Database Archive Research Articles

Economic evaluations of treatment of depressive disorders in adolescents: Protocol for a scoping review.

Depressive disorders in adolescents are common and impairing. Evidence-based treatments are available; however, at a cost. In the context of the COVID-19 pandemic, we anticipate increased demand for treatment services for adolescents with depression. We also anticipate that economic resources will be strained. Identifying cost-effective strategies to optimally treat depression in adolescents is imperative. This protocol for a scoping review aims to describe the literature with respect to economic evaluations of treatments for depression in adolescents. We will conduct a scoping review using established methods and reporting guidelines. MEDLINE, Embase, PsyclNFO, Econlit, and the International HTA Database will be searched from inception to June 13, 2023, with an update closer to time of manuscript submission, while the NHS Economic Evaluation Database archives will be searched from inception to December 2014. Publications that contain economic evaluations, in the context of a clinical trial or a model-based study, testing a treatment of depression in adolescents will be selected for inclusion. Extracted data items will include: economic evaluation perspectives, health outcome variables and costs used in economic evaluations, types of analyses performed, as well as quality of reporting and methodology. A narrative synthesis with summary tables will be used to describe our findings. Our findings will help identify gaps in the literature with respect to economic analyses for the treatment of depression such that these gaps can be filled with future research. Policy-makers, funders and administrators may also use our findings to inform their decisions around provision of various treatments for depression in adolescents. osf.io/5fteb (note that information on this link will be updated upon acceptance for publication based on reviewer comments).

Analgesic effects of ibuprofen soft and flexible absorbent foam dressing after colorectal surgery: A single-center comparative study

Abstract Background Postoperative wound care and pain management are crucial components of the postoperative recovery process. This study aims to evaluate whether the analgesic effects of the ibuprofen soft and flexible absorbent foam dressing are superior to those of traditional gauze after colorectal surgery. Materials and Methods Data pertaining to patients who underwent colorectal surgery between May 2021 and January 2022 were extracted from the electronic medical records database archive of a single institution. The ibuprofen soft and flexible absorbent foam dressing was applied to 62 patients for postoperative wound care, whereas gauze was used for 78 patients. Primary outcome measurements included the count of dressings, visual analog scale pain scores, analgesic dosage, complications, and length of hospitalization. Results The mean pain score on the 5th day postoperation was significantly lower for the ibuprofen group (2.03 ± 1.55) than for the control group (2.63 ± 1.72, p = 0.035). The mean dosage (mg) of parecoxib on the 3rd day postoperation was significantly lower for the ibuprofen group (2.58 ± 12.27) than for the control group (10.26 ± 21.80, p = 0.01). The cumulative number of dressing changes during the 1st week postoperation was significantly lower for the ibuprofen group (mean ± standard deviation, 3.00 ± 0.98) than for the control group (7.55 ± 2.68, p < 0.001). No significant differences were observed in the complication rate or length of hospitalization between the groups. Conclusions The ibuprofen soft and flexible absorbent foam dressing can either enhance the analgesic effect during postoperative recovery or reduce the cost and dressing time associated with postoperative wound care.

Proposed framework for enhancing integrity technique using distributed query operation

Database growth and Storage problems are main concern for large and small enterprises nowadays, which significantly influences the efficiency of database applications. Database archiving is one of the solutions available for management. Many issues have been identified as a result of the use of archive databases, including the elimination of inactive data from online transaction processing systems (OLTP), performance and integrity management. The aim of this paper is to propose a framework for storing OLTP and archiving data in a distributed database environment as part of an integrated system. Maintaining the integrity between an OLTP database and an archiving database is crucial, but even more critical is the performance of the required query. The main aspect of the proposed framework is that it will not only ensure data integrity for primary and unique keys between OLTP and archive database, but it will also improve query performance by introducing parallel processing and query execution plans to maintain that integrity.

DDBJ update in 2023: the MetaboBank for metabolomics data and associated metadata.

