190 10 20655 https://socictopen.socict.org/files/original/cc66f7b07ef6184a7c557e1bec8a1b7f.pdf c3cf8df231ee4c9c46354f9159efb0d7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis on the characteristics and contributing factors of ophthalmic online consultation during the COVID-19 epidemic Creator An entity primarily responsible for making the resource Zhen-Zhen Liu, Xiao-Hang Wu, Jing-Jing Chen, Pi-Song Yan, Ling Jin, Wei-Ling Hu, En-En Zhang, Wei Li, Hao-Tian Lin Description An account of the resource AIM: To analyze the demands, consultation patterns and contributing factors of the patients involved in the ophthalmic on-line consultation during the COVID-19 epidemic to provide reference for the optimization of the ophthalmic online health care system.METHODS: Prospective observational study. The patients using “expert online consultation” provided by the Zhongshan Ophthalmic Center from February 1 to March 15, 2020 were enrolled. The baseline characteristics were analyzed. For the patients with repeated consultation, we randomly selected the same amount of patients with single consultation as comparison. The baseline characteristics, consultation purposes, disease types, and number of combined eye diseases between the patients with single and repeated consultation were compared, and the contributing factors of patients'consultation pattern were analyzed with statistical test and regression model.RESULTS: The “expert online consultation” provided 9 831 consultations during the study, 3 919 of which were single consultation while 5 912 of which came from the repeated consultations of 1 967 patients. 1 967 patients with single consultation were randomly selected and compared with the repeated inquirers. The main consultation purposes in the both groups were “return visit”(59.7%, 64.9%). There was no significant difference in age and gender distribution between the patients with single online visit and repeated online visits(P=0.897, 0.482). Compared with the patients with single visit, the patients with repeated visit were less with the purpose of “new-onset discomfort”, more with the purpose of “return visit” and “follow-up after surgery”, more with ocular surface, fundus and uveal diseases, and more with two or more combined eye diseases. Regression analysis showed that the patients with ocular refractive issues tended to have a single consultation, while the patients inquired on previous eye disease and surgery, with fundus or uveal disease tended to have repeated consultations. The number of repeated visits for the patients with diagnosed ophthalmopathy and ophthalmic surgery was higher than that for the patients with new-onset discomfort; the number of repeated visits was lower for the patients with lens and anterior segment diseases, and was higher for the patients with ocular surface diseases.CONCLUSION: During the COVID-19 epidemic, the main purpose of on-line ophthalmic consultation was return visit for the diagnosed eye diseases. Type of eye disease, consultation purpose and number of combined ophthalmopathies all affected whether the patients repeated the consultation and the repeated times. The arrangement of ophthalmologists in different sub-specialties should be adjusted according to the patients' needs and their consultation patterns. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Epidemic, covid-19, , telemedicine Identifier An unambiguous reference to the resource within a given context 10.3980/j.issn.1672-5123.2020.11.35 Source A related resource from which the described resource is derived Guoji Yanke Zazhi Publisher An entity responsible for making the resource available Press of International Journal of Ophthalmology (IJO PRESS) Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ophthalmology https://socictopen.socict.org/files/original/3e680ef8a7d7fc5b251c05dfd1f54d38.pdf 716b8afe54d58461d018bade86a79f39 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China Creator An entity primarily responsible for making the resource Chao Zou, Xiaoxia Wang, Luqi Wang Description An account of the resource During the large-scale outbreak of COVID-19 in China, the Chinese government adopted multiple measures to prevent the epidemic. The consequence was that a sudden variation in residents’ travel behavior took place. In order to better evaluate the temporal distribution of air pollution, and to effectively explore the influence of human activities on air quality, especially under the special situation, this study was conducted based on the real data from a case city in China from this new perspective. Two case scenarios were constructed, in which the research before the changes of residents’ travel behavior was taken as case one, and the research after the changes in residents’ travel behavior as case two. The hourly real-time concentrations of PM2.5, PM10, SO2, NO2, CO and O3 that have passed the augmented Dickey–Fuller (ADF) test were employed as a data source. A series of detailed studies have been carried out using the correlation method, entropy weight method and the Air Quality Index (AQI) calculation method. Additionally, the research found that the decrease rate of NO2 concentration is 61.05%, and the decrease rate of PM10 concentration is 53.68%. On the contrary, the average concentration of O3 has increased significantly, and its growth rate has reached to 9.82%. Although the air quality in the first week with fewer travels was in the excellent category, and chief pollutant (CP), as well as excessive pollutant (EP), were not found, as traffic volume increased, it became worse in the second and third weeks. In addition to that, special attention should still be paid to the development trend of O3, as its average hourly concentration has increased. The findings of this study will have some guiding significance for the study of air pollution prevention, cleaner production, and indoor environmental safety issues, especially for the study of abnormal traffic environments where residents’ travel behaviors have changed significantly. