Relevant bibliographies by topics / In silico methodologies

Academic literature on the topic 'In silico methodologies'

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Published: 14 March 2023

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Journal articles on the topic "In silico methodologies"

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Hasan, Doaa Mohamed, Ahmed Sharaf Eldin, Ayman Elsayed Khedr, and Hanan Fahmy. "In-Silico Methodologies for Cancer Multidrug Optimization." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (July 6, 2018): 7186–205. http://dx.doi.org/10.24297/ijct.v17i2.7168.

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear, which makes it not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer, and discuss the limitations and challenges of these methods.
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Scotti, Luciana, Jahan Ghasemi, and Marcus T. Scotti. "Editorial: In Silico Methodologies Applied to Drug Discovery." Combinatorial Chemistry & High Throughput Screening 21, no. 3 (April 23, 2018): 150–51. http://dx.doi.org/10.2174/138620732103180423125817.

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Scotti, Luciana, and Marcus T. Scotti. "In Silico Methodologies Applied to Anti-infections Drug Discovery." Combinatorial Chemistry & High Throughput Screening 23, no. 6 (October 5, 2020): 456–57. http://dx.doi.org/10.2174/138620732306200612101828.

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Remtulla, Raheem, Sanjoy Kumar Das, and Leonard A. Levin. "Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning." Molecules 26, no. 9 (April 25, 2021): 2505. http://dx.doi.org/10.3390/molecules26092505.

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Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.
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Uysal, Sengul, Abdurrahman Aktumsek, Carene M. N. Picot, Alime Sahan, Adriano Mollica, Gokhan Zengin, and Mohamad Fawzi Mahomoodally. "A comparative in vitro and in silico study of the biological potential and chemical fingerprints of Dorcycinum pentapyllum subsp. haussknechtii using three extraction procedures." New Journal of Chemistry 41, no. 22 (2017): 13952–60. http://dx.doi.org/10.1039/c7nj03497k.

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Moura, Ana S., Amit K. Halder, and M. Natália DS Cordeiro. "From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling." Future Medicinal Chemistry 11, no. 17 (September 2019): 2313–31. http://dx.doi.org/10.4155/fmc-2018-0365.

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Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.
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Ball, Nicholas, Remi Bars, Philip A. Botham, Andreea Cuciureanu, Mark T. D. Cronin, John E. Doe, Tatsiana Dudzina, Timothy W. Gant, Marcel Leist, and Bennard van Ravenzwaay. "A framework for chemical safety assessment incorporating new approach methodologies within REACH." Archives of Toxicology 96, no. 3 (February 1, 2022): 743–66. http://dx.doi.org/10.1007/s00204-021-03215-9.

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AbstractThe long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution.
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Gimeno, Aleix, María Ojeda-Montes, Sarah Tomás-Hernández, Adrià Cereto-Massagué, Raúl Beltrán-Debón, Miquel Mulero, Gerard Pujadas, and Santiago Garcia-Vallvé. "The Light and Dark Sides of Virtual Screening: What Is There to Know?" International Journal of Molecular Sciences 20, no. 6 (March 19, 2019): 1375. http://dx.doi.org/10.3390/ijms20061375.

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Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.
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Kothandan, Gugan, Changdev G. Gadhe, Thirumurthy Madhavan, and Seung J. Cho. "Binding Site Analysis of CCR2 Through In Silico Methodologies: Docking, CoMFA, and CoMSIA." Chemical Biology & Drug Design 78, no. 1 (March 29, 2011): 161–74. http://dx.doi.org/10.1111/j.1747-0285.2011.01095.x.

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Gadhe, Changdev G., Gugan Kothandan, and Seung Joo Cho. "Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach." Archives of Pharmacal Research 36, no. 1 (January 2013): 6–31. http://dx.doi.org/10.1007/s12272-013-0001-1.

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Dissertations / Theses on the topic "In silico methodologies"

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Sala, Argüello Esther. "In silico methodologies for the design of functional foods that can prevent cardiovascular diseases." Doctoral thesis, Universitat Rovira i Virgili, 2011. http://hdl.handle.net/10803/33626.