The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) provides database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), DDBJ accepts and distributes nucleotide sequence data as well as their study and sample information along with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute (EBI). Besides INSDC databases, the DDBJ Center provides databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank) and human genetic and phenotypic data (JGA: Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics (NIG) supercomputer, which is also open for domestic life science researchers to analyze large-scale sequence data. This paper reports recent updates on the archival databases and the services of the DDBJ Center, highlighting the newly redesigned MetaboBank. MetaboBank uses BioProject and BioSample in its metadata description making it suitable for multi-omics large studies. Its collaboration with MetaboLights at EBI brings synergy in locating and reusing public data.

Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data.

DNA Data Bank of Japan (DDBJ) update report 2022.

The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) maintains database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), our primary mission is to collect and distribute nucleotide sequence data, as well as their study and sample information, in collaboration with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute. In addition to INSDC resources, the Center operates databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank), and human genetic and phenotypic data (JGA: Japanese Genotype-Phenotype Archive). These databases are built on the supercomputer of the National Institute of Genetics, whose remaining computational capacity is actively utilized by domestic researchers for large-scale biological data analyses. Here, we report our recent updates and the activities of our services.

Implementation of EDGE Computing Platform in Feeder Terminal Unit for Smart Applications in Distribution Networks with Distributed Renewable Energies

Under the plan of net-zero carbon emissions in 2050, the high penetration of distributed renewable energies in distribution networks will cause the operation of more complicated distribution networks. The development of edge computing platforms will help the operator to monitor and compute the system status timely and locally, and it can ensure the security operation of the system. In this paper, a novel EDGE computing platform that is implemented by a graphics processing unit in the existing feeder terminal unit (FTU) is proposed for smart applications in distribution networks with distributed renewable energies and loads. This platform makes timely forecasts of the feeder status for the next seven days in accordance with historical weather, sun, and loading data. The forecast solver uses the machine learning long short-term memory (LSTM) method. Thereafter, the power calculation analyzers transform feeder topology into the circuit model for transient-state, steady-state, and symmetrical component analyses. An important-factor explainer parses the LSTM model into the concise value of each historical datum. All information transports to remote devices via the internet for the real-time monitor feature. The software stack of the EDGE platform consists of the database archive file system, time-series forecast solver, power flow analyzers, important-factor explainer, and message queuing telemetry transport (MQTT) protocol communication. All open-source software packages, such as SQLite, LSTM, ngspyce, Shapley Additive Explanations, and Paho-MQTT, form the aforementioned function. The developed EDGE forecast and power flow computing platform are helpful for achieving FTU becoming an Internet of Things component for smart operation in active distribution networks.

Remote Data Auditing in a Cloud Computing Environment

In the current paradigms of information technology, cloud computing is the most essential kind of computer service. It satisfies the need for high-volume customers, flexible computing capabilities for a range of applications like as database archiving and business analytics, and the requirement for extra computer resources to provide a financial value for cloud providers. The purpose of this investigation is to assess the viability of doing data audits remotely inside a cloud computing setting. There includes discussion of the theory behind cloud computing and distributed storage systems, as well as the method of remote data auditing. In this research, it is mentioned to safeguard the data that is outsourced and stored in cloud servers. There are four different techniques of remote data auditing procedures that are presented here for distributed cloud services. There are several difficulties associated with data audit methods; however, these difficulties may be overcome by using a variety of techniques, such as the Boneh-Lynn-Shacham signature or the automated blocker protocol. In addition to that, other difficulties associated with distributed-based remote data auditing solutions are discussed. In addition, a variety of approaches might be researched further for further examination in order to find answers to these impending problems.