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource air pollution, particulate matter, travel behavior, Human activity, Temporal Distribution, gaseous contaminants Identifier An unambiguous reference to the resource within a given context 10.3390/ijerph17144947 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine https://socictopen.socict.org/files/original/e9fcee98fa548c89040f301a73198581.pdf 61e00df14ecd0ca2a439f649951ec4c2 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analytical and clinical performance of the panbio COVID-19 antigen-detecting rapid diagnostic test. Creator An entity primarily responsible for making the resource Andrea Alemany, Bàrbara Baró, Dan Ouchi, Pau Rodó, Maria Ubals, Marc Corbacho-Monné, Júlia Vergara-Alert, Jordi Rodon, Joaquim Segalés, Cristina Esteban, Gema Fernández, Lidia Ruiz, Quique Bassat, Bonaventura Clotet, Jordi Ara, Martí Vall-Mayans, Camila G-Beiras, Ignacio Blanco, Oriol Mitjà Date A point or period of time associated with an event in the lifecycle of the resource 2021 Identifier An unambiguous reference to the resource within a given context 10.1016/j.jinf.2020.12.033 Source A related resource from which the described resource is derived The Journal of infection https://socictopen.socict.org/files/original/911495e36624397e1ad80eed1a761e2a.pdf cf47bfc3e310cf91e8cdae9f3dac698f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analytical model of innovation ecosystem development Creator An entity primarily responsible for making the resource Popov Evgeny, Dolghenko Ruslan, Simonova Victoria, Chelak Igor Description An account of the resource The fundamental challenges faced by the global economy with the advent of the COVID-19 pandemic and the prospects for re-globalization provide opportunities for the intensification of the development of new forms of coordination of economic relations – innovative ecosystems (regional, industrial, entrepreneurial). It seems that economic relations are built within the framework of a particular ecosystem, and the scientific descriptive and predictive analytics are not sufficiently able to describe their nature and the main patterns of growth. In order to provide a theoretical and practical contribution to the developing ecosystem approach, the article presents an analytical model of the innovation ecosystem of a high-tech company, proposed for evaluating and comparing its level of development. Research methods include content analysis of scientific literature devoted to the study of innovative ecosystems and approaches to their assessment, system analysis and factor modeling. The research fundament is based on the dynamics of indicators of innovative development of the ecosystem of a large production company in the Sverdlovsk region for 2013-2019. The model was tested according to the indicators of 2018. Further directions of the research in the practical aspect will consist in a comparative analysis of other periods of the company’s activity, as well as in the application and verification of the model on the indicators of other organizations. Justification of the possibilities of creating technologies for managing the innovation ecosystem is seen as a theoretical component of future research. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Identifier An unambiguous reference to the resource within a given context 10.1051/e3sconf/202125001004 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Environmental sciences https://socictopen.socict.org/files/original/3b63ecc9d479d3df7cd647824ae750a5.pdf dbc61fb0fa27efc573545a10055aa0ca Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analytical parameters and validation of homopolymer detection in a pyrosequencing-based next generation sequencing system Creator An entity primarily responsible for making the resource Gergely Ivády, László Madar, Erika Dzsudzsák, Katalin Koczok, János Kappelmayer, Veronika Krulišová, Milan Macek, Attila Horváth, István Balogh Description An account of the resource Abstract Background Current technologies in next-generation sequencing are offering high throughput reads at low costs, but still suffer from various sequencing errors. Although pyro- and ion semiconductor sequencing both have the advantage of delivering long and high quality reads, problems might occur when sequencing homopolymer-containing regions, since the repeating identical bases are going to incorporate during the same synthesis cycle, which leads to uncertainty in base calling. The aim of this study was to