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La indústria alimentària incorpora als aliments extractes d’origen natural rics en molècules bioactives amb la finalitat de convertir-los en aliments funcionals, és a dir, que tinguin propietats beneficioses per a la salut més enllà del seu valor nutricional. Un dels principals reptes és millorar l'eficiència en la selecció de nous compostos bioactius per accelerar el desenvolupament de nous aliments funcionals. Les tècniques computacionals, com ara el cribratge virtual, poden exercir un paper essencial en la fase inicial del descobriment de noves substàncies bioactives. Durant la tesi s’han desenvolupat i validat diferents protocols de cribatge virtual per a IKK-2 i 11-HSD1 (dianes involucrades en inflamació crònica), els quals han permès trobar compostos i extractes d’origen natural amb propietats anti-inflamatòries. Aquests compostos bioactius poden ser de gran utilitat com a additius en l’alimentació funcional, ja que poden presentar activitat semblant a la dels fàrmacs emprats en el tractament de malalties cardiovasculars.
One of the main challenges in nutrigenomics is improving the efficiency of the selection (which is currently time consuming and expensive) of new bioactive compounds in order to expedite the development of new functional foods. Computational techniques, such as virtual screening, may play an essential role in accelerating the early stages of the discovery of new bioactive substances by efficiently searching for compounds that could activate or inhibit a known target. So, by modulating specific target functions in the body, molecules that act as IKK-2 or 11β-HSD1 inhibitors have beneficial physiological effects that can be of interest for preventing, retarding and/or reversing the metabolic syndrome. Because of their anti-inflammatory properties, natural extracts that contain these molecules have a promising role as ingredients in new functional foods. Therefore, this PhD thesis will focus on the development of virtual screening workflows that predict natural products that can inhibit both targets.
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Chemi, Giulia. "Computer-aided drug discovery methodologies for the identification and optimization of bioactive compounds." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1095491.

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In silico methodologies have become an essential part of the modern drug discovery process. This thesis covers a wide range of computational approaches, and all the techniques employed can be grouped and divided in two big different classes: Ligand-Based methods and Structure-Based methods. Particular attention was payed to the evaluation of drug-like features of the identified molecules during both Structure-Based and Ligand-Based projects, in order to propose hit compounds with satisfactory pharmacokinetic profiles. This thesis work is divided in two main chapters according to the two methods applied to different medicinal chemistry issues.
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Michielan, Lisa. "Advance Methodologies in Linear and Nonlinear Quantitative Structure-Activity Relationships (QSARs): from Drug Design to In Silico Toxicology Applications." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3422242.