ГЕНЕЗИС СУДОВОЇ ЕКОНОМІЧНОЇ ЕКСПЕРТИЗИ

The purpose of the study is to reconstruct the main stages of the genesis of forensic economic expertise and determine the future prospects of its development. To achieve the goal, two tasks have been allocated. The first task is the definition of scientists and their concept of the development of forensic expertise in the economy during the historical period of its establishment. The second task concerns the classification of types of forensic economic expertise in different periods of time and the analysis of the argumentation of its development according to the main stages. In the article, the author analyzed the relationship between forensic expertise and the development of accounting. It was established that the first stage of its appearance as a separate type of activity is the period of expertise, which is essentially related to the control function and has the features of an audit. It has been established that scientists associate the second stage (seventeenth century) with the period of creation of special institutes of expert accountants, whose specialists conducted expertise at the request of judicial authorities. The third stage of the development of forensic expertise is characterized by researchers with the processes of distinguishing audit methods and methods of forensic accounting expertise in practice. The results of the reasoned testimony of scientists made it possible to form the fourth stage of the genesis of forensic expertise, when it became an organizationally independent branch of knowledge and was provided with methodological developments for the work of forensic experts. The scientific result of the study represents the establishment of the prerequisites for the further transformation of forensic economic expertise taking into account the global digitalization of the economy and the beginning of the fifth stage of the development of forensic economic expertise. The author provided the characteristics of modern economic relationships based on digital technologies, which will significantly affect the methodical support of the expertise for judicial proceedings. The author attributes to such features: the introduction of IT technologies as an intermediary between the provider and the consumer of services, the modification of documentary confirmation of the facts of economic transactions to virtual transactions with database archives regarding their implementation in cloud technologies. The author made a conclusion about the emergence of new objects of forensic economic expertise. They represent economic products, resources and factors determined by digitized relations. The new circumstances of economic relations in the IT economic environment and objects of forensic economic expertise will become part of the methodological guidelines, according to which the forensic experts will be able to perform expertise. The abovementioned represents the practical significance of the obtained results and the direction of further scientific research. Keywords: forensic economic examination, stages of the genesis of forensic economic examination, tools of forensic economic examination, regulatory regulation of forensic economic examination, retrospective analysis of forensic economic examination, accounting examination

Data from “Ceramic Technology and Cultural Change in Sicily from the 6th to the 11th Century AD.” PhD Thesis

This dataset presents the results of the petrographic and SEM-EDS analysis of 516 ceramic artefacts sampled from four early medieval sites across Sicily. The full database archive as well as the raw data are stored in the University of Sheffield repository. Petrographic descriptions can be found in Testolini’s thesis from page 94 to 142. These data shed new light into the ceramic provenance and technological choices in Sicily in a time of shifting power between Byzantine and Islamic rulers. The Dataset can be reused to identify other ceramic production centres and technological traditions in Sicily across time and space, as well as to challenge cultural labelling of ceramic artefacts.

A Correlation Method for Meteorological Factors and Air pollution in association to covid-19 pandemic in the most affected city in Indonesia

This study aims to validate the correlation between meteorological factors and air pollution with the spread of Covid-19 in Jakarta, Indonesia. This study examined the Covid-19 cases of Jakarta and its five municipalities. The secondary data of Covid-19 cases, includes Daily Positive Cases (DPC) and Total Daily Positive Cases (TDPC), were retrieved from the Health Office of DKI Jakarta Province, while the meteorological and air pollution parameters were obtained from the online database archives. Kendall and Spearman rank correlation tests were used to analyze correlation between DPC and TDPC with meteorological and air pollution parameters. This study found that Air Quality Index and PM10 showed a significant positive correlation with DPC in municipalities of Jakarta. Also, the average air temperature was positively correlated to TDPC in all region of Jakarta. Average air temperature, Air Quality Index, and PM10 were the factors that take into account for the spread of Covid-19 pandemic in Jakarta, Indonesia. The warmer temperature associated to the higher number of case. Thus, there are no indications that the spread of Covid-19 in subtropical or temperate country may decrease when entering a warmer season that resembles the climatic characteristics in tropical region. Additionally, the significance of air pollutant factors implies that reducing air pollution should be promoted as it might reduce the spread of Covid-19. The findings of this study would be useful to support the strategy and policy in preventing the spread of Covid-19 in the country.