evaluate the analytical performance of a pyrosequencing-based next-generation sequencing system in detecting homopolymer sequences using homopolymer-preintegrated plasmid constructs and human DNA samples originating from patients with cystic fibrosis. Results In the plasmid system average correct genotyping was 95.8% in 4-mers, 87.4% in 5-mers and 72.1% in 6-mers. Despite the experienced low genotyping accuracy in 5- and 6-mers, it was possible to generate amplicons with more than a 90% adequate detection rate in every homopolymer tract. When homopolymers in the CFTR gene were sequenced average accuracy was 89.3%, but varied in a wide range (52.2 – 99.1%). In all but one case, an optimal amplicon-sequencing primer combination could be identified. In that single case (7A tract in exon 14 (c.2046_2052)), none of the tested primer sets produced the required analytical performance. Conclusions Our results show that pyrosequencing is the most reliable in case of 4-mers and as homopolymer length gradually increases, accuracy deteriorates. With careful primer selection, the NGS system was able to correctly genotype all but one of the homopolymers in the CFTR gene. In conclusion, we configured a plasmid test system that can be used to assess genotyping accuracy of NGS devices and developed an accurate NGS assay for the molecular diagnosis of CF using self-designed primers for amplification and sequencing. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Subject The topic of the resource pyrosequencing, Homopolymer detection, Cystic fibrosis Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12864-018-4544-x Source A related resource from which the described resource is derived BMC Genomics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Genetics, Biotechnology Language A language of the resource EN https://socictopen.socict.org/files/original/e9d132c338cca57331e687defaaea045.pdf 2819c9d464dde05f06d8d63e5477262e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analytical Review of COVID-19 Outbreak in India During the Global Pandemic Creator An entity primarily responsible for making the resource Amit Ganatra, Parth Goel, Dweepna Garg Description An account of the resource COVID-19 is one of the very contagious diseases from the family Coronaviridae and spreading at afaster rate in the community. In December 2019, the first case of COVID-19 was reported in Wuhan,China. An epidemic outbreak of COVID-19 was seen in India from March 2020. Epidemiological dataof COVID-19 cases of the world and India have been analyzed in our study. We have utilized publiclyavailable two databases from data repository by Johns Hopkins CSSE and covid19india.org. COVID-19cases and case fatality rate (CFR) of the world have been summarized and compared with India fromJanuary 22, 2020 to April 15, 2020. Indian cases were analyzed among states of India and also comparedwith age and gender by performing statistical approaches such as central tendency, standard deviationand interquartile range. By April 15, 2020, Indian has reported 12,322 confirmed cases, 1,498 recoveredcases and 405 death cases of COVID-19. In spite of India being a diverse country with the secondhighest population, the deadly side of COVID-19 was comparatively far less as compared to the othercountries. India has taken preemptive measures at an early stage to prevent transmission of COVID-19outbreak and it is reviewed from our study by comparing India with other countries. Our study alsosummarizes that age also plays a vital role in the intervention of COVID-19 cases. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource India, coronavirus, statistical analysis, epidemic, Pandemic, COVID-19 Identifier An unambiguous reference to the resource within a given context DOI: 10.22207/JPAM.14.SPL1.27 Source A related resource from which the described resource is derived Journal of Pure and Applied Microbiology Publisher An entity responsible for making the resource available Journal of Pure and Applied Microbiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Microbiology https://socictopen.socict.org/files/original/6a26f98a4874061831ee214a4fa3333a.pdf 250cfe3fe5c1d95ba95b04a397e49763 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics Creator An entity primarily responsible for making the resource Umberto Ferraro Petrillo, Mara Sorella, Giuseppe Cattaneo, Raffaele Giancarlo, Simona E. Rombo Description An account of the resource Abstract Background Distributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the software with respect to the specific framework under consideration may be crucial in order to achieve good performance, especially on very large amounts of data. We choose k-mers counting as a case study for our analysis, and Spark as the framework to implement FastKmer, a novel approach for the extraction of k-mer statistics from large collection of biological sequences, with arbitrary values of k. Results One of the most relevant contributions of FastKmer is the introduction of a module for balancing the statistics aggregation workload over the nodes of a computing cluster, in order to overcome data skew while allowing for a full exploitation of the underlying distributed architecture. We also present the results of a comparative experimental analysis showing that our approach is currently the fastest among the ones based on Big Data technologies, while exhibiting a very good scalability. Conclusions We provide evidence that the usage of technologies such as Hadoop or Spark for the analysis of big datasets of biological sequences is productive only if the architectural details and the peculiar aspects of the considered framework are carefully taken into account for the algorithm design and implementation. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource Distributed Computing, Apache Spark, k-mer counting, Performance evaluation Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12859-019-2694-8 Source A related resource from which the described resource is derived BMC Bioinformatics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Computer applications to medicine. Medical informatics Language A language of the resource EN https://socictopen.socict.org/files/original/6d71b37d67a13a80e068393148ec9a76.pdf 7b8ad05cfaa03441d8916ef881e36426 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analyzing COVID-19 Impacts on Vehicle Travels and Daily Nitrogen Dioxide (NO₂) Levels among Florida Counties Creator An entity primarily responsible for making the resource Alican Karaer, Nozhan Balafkan, Michele Gazzea, Reza Arghandeh, Eren Erman Ozguven Description An account of the resource The COVID-19 outbreak and ensuing social distancing behaviors resulted in substantial reduction on traffic, making this a unique experiment on observing the air quality. Such an experiment is also supplemental to the smart city concept as it can help to identify whether there is a delay on air quality improvement during or after a sharp decline on traffic and to determine what, if any, factors are contributing to that time lag. As such, this study investigates the immediate impacts of COVID-19 causing abrupt declines on traffic and NO2 concentration in all Florida Counties through March 2020. Daily tropospheric NO2 concentrations were extracted from the Sentinel-5 Precursor satellite and vehicle mile traveled (VMT) estimates were acquired from cell phone mobility records. It is observed that overall impacts of the COVID-19 response in Florida have started in the first half of the March 2020, two weeks earlier than the official stay-at-home orders, and resulted in 54.07% and 59.68% decrease by the end of the month on NO2 and VMT, respectively. Further, a cross-correlation based dependency analysis was conducted to analyze the similarities and associated time lag between 7-day moving averages of VMT and NO2 concentrations of the 67 counties. Although such reduction is unprecedented for both data sets, results indicate a strong correlation and this correlation increases with the identification of a time lag between VMT and NO2 concentration. Majority of the counties have no time lag between VMT and NO2 concentration; however, a cluster of South Florida counties presents earlier decrease on NO2 concentration compare to VMT, which indicates that the air quality improvements in those counties are not traffic related. Investigation on the socioeconomic factors indicates that population density and income level have no significant impact on the time lag between traffic and air quality improvements in light of COVID-19. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource covid-19, traffic, remote sensing, nitrogen dioxide (NO₂), Sentinel-5P, vehicle mile traveled (VMT) Identifier An unambiguous reference to the resource within a given context 10.3390/en13226044 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Technology https://socictopen.socict.org/files/original/72b147313d6e4658d68befc826857d91.pdf a3bee16ef97bc3831231581923e19765 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM Creator An entity primarily responsible for making the resource Yasemen UÇAN, Nilgün GÜLER BAYAZIT, Melike Bildirici Description An account of the resource Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Time series Forecasting, LSTM, crude oil prices, LSTARGARCH Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/en13112980 Source A related resource from which the described resource is derived Energies Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Technology https://socictopen.socict.org/files/original/4e30eb177f99c1cf63da04bb7c2d14a0.pdf d2d1126cfcf06534bf49b435ab55ea1f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing – Review Creator An entity primarily responsible for making the resource Gabriel Dorado, Sergio Gálvez, Teresa E. Rosales, Víctor F. Vásquez, Pilar Hernández Description An account of the resource Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource Next generation sequencing (NGS), first-generation sequencing (FGS), high throughput sequencing (HTS), second-generation sequencing (SGS), structural genomics, third-generation sequencing (TGS) Identifier An unambiguous reference to the resource within a given context 10.3390/biom11081111 Source A related resource from which the described resource is derived Biomolecules Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Microbiology Relation A related resource <a href="https://www.mdpi.com/2218-273X/11/8/1111" target="_blank" rel="noreferrer noopener">https://www.mdpi.com/2218-273X/11/8/1111</a>