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Novel computational strategies are continuously being demanded by the pharmaceutical industry to assist, improve and speed up the drug discovery process. In this scenario chemoinformatics provide reliable mathematical tools to derive quantitative structure-activity relationships (QSARs), able to describe the correlation between molecular descriptors and various experimental profiles of the compounds. In the last years, nonlinear machine learning approaches have demonstrated a noteworthy predictive capability in several QSAR applications, confirming their superiority over the traditional linear methodologies. Particularly the feasibility of the classification approach has been highlighted in solving complex tasks. Moreover, the introduction of the autocorrelation concept in chemistry allows the structural comparison of the molecules by using a vectorial fixed-length representation to serve as effective molecular descriptor. In the present thesis we have deeply investigated the wide applicability and the potentialities of nonlinear QSAR strategies, especially in combination with autocorrelation molecular electrostatic potential descriptors projected on the molecular surface. Our intent is arranged in six different case studies that focus on crucial problems in pharmacodynamics, pharmacokinetics and toxicity fields. The first case study considers the estimation of a physicochemical property, the aqueous solvation free energy, that strictly relates to the pharmacokinetic profile and toxicity of chemicals. Our discussion on pharmacodynamics deals with the prediction of potency and selectivity of human adenosine receptor antagonists (hAR). The adenosine receptor family belongs to GPCR (G protein-coupled receptors) family A, including four different subtypes, referred to as A1, A2A, A2B and A3, which are widely distributed in the tissues. They differentiate for both pharmacological profile and effector coupling. Intensive explorative synthesis and pharmacological evaluation are aimed at discovering potent and selective ligands for each adenosine receptor subtype. In the present thesis, we have considered several pyrazolo-triazolo-pyrimidine and xanthine derivatives, studied as promising adenosine receptor antagonists. Then, a second case study focuses on the comparison and the parallel applicability of linear and nonlinear models to predict the binding affinity of human adenosine receptor A2A antagonists and find a consensus in the prediction results. The following studies evaluate the prediction of both selectivity and binding affinity to A2AR and A3R subtypes by combining classification and regression strategies, to finally investigate the full adenosine receptor potency spectrum and human adenosine receptor subtypes selectivity profile by applying a multilabel classification approach. In the field of pharmacokinetics, and more specifically in metabolism prediction, the use of multi- and single-label classification strategies is involved to analyze the isoform specificity of cytochrome P450 substrates. The results lead to the identification of the appropriate methodology to interpret the real metabolism information, characterized by xenobiotics potentially transformed by multiple cytochrome P450 isoforms. As final case study, we present a computational toxicology investigation. The recent regulatory initiatives due to REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) require the ecotoxicological and risk assessment of chemicals for safety. Most of the current evaluation protocols are based on costly animal experiments. So, chemoinformatic tools are heartily recommended to facilitate the toxicity characterization of chemical substances. We describe a novel integrated strategy to predict the acute aquatic toxicity through the combination of both toxicokinetic and toxicodynamic behaviors of chemicals, by using a machine learning classification method. The goal is to assign chemicals to different levels of acute aquatic toxicity, providing an appropriate answer to the new regulatory requirements. As preliminary validation of our approach, two toxicokinetic and toxicodynamic models have been applied in series to inspect both aquatic toxicity hazard and mode of action of a set of chemical substances with unknown or uncertain toxicodynamic information, assessing the potential ecological risk and the toxic mechanism.
Nuove strategie computazionali vengono continuamente richieste dall'industria farmaceutica per assistere, migliorare e velocizzare il processo di scoperta dei farmaci. In questo scenario la chemoinformatica fornisce affidabili strumenti matematici per ottenere relazioni quantitative struttura-attività (QSAR), in grado di descrivere la correlazione tra descrittori molecolari e vari profili sperimentali dei composti. Negli ultimi anni approcci non lineari di machine learning hanno dimostrato una notevole capacità predittiva in diverse applicazioni QSAR, confermando la loro superiorità sulle tradizionali metodologie lineari. E' stata evidenziata particolarmente la praticabilità dell'approccio di classificazione nel risolvere compiti complessi. Inoltre, l'introduzione del concetto di autocorrelazione in chimica permette il confronto strutturale delle molecole attraverso l'uso di una rappresentazione vettoriale di lunghezza fissa che serve da efficace descrittore molecolare. Nella presente tesi abbiamo studiato approfonditamente l'ampia applicabilità e le potenzialità delle strategie QSAR non lineari, soprattutto in combinazione con i descrittori autocorrelati potenziale elettrostatico molecolare proiettato sulla superficie molecolare. Il nostro intento si articola in sei differenti casi studio, che si concentrano su problemi cruciali nei campi della farmacodinamica, farmacocinetica e tossicologia. Il primo caso studio considera la valutazione di una proprietà fisico-chimica, l'energia libera di solvatazione acquosa, che è strettamente connessa con il profilo farmacocinetico e la tossicità dei composti chimici. La nostra discussione in farmacodinamica riguarda la predizione di potenza e selettività di antagonisti del recettore adenosinico umano (hAR). La famiglia del recettore adenosinico appartiene alla famiglia A di GPCR (recettori accoppiati a proteine G), che include quattro diversi sottotipi, cui ci si riferisce come A1, A2A, A2B e A3, ampiamente distribuiti nei tessuti. Si differenziano sia per profilo farmacologico che per effettore cui sono accoppiati. Le intense sintesi esplorativa e valutazione farmacologica hanno lo scopo di scoprire ligandi potenti e selettivi per ogni sottotipo del recettore adenosinico. Nella presente tesi abbiamo considerato diversi derivati pirazolo-triazolo-pirimidinici e xantinici, studiati come promettenti antagonisti del recettore adenosinico. Quindi, un secondo caso studio si focalizza sul confronto e l'applicabilità in parallelo di modelli lineari e non lineari per predire l'affinità di legame di antagonisti del recettore adenosinico A2A umano e trovare un consenso nei risultati di predizione. Gli studi successivi valutano la predizione sia della selettività che dell'affinità di legame ai sottotipi A2AR e A3R combinando strategie di classificazione e regressione, per studiare infine il completo spettro di potenza del recettore adenosinico e il profilo di selettività per i sottotipi hAR mediante l'applicazione di un approccio di classificazione multilabel. Nel campo della farmacocinetica, e più specificamente nella predizione del metabolismo, è coinvolto l'uso di strategie di classificazione multi- e single-label per analizzare la specificità di isoforma di substrati del citocromo P450. I risultati conducono all'identificazione della metodologia appropriata per interpretare la reale informazione sul metabolismo, caratterizzata da xenobiotici potenzialmente trasformati da multiple isoforme del citocromo P450. Come caso studio finale, presentiamo un'indagine in tossicologia computazionale. Le recenti iniziative regolatorie dovute al REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) richiedono l'accertamento ecotossicologico e del rischio dei composti chimici per la sicurezza. La maggiorparte dei correnti protocolli di valutazione è basata su costosi esperimenti animali. Così, gli strumenti chemoinformatici sono caldamente raccomandati per facilitare la caratterizzazione della tossicità di sostanze chimiche. Noi descriviamo una nuova strategia integrata per predire la tossicità acquatica acuta attraverso la combinazione di entrambi i comportamenti tossicocinetico e tossicodinamico dei composti chimici, utilizzando un metodo di classificazione machine learning. L'obbiettivo è assegnare i composti chimici a diversi livelli di tossicità acquatica acuta, fornendo un'appropriata risposta alle nuove esigenze regolatorie. Come validazione preliminare del nostro approccio, due modelli tossicocinetico e tossicodinamico sono stati applicati in serie per esaminare sia il rischio di tossicità acquatica che il modo d'azione di un set di sostanze chimiche con informazione tossicodinamica sconosciuta o incerta, valutandone il potenziale rischio ecologico ed il meccanismo tossico.
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Hasin, Saifa. "Test methodologies for dynamic fracture strength of single crystal silicon." College Park, Md. : University of Maryland, 2008. http://hdl.handle.net/1903/8195.