Seismic Surveillance and Earthquake Monitoring in Italy

Abstract The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution with focus on earth sciences. Moreover, the INGV is the operational center for seismic surveillance and earthquake monitoring in Italy and is a part of the civil protection system as a center of expertise on seismic, volcanic, and tsunami risks.INGV operates the Italian National Seismic Network and other networks at national scale and is a primary node of the European Integrated Data Archive for archiving and distributing strong-motion and weak-motion seismic recordings. In the control room in Rome, INGV staff performs seismic surveillance and tsunami warning services; in Catania and Naples, the control rooms are devoted to volcanic surveillance. Volcano monitoring includes locating earthquakes in the regions around the Sicilian (Etna, Eolian Islands, and Pantelleria) and the Campanian (Vesuvius, Campi Fregrei, and Ischia) active volcanoes. The tsunami warning is based on earthquake location and magnitude (M) evaluation for moderate to large events in the Mediterranean region and also around the world. The technologists of the institute tuned the data acquisition system to accomplish, in near real time, automatic earthquake detection, hypocenter and magnitude determination, and evaluation of several seismological products (e.g., moment tensors and ShakeMaps). Database archiving of all parametric results is closely linked to the existing procedures of the INGV seismic surveillance environment and surveillance procedures. Earthquake information is routinely revised by the analysts of the Italian seismic bulletin. INGV provides earthquake information to the Department of Civil Protection (Dipartimento di Protezione Civile) to the scientific community and to the public through the web and social media. We aim at illustrating different aspects of earthquake monitoring at INGV: (1) network operations; (2) organizational structure and the hardware and software used; and (3) communication, including recent developments and planned improvements.

Staying away, staying alive: Exploring risk and stigma of COVID-19 in the context of beliefs, actors and hierarchies in India

The article examines the media discourse of risk and stigma which developed in response to the COVID-19 pandemic in India, employing the theoretical frameworks of Mary Douglas and Erving Goffman. Accessing the Factiva database archive, the authors analysed a total of 139 stigma-linked media reports, using the Discourse Network Analyzer (DNA) to identify thematic groups of beliefs and related actors contributing to the risk discourse on the contagion. The results exhibit a clear difference in opinion on various stigma-related beliefs among the individuals diagnosed or assumed susceptible to COVID-19, including the issue of disclosing identities. In India, domestic actors have dominated the media discourse, particularly national government agencies, rather than intergovernmental organisations or foreign governments. The media content analysis in this article shows that new hierarchies have emerged based on confirmed or suspected contact with the disease along with reinforcement of traditional myths and superstitions, leading to discrimination against the quarantined individuals, their families, healthcare staff and socially marginalised communities.

Myositis-specific and myositis-associated autoantibodies in a large Indian cohort of inflammatory myositis

