository.
Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.
The aim of drug delivery is primarily focused on the optimum bioavailability at the targeted site of action over a defined period of time. Nanoparticle plays significant role in the drug delivery as it can be designed as target based, with improved stability, increased drug stability as well as can offer constant rate in the drug delivery. Nanoparticles can be created via carbamate, thiourea and amide linkage as well as via electrostatic interaction, hydrophobic entrapment and chemisorptions. Literature also supported the profound antibacterial and antiviral activity of silver nanoparticles. On the basis of methodology adopted for the preparation, nanoparticles, nanospheres or nanocapsules can be prepared. For the nanoparticles, methods like dispersion of preformed polymers, polymerization of monomers and ionic gelation/ coecervation of hydrophilic polymer technology were usually adopted. References  Nagal, A.; Singla, R.K. Nanoparticles in different delivery systems: a brief review. Indo Global J. Pharm. Sci., 2013; 3(2): 96-106.  Mitra, A.; Dey, B. Chitosan microspheres in novel drug delivery systems. Indian J. Abstract Abstract The aim of drug delivery is primarily focused on the optimum bioavailability at the targeted site of action over a defined period of time. Nanoparticle plays significant role in the drug delivery as it can be designed as target based, with improved stability, increased drug stability as well as can offer constant rate in the drug delivery. Nanoparticles can be created via carbamate, thiourea and amide linkage as well as via electrostatic interaction, hydrophobic entrapment and chemisorptions. Literature also supported the profound antibacterial and antiviral activity of silver nanoparticles. On the basis of methodology adopted for the preparation, nanoparticles, nanospheres or nanocapsules can be prepared. For the nanoparticles, methods like dispersion of preformed polymers, polymerization of monomers and ionic gelation/ coecervation of hydrophilic polymer technology were usually adopted. References  Nagal, A.; Singla, R.K. Nanoparticles in different delivery systems: a brief review. Indo Global J. Pharm. Sci., 2013; 3(2): 96-106.  Mitra, A.; Dey, B. Chitosan microspheres in novel drug delivery systems. Indian J. Abstract Abstract The aim of drug delivery is primarily focused on the optimum bioavailability at the targeted site of action over a defined period of time. Nanoparticle plays significant role in the drug delivery as it can be designed as target based, with improved stability, increased drug stability as well as can offer constant rate in the drug delivery. Nanoparticles can be created via carbamate, thiourea and amide linkage as well as via electrostatic interaction, hydrophobic entrapment and chemisorptions. Literature also supported the profound antibacterial and antiviral activity of silver nanoparticles. On the basis of methodology adopted for the preparation, nanoparticles, nanospheres or nanocapsules can be prepared. For the nanoparticles, methods like dispersion of preformed polymers, polymerization of monomers and ionic gelation/ coecervation of hydrophilic polymer technology were usually adopted. References  Nagal, A.; Singla, R.K. Nanoparticles in different delivery systems: a brief review. Indo Global J. Pharm. Sci., 2013; 3(2): 96-106.  Mitra, A.; Dey, B. Chitosan microspheres in novel drug delivery systems. Indian J. Pharm. Sci., 2011; 73(4): 355-366.
In the current study, we have isolated and characterized a novel molecule from the hard shell of Cocos nucifera Linn. and evaluated it for its inhibitory potential against hyaluronidase enzyme. Alcoholic extract of the hard shell was subjected to various purification procedures viz. column chromatography, solvent based extraction and TLC to yield a phytomolecule. Spectral characterization indicated that it is a novel keto fatty acid. To the best of our knowledge, this is the first keto fatty acid from the coconut plant even. Results of hyaluronidase inhibition assay indicated that it has moderate hyaluoronidase inhibitory activity.
Carbonic anhydrase is an omnipresent zinc-containing metalloenzyme which is essential for a lot of physiological activities because of its property to convert CO2 to HCO3- reversibly. It is one of the fastest enzymes known for hydrating 106 molecules of CO2 per second. The rate of reaction of this enzyme is typically limited by the rate of diffusion of its substrates. There are six types of carbonic anhydrases- alpha, beta, gamma, delta, epsilon and zeta, named by greek letters. Carbonic anhydrase is often arranged in clusters along membranes or localised in extracellular spaces, which may contribute to the ability of carbonic anhydrase to facilitate the intracellular diffusion of carbon dioxide and protons (H+). By increasing the movement of protons, carbonic anhydrase can dissipate intracellular pH gradients, thereby helping the cell to maintain a uniform cellular pH. Overall, the uses of Carbonic anhydrase are multifold which will be later discussed in this paper.
Nonhealing wounds represent a significant cause of morbidity and mortality for a large portion of the population. One of the underlying mechanisms responsible for the failure of chronic wounds to heal is an out-of-control inflammatory response that is self-sustaining. Underappreciation of the inherent complexity of the healing wound has led to the failure of monotherapies, with no significant reduction in wound healing times. A model of the inflammatory profile of a nonhealing wound is one in which the equilibrium between synthesis and degradation has been shifted toward degradation. This review summarizes the current information regarding acute wound healing responses as contrasted to the delayed response characteristic of chronic wounds. In addition, some initial complexity theoretical models are proposed to define and explain the underlying pathophysiology.