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Thesis (M.S.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Mechanical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Llinàs, Riera Maria del Carme. "New functionalization methodologies of mesoporous silica nanoparticles (MSNs) for biomedical applications." Doctoral thesis, Universitat Ramon Llull, 2016. http://hdl.handle.net/10803/369849.

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En la present tesi doctoral es descriu un procediment general per a l'obtenció de nanopartícules mesoporoses de sílice (MSNs) regioselectivament bifuncionalizades de forma ortogonal amb diferents grups funcionals. L'estratègia sintètica consisteix en la preparació de MSNs mitjançant co-condensació, seguit d'una posterior funcionalització covalent, mentre el tensioactiu es troba encara present en l'estructura de les MSNs. Seguint aquesta metodologia, s'han sintetitzat les nanopartícules bifuncionalizades amina-azida (MSN-(NH2)i(N3)o), amina-isotiocianat (MSN-(NH2)i(NCS)o) i amina-aldehid (MSN-(NH2)i(CHO)o), per al seu ús en aplicacions biomèdiques. En primer lloc, s'han sintetitzat i caracteritzat de forma homogènia i reproduïble les nanopartícules de referència (MSN-NH2) que permetran les successives funcionalitzacions. Aquestes nanopartícules aminades s'han fet servir posteriorment per a la síntesi de sensors de naftalimida. S'ha aconseguit desenvolupar un procediment general per a la introducció de 4-amino-1,8 naftalimides. Aquestes naftalimides han estat provades com a sensor i portes lògiques per a la detecció de H+ i F-. D'altra banda, s'ha descrit un protocol per preparar amino-azida-MSNs de forma regioselectiva. Aquestes MSNs han estat funcionalitzades per primera vegada amb foldàmers catiònics i la seva capacitat per creuar membranes citoplasmàtiques i viabilitat ha estat estudiada, així com l'ús d'aquests sistemes per a l'alliberament intracel·lular de Doxorubicina (DOX) de forma controlada. També s'ha realitzat un nou protocol per preparar MSNs amb isotiocianat en la seva estructura. La metodologia sintètica és general i es pot aplicar, en principi, a qualsevol MSNs aminada. L'eficiència de la funcionalització és comparable a la cicloaddició de coure (CuAAC) evitant els protocols d'aïllament i d'eliminació del metall. Seguint aquesta metodologia, s'han preparat unes noves amino-isotiocianat-MSNs per al disseny d'un nano-contenidor capaç d'alliberar el fàrmac Ataluren de forma controlada, per el seu us en la distròfia muscular de Duchenne (DMD). S'han aconseguit sintetitzar amina-aldehid-MSNs. Aquestes MSNs s'han aplicat com una nanoplataforma simple i versàtil capaç d'alliberar de forma dual una barreja CPT/DOX per al tractament del càncer, mitjançant l'ús d'estímuls de pH. Mentre un fàrmac és absorbit dins de la superfície interior, l'altre està unit covalentment a la superfície externa, actuant així, a la vegada, com a fàrmac i com agent bloquejant de porus. Aquest sistema respon als estímuls de pH i tots dos fàrmacs són només alliberats en un medi àcid.
En la presente tesis doctoral se describe un procedimiento general para la obtención de nanopartículas mesoporosas de sílice (MSNs) regioselectivamente bifuncionalizadas de forma ortogonal con distintos grupos funcionales. La estrategia sintética consiste en la preparación de MSNs mediande co-condensación, seguido de una posterior funcionalización covalente, mientras el tensioactivo se encuentra todavía presente en la estructura de las MSNs. Siguiendo esta metodología, se han sintetizado las nanopartículas bifuncionalizadas amina-azida (MSN-(NH2)i(N3)o), amina-isotiocianato (MSN-(NH2)i(NCS)o) y amina-aldehído (MSN-(NH2)i(CHO)o), para su uso en aplicaciones biomédicas. En primer lugar, se han sintetizado y caracterizado de forma homogénea y reproducible las nanopartículas aminadas de referencia (MSN-NH2) que permitirán las sucesivas funcionalizaciones, con un tamaño de 50 nm y 100 nm aproximadamente. Estas nanopartículas aminadas se han usado posteriormente para la síntesis de sensores de naftalimida. Se ha conseguido desarrollar un procedimiento general para la introducción de 4-amino-1,8 