BackgroundThe Idiopathic Inflammatory Myositis (IIM) are heterogenous with distinct clinical phenotypes associated with specific myositis specific antibodies (MSA) and myositis associated antibodies (MAA). ObjectivesTo evaluate the frequency, pattern and associations of MSA/MAA in a large Indian cohort of IIM. MethodsAdult and juvenile IIM (2017 ACR/EULAR criteria), were recruited in the MyoCite cohort between 2017and 2020 at a tertiary center in Northern India. Standardized clinical and laboratory variables were extracted from the database archive. Serum samples were evaluated for the presence of MSAs/MAAs by Line immunoassay and anti-nuclear antibodies (ANA) by Immunofluorescence assay (IFA). The prevalence and clinical associations of different MSA/MAAs were assessed. ResultsMSA and MAAs were tested in 250 IIM patients (214 adults, 36 children) of age [40 (30–49), 13 (7.5–16) years] and disease duration [ 7 (3–17), 6 (2–17) months] comprising predominantly of Dermatomyositis (DM) followed by Overlap myositis (OM). MSAs/MAAs were found in 148 (59.2%, 60.7% adults and 50% JIIM), of which two-thirds were MSA (95, 64% overall). Two cases (0.8%) had more than one MSA. In adult IIM, the most common MSA was anti-Jo-1 (10%), whereas it was anti-MDA5 and anti-NXP2 4 (11%) each in Juvenile IIM (JIIM). 76.0% (172/226) were ANA positive, with speckled pattern being the most common (37%,). Nearly two-thirds (54, 61%) of those with negative ANA had MSA/ MAA. Nearly half (18/54, 54.6%) had MSA associated with cytoplasmic patterns. ARS (anti-aminoacyl-tRNA synthetase) were associated with mechanic's hands (OR-7.06), ILD (OR-4.4), and arthritis (OR-2.23). Clinical associations of anti-Jo-1 and non-Jo-1 Anti synthetase syndrome (ASS) did not differ. Anti-MDA-5 associated with oral ulcers (OR-8.3), fever (OR-8.6) and weight loss (OR-7.35) in adults, and arthritis (OR-11.5), and periungual rash (OR-9.6) in children. Anti-TIF-1γ associated with photosensitivity (OR-10.44) and malignancy (OR-34) in adults, and cuticular overgrowth (OR-11.2) in children. ConclusionMyositis autoantibodies are seen in two-thirds IIMs and are associated with distinct clinical subsets. Jo-1 and non-Jo-1 ASS exhibit similar characteristics. The association of anti-TIF1 γ with malignancy was confirmed in adults. MSA/MAA were present in two-thirds of those with negative ANA and MSA were nearly always mutually exclusive.

Some Aspects of a Client-Server Architecture System for Processing Radar Images

This article discusses some aspects of a client-server architecture system designed to process radar images. It is assumed that data obtained remotely are processed to determine oil pollution on the water surface. The synthesis of monochrome images and the infological model of the system are considered. The developed application provides the ability to preview the image, forming a graphic file from satellite data; implements functions that allow you to annotate images, marking areas of interest and adding comments; implements, if necessary, an algorithm for merging monochrome images; implements a keyword support system that allows flexible categorization of all images; provides the necessary level of information security through the separation of user rights and authorization systems. The developed software product allows access to files stored in the GIS database archive in real time simultaneously by a large number of users, i.e. represents its network (web) application. The software product contains three levels: a user interface on a client browser, a web application, and a database server.

A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function.

Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how’ these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).

Impact of cancer in the management of delivery: 10 years of variations

Importance: The active-during-pregnancy-cancer (ADPC) is a condition that complicates the 0.1% of pregnancies. Abortion, preterm delivery and cesarean section (CS) are common attitudes for these patients, because of scarcity of evidence-based studies. Not-active-during-pregnancy-cancer (NADPC) is an increasing medical problem. The fertility of young girls survived to neoplasia is significantly lower compared to general population and there are increased rates of low birth weight and preterm birth.Objective: To analyze the impact that the pregnancy-related neoplastic disease has on management of deliveries in the decade 2006–2015.Material and methods: In this observational study, we collected obstetric and oncological data about 205 patients bearing a history of cancer related to pregnancy between January 2006 and September 2016 from Sant’Anna Hospital database archive in Turin. The entire population was divided in 59 patients with ADPC and 146 patients with NADPC because it was cured before starting the gestation. Three ADPC and three NADPC patients who completed their pregnancy in the year 2016 were excluded from the 10 years 2006–2015 trends realization. All in situ and invasive cancers were considered.Results: In ADPC patients, we registered 3.4% miscarriage and 15.3% iatrogenic abortion. The type of delivery was vaginal (22%) and CS (59.3%). Induction of labor was 14.6%, elective CS was 68.8%: the indication for these procedures was 78.6% oncological. The average gestational age was 35.5 weeks. In NADPC patients, we registered 9.6% miscarriage and 8.2% iatrogenic abortion. The type of delivery was vaginal (43.2%) and CS (39%). Induction of labor was 11.7%, elective CS was 36.7%: the indication for these procedures was 77.5% obstetrical. The average gestational age was 38.3 weeks.Conclusions: Ten-year trends in ADPC and NADPC patients showed an increase of induced deliveries and a decrease in elective CS. We observed not significant reduction of gestational age and birth weight. A contemporary decrease of oncological indications for CS in the two populations was reported.