naftalimidas. Estas naftalimidas han sido probadas como sensores y puertas lógicas para la detección de H + y F-. Por otra parte, se ha descrito un protocolo para preparar amino-azida-MSNs de forma regioselectiva. Estas MSNs han sido funcionalizadas por primera vez con foldámeros catiónicos y su capacidad para cruzar membranas citoplasmáticas y viabilidad ha sido estudiada, así como el uso de estos sistemas para la liberación intracelular de doxorubicina (DOX) de forma controlada. También se ha realizado un nuevo protocolo para preparar MSNs con isotiocianato en su estructura. La metodología sintética es general y puede aplicarse, en principio, a todo tipo de MSNs aminadas. La eficiencia de la funcionalización es comparable a la cicloadición de cobre (CuAAC) evitando los protocolos de aislamiento y de eliminación del metal. Siguiendo esta metodología, se han preparado unas nuevas amino-isotiocianato-MSNs para el diseño de un nano-contenedor capaz de liberar el fármaco Ataluren de forma controlada. Se ha logrado sintetizar amina-aldehído-MSN. Estas MSNs se han aplicado como una nanoplataforma simple y versátil capaz de liberar de forma dual una mezcla CPT/DOX para el tratamiento del cáncer, mediante el uso de estímulos de pH. Mientras un fármaco es absorbido dentro de la superficie interior, el otro está unido covalentemente a la superficie externa, actuando así como fármaco y como agente bloqueante de poro. Este sistema responde a los estímulos de pH y ambos fármacos son solamente liberados en un medio ácido.
In this PhD dissertation, a general procedure for the obtaining of different regioselective orthogonal bifunctionalized mesoporous silica nanoparticles (MSNs) has been carried out. The strategy consists of a covalent functionalization of co-condensed monodispersed and uniform aminated-MSNs, where tensioactive is still present in its structure. Three bifunctionalized MSNs, amine-azide (MSN-(NH2)i(N3)o), amine-isothiocyanate (MSN-(NH2)i(NCS)o) and amine-aldehyde (MSN-(NH2)i(CHO)o), with efficient “click” reactions, have been synthetized for its use in biomedical applications. First, a well characterized batch of precursor aminated-MSNs (MSN-(NH2)) has been prepared. The best conditions for the synthesis of homogenous and reproducible MSN-(NH2) with a size between 50-100 nm have been studied. These aminated-MSNs have been used for the synthesis of naphthalimide sensors where a general procedure for the introduction of 4-amine-1,8-naphthalimides has been developed. These naphthalimides have been tested as potential logic gates for the detection of H+ and F-. A straightforward protocol to prepare amine-azide MSNs has been described. These MSNs have been functionalized with quinolin cationic foldamers for the first time. The ability of these foldamer-MSNs to cross cytoplasmic membranes and its viability has been studied. The penetrating capacity of foldamer-MSNs have been used for intracellular delivery of Doxorubicin (DOX). A new protocol to prepare isothiocyanate functionalized MSNs is described. The synthetic methodology is general and can be applied, in principle, to all type of aminated MSNs. The efficiency of the functionalization is comparable to the copper cycloaddition (CuAAC) avoiding isolation and copper removal protocols. Following this methodology, new amino-isothiocyanate-MSNs have been prepared for the design of a nano-container able to release the drug Ataluren in a controlled manner, for the treatment of Duchenne muscular dystrophy (DMD). Regioselective bifunctionalized amine-aldehyde-MSNs have been synthetized. These MSNs have been applied as a versatile nanoplatform able to release dual synergistic CPT/DOX mixture for cancer treatment only by using pH stimuli. While CPT is absorbed at the inner surface, DOX is covalently linked to the external surface acting both as an active and a capping agent (pH=4).
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Dumeunier, Raphaël. "New methodologies towards lactones and methylene-lactones : application to the total synthesis of Polycavernoside A." Université catholique de Louvain, 2004. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-05242004-130732/.

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Methylene-butyrolactones are readily accessed by two methodologies based on a particular ene reaction. Both methodologies have been applied to the synthesis of the northern fragment of Polycavernoside. A reverse Julia reaction was used as the key step of a new methodology towards triene frameworks ; a mechanistic study revealed the unexpected role of triflic acid in the field of metal triflates catalysed acylation of alcohols, and a new tandem Brook rearrangement-allylation sequence was developed in the viewpoint of an improved total synthesis of Polycavernoside A.
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Leming, Edward J. "Particle physics methodologies applied to time-of-flight positron emission tomography with silicon-photomultipliers and inorganic scintillators." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/54457/.

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Positron emission tomography, or PET, is a medical imaging technique which has been used in clinical environments for over two decades. With the advent of fast timing detectors and scintillating crystals, it is possible to envisage improvements to the technique with the inclusion of time-of-flight capabilities. In this context, silicon photomultipliers coupled to fast inorganic LYSO crystals are investigated as a possible technology choice. As part of the ENVISION collaboration a range of photon detectors were investigated experimentally, leading to the selection of specific devices for use in a first prototype detector, currently being commissioned at the Rutherford Appleton Laboratory. In order to characterise the design of the prototype a GEANT4 simulation has been developed describing coupled systems of silicon photomultipliers and LYSO scintillators. Very good agreement is seen between the timing response of the experimental and simulated systems. Results of the simulation for a range of detector array arrangements are presented and a number of optimisations proposed for the final prototype design. Without the results provided here a detector system including only 3x3x5 mm3 crystals would have been adopted. A 3x3x5 mm3 crystal geometry is shown to provide little-to-no timing advantage over an identical system with 3x3x10 mm3 crystals, where detection efficiency is improved by approximately a factor of three. Additionally an investigation is presented which explores the impact of using events where gamma-ray photons are scattered internally within the detector array. It is shown that including such events could increase the signal achievable with one-to-one coupled detector arrays systems for PET by approximately 60%, with only minor reductions in coincidence timing resolution.
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Nordesjö, Olle. "Searching for novel protein-protein specificities using a combined approach of sequence co-evolution and local structural equilibration." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275040.

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Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position.
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Baillot, Raphaël. "METHODOLOGIE D'ANALYSE DE DEFAILLANCE POUR L'EVALUATION DE LA FIABILITE DE DIODES ELECTROLUMINESCENTES GaN." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2011. http://tel.archives-ouvertes.fr/tel-00673985.

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Ce mémoire s'inscrit dans la construction d'une méthodologie d'analyse de défaillance pour l'évaluation de la fiabilité de diodes électroluminescentes, par une approche basée sur l'analyse physique de dégradation et l'extraction de signatures électriques de défaillance et optiques pour localiser les zones dégradées. L'ajout d'analyses physico-chimiques réduit le nombre de composants et peut confirmer les mécanismes de dégradation induits par les vieillissements en stockage actif. Un projet, en collaboration avec le CNES, a permis la mise en évidence des zones sensibles de DELs à MPQ InGaN/GaN à faible puissance (30mW) soumises à un vieillissement en conditions opérationnelles (1500h/85°C/Inominal). L'analyse de défaillance de ces DELs a permis d'expliquer une perte de 65% de puissance optique par la modification de la structure moléculaire de l'huile silicone activée photothermiquement induisant une perte de fluorescence de 69% et une très forte diminution de l'absorption de la lumière de la DEL (90%). Nous avons également démontré (projet CEA-LETI - éclairage public) que le même mécanisme est présent dans le mélange gel silicone/phosphore YAG:Ce de DELs blanches à MPQ InGaN/GaN soumises à un vieillissement similaire (85°C/550mA/500h). A 450nm, le rendement de fluorescence a augmenté de 1,2% malgré des pertes en absorption (> 94%) et en réémission de fluorescence (> 85%). La modification de la structure moléculaire du gel a induit une perte de puissance optique des DELs de 45% et une dérive de la couleur blanche vers le jaune (≈ 3,6%). Cette dérive est due à un décalage spectral de la fluorescence de l'UV (5nm) vers le bleu entraînant un décalage vers le rouge (2nm) de la lumière de la DEL.
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Farahini, Nasim. "SiLago: Enabling System Level Automation Methodology to Design Custom High-Performance Computing Platforms : Toward Next Generation Hardware Synthesis Methodologies." Doctoral thesis, KTH, Elektronik och Inbyggda System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185787.

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Books on the topic "In silico methodologies"

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A, Bayoumi Magdy, ed. VLSI design methodologies for digital signal processing architectures. Boston: Kluwer Academic, 1994.

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United States. National Aeronautics and Space Administration., ed. Final report on the development of methodologies and solvent systems to replace CFC-113 in the validation of large-scale spacecraft hardware: NASA research grant award no. NAG10-0169. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Roy, Kunal. In Silico Drug Design: Repurposing Techniques and Methodologies. Elsevier Science & Technology, 2019.

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Roy, Kunal. In Silico Drug Design: Repurposing Techniques and Methodologies. Elsevier Science & Technology Books, 2019.

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Bayoumi, Magdy A. Vlsi Design Methodologies for Digital Signal Processing Architectures. Springer, 2012.

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Bayoumi, Magdy A. VLSI Design Methodologies for Digital Signal Processing Architectures. Springer London, Limited, 2012.

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Ellam, Rob. 8. Scratching the surface with cosmogenic isotopes. Oxford University Press, 2016. http://dx.doi.org/10.1093/actrade/9780198723622.003.0008.

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‘Scratching the surface with cosmogenic isotopes’ explains spallation—when a high energy cosmic ray particle removes several nucleons from an atom. Spallation produces 10Be from 16O in the atmosphere and rock surfaces, while spallation of silicon produces another cosmogenic isotope, 26Al. Cosmogenic isotope production is about four times greater at the poles than at the equator and is also greater at higher altitudes. To calculate a cosmogenic isotope exposure age, the latitude and altitude at which the sample was exposed needs to be known. Using ‘exposure’ and ‘burial’ methodologies, cosmogenic isotopes can be used to address various scientific problems such as recreating the seismic histories of tectonically active areas.
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Fejerskov, Adam. The Global Lab. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198870272.001.0001.

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The Global Lab maps out the political, institutional, and ethical coordinates of a new experimental movement operating across the Global South. It tells the story of how a group of organizations, foundations, and corporations are increasingly using developing countries, local communities, and refugee camps as live laboratories to experiment with untested technologies, biometric humanitarian solutions, new drugs, and radical methodologies for social change. The book asks where and how this movement works, laying bare the human, ethical, philosophical, and political consequences of its practices. The Global Lab takes the reader through Silicon Valley, Africa, and Asia to understand the tangible and transformative implications of experimentation, following a set of main protagonists, from the Bill and Melinda Gates Foundation to experimental economists known as the randomistas, to humanitarian organizations and pharmaceutical companies. Deeply entrenched in contemporary experimental practice, these actors form a movement inspired by core logics emanating out of Silicon Valley about the need for fast-paced radical change and societal disruption, technological innovation as progress, and the privatization and commercialization of the human mind and body. Ultimately, the book examines the inequality of experimentation that is found in the erection of imaginary walls between us and them, and the imagined universal and often unquestioned value of scientific and technological progress in the name of development.
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Book chapters on the topic "In silico methodologies"

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Myrianthopoulos, Vassilios, George Lambrinidis, and Emmanuel Mikros. "In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies." In Methods in Molecular Biology, 261–77. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8630-9_15.

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Sarkar, Indrani, and Sanjay Goswami. "Computational Methodologies Followed in Structure Based In-Silico Drug Design: An Example." In Lecture Notes in Networks and Systems, 569–74. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3953-9_55.

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Marousis, Konstantinos D., Aikaterini C. Tsika, Maria Birkou, Minos-Timotheos Matsoukas, and Georgios A. Spyroulias. "Lead Identification Through the Synergistic Action of Biomolecular NMR and In Silico Methodologies." In Methods in Molecular Biology, 299–316. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8630-9_18.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomics Data." In Big Data in Bioeconomy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_6.

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AbstractIn silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.
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Azevedo, Vasco, Vinicius Abreu, Sintia Almeida, Anderson Santos, Siomar Soares, Amjad Ali, Anne Pinto, et al. "Whole Genome Annotation: In Silico Analysis." In Bioinformatics - Trends and Methodologies. InTech, 2011. http://dx.doi.org/10.5772/23724.

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Ogilvie, Lesley A., Damian T. Rieke, and Hans Lehrach. "A vision: in silico clinical trials without patients." In Innovation in Clinical Trial Methodologies, 39–48. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-824490-6.00020-7.

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"Protein-Protein Interaction Inhibition (2P2I): Mixed Methodologies for the Acceleration of Lead Discovery." In In-Silico Lead Discovery, edited by Philippe Roche and Xavier Morelli, 118–43. BENTHAM SCIENCE PUBLISHERS, 2012. http://dx.doi.org/10.2174/978160805142711101010118.

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Minovski, Nikola, and Marjana Novič. "Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates." In Oncology, 434–81. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0549-5.ch016.

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Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.
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Minovski, Nikola, and Marjana Novič. "Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates." In Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, 269–317. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8136-1.ch008.

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Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.
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ElHefnawi, Mahmoud, and Mohamed Mysar. "In-silico Approaches for RNAi Post-Transcriptional Gene Regulation: Optimizing siRNA Design and Selection." In Bioinformatics - Trends and Methodologies. InTech, 2011. http://dx.doi.org/10.5772/18455.

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Conference papers on the topic "In silico methodologies"

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Barreira, Nina, Rodrigo Silva, Tatiana Tilli, and Patrícia Neves. "Identification of breast cancer neoantigens using in silico methodologies." In IV International Symposium on Immunobiologicals & VII Seminário Anual Científico e Tecnológico. Instituto de Tecnologia em Imunobiológicos, 2019. http://dx.doi.org/10.35259/isi.sact.2019_32690.

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Panada, Dario, and Bijan Parsia. "The Trap of 2D in Artificial Models of Tumours: The Case for 3D In-silico Simulations." In 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010517702390247.

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Panada, Dario, and Bijan Parsia. "The Trap of 2D in Artificial Models of Tumours: The Case for 3D In-silico Simulations." In 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010517700002995.

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Narahara, Taro. "New Methodologies in Architectural Design inspired by Self-Organization." In ACADIA 2008: Silicon + Skin. ACADIA, 2008. http://dx.doi.org/10.52842/conf.acadia.2008.324.

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Chrostowski, Lukas, Jonas Flueckiger, Charlie Lin, Michael Hochberg, James Pond, Jackson Klein, John Ferguson, and Chris Cone. "Design methodologies for silicon photonic integrated circuits." In SPIE OPTO, edited by Louay A. Eldada, El-Hang Lee, and Sailing He. SPIE, 2014. http://dx.doi.org/10.1117/12.2047359.

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Oosterbroek, R. E., J. W. Berenschot, A. J. Nijdam, Gregory Pandraud, Miko C. Elwenspoek, and Albert van den Berg. "New design methodologies in (111)-oriented silicon wafers." In Symposium on Micromachining and Microfabrication, edited by James H. Smith and Jean Michel Karam. SPIE, 1999. http://dx.doi.org/10.1117/12.361243.

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Hibbard, Douglas L., Steven J. Hoskins, and Evan C. Lundstedt. "Surface figuring of silicon carbide using chemical etching methodologies." In Optical Science, Engineering and Instrumentation '97, edited by Alson E. Hatheway. SPIE, 1997. http://dx.doi.org/10.1117/12.284076.

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Chitakudige, Ramachandra, Sarat Kumar Dash, and A. M. Khan. "Deprocessing Methodologies for Detection of IBC and Cell-to-Cell Shorts in Submicron DRAM." In ISTFA 2012. ASM International, 2012. http://dx.doi.org/10.31399/asm.cp.istfa2012p0383.

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Abstract Detection of both Insufficient Buried Contact (IBC) and cell-to-cell short defects is quite a challenging task for failure analysis in submicron Dynamic Random Access Memory (DRAM) devices. A combination of a well-controlled wet etch and high selectivity poly silicon etch is a key requirement in the deprocessing of DRAM for detection of these types of failures. High selectivity poly silicon etch methods have been reported using complicated system such as ECR (Electron Cyclotron Resonance) Plasma system. The fact that these systems use hazardous gases like Cl2, HBr, and SF6 motivates the search for safer alternative deprocessing chemistries. The present work describes high selectivity poly silicon etch using simple Reactive Ion Etch (RIE) plasma system using less hazardous gases such as CF4, O2 etc. A combination of controlled wet etch and high selectivity poly silicon etch have been used to detect both IBC and cell-to-cell shorts in submicron DRAMs.
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Liao, Joy Y., Howard Lee Marks, and Herve Deslandes. "Complementary Optical Techniques for Advanced IC Failure Analysis – Case Study." In ISTFA 2005. ASM International, 2005. http://dx.doi.org/10.31399/asm.cp.istfa2005p0084.

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Abstract We investigated and demonstrated the advantages and limitations of several optical methodologies as valuable silicon failure analysis techniques, and how they could be used in a complementary manner to assist in shortening the diagnostic time.
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Chakraverty, Mayank, Krishna Arla Prabhu, and P. S. Harisankar. "Development, characterization and validation of compact models for RF analog and mixed signal PDKS enabling first pass silicon." In 2017 International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2017. http://dx.doi.org/10.1109/iccmc.2017.8282570.

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