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.

Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants. Patient data are extracted from DREAM-Phil Bowen ALS Prediction Prize4Life Challenge data, most of which are from the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) archive. We employed model-based and model-free machine-learning methods to predict the change of the ALSFRS score over time. Using training and testing data we quantified and compared the performance of different techniques. We also used unsupervised machine learning methods to cluster the patients into separate computable phenotypes and interpret the derived subcohorts. Direct prediction of univariate clinical outcomes based on model-based (linear models) or model-free (machine learning based techniques - random forest and Bayesian adaptive regression trees) was only moderately successful. The correlation coefficients between clinically observed changes in ALSFRS scores relative to the model-based/model-free predicted counterparts were 0.427(random forest) and 0.545(BART). The reliability of these results were assessed using internal statistical cross validation and well as external data validation. Unsupervised clustering generated very reliable and consistent partitions of the patient cohort into four computable phenotypic subgroups. These clusters were explicated by identifying specific salient clinical features included in the PRO-ACT archive that discriminate between the derived subcohorts. There are differences between alternative analytical methods in forecasting specific clinical phenotypes. Although predicting univariate clinical outcomes may be challenging, our results suggest that modern data science strategies are useful in clustering patients and generating evidence-based ALS hypotheses about complex interactions of multivariate factors. Predicting univariate clinical outcomes using the PRO-ACT data yields only marginal accuracy (about 70%). However, unsupervised clustering of participants into sub-groups generates stable, reliable and consistent (exceeding 95%) computable phenotypes whose explication requires interpretation of multivariate sets of features. HIGHLIGHTS: • Used a large ALS data archive of 8,000 patients consisting of 3 million records, including 200 clinical features tracked over 12 months. • Employed model-based and model-free methods to predict ALSFRS changes over time, cluster patients into cohorts, and derive computable phenotypes. • Research findings include stable, reliable, and consistent (95%) patient stratification into computable phenotypes. However, clinical explication of the results requires interpretation of multivariate information. Graphical Abstract ᅟ.

BTrim

To address the need for extreme OLTP performance on commodity multi-core hardware supporting large amounts of memory, SAP ASE is re-architected to tightly integrate an In-Memory Row Store (IMRS) within the existing database engine. The IMRS is both a store and a caching layer to host "hot" rows in-memory, in a row-oriented format. The IMRS is an extension to the traditional buffer-cache which deals with data in a page-oriented storage format (referred to as the page-store). Data in individual tables marked as IMRS-enabled can be fully memory-resident or can straddle the page store and the IMRS. Cold data in the IMRS is organically identified, harvested, and "packed" back to the page store. SQL statements and transactions can access data transparently from both stores for the same table. All Transact-SQL capabilities and language constructs are supported with no application or stored procedure code changes required. Full durability for in-memory data is provided, including support for backup and restore of database archives and periodic transaction dumps. The high-level system design supporting this architecture, along with experimental results and performance benefits is presented.

The General Data Assimilation Method, its Comparison with the Standard Scheme, and its Application to Dynamical Simulation in the Atlantic

A new data assimilation scheme developed earlier and based on the theory of diffusion stochastic processes and parabolic differential equations is presented and tested. This scheme is applied to the Hybrid Circulation Ocean Model (HYCOM) and altimetry data base Archiving, Validating and Interpolating Satellite Oceanography Data (AVISO) over the Atlantic. Several numerical experiments are conducted and their results are analyzed. It is shown that the method really assimilates data, makes the output oceanic fields closer to observations and, on the other hand, conserves the model integrals and balance. The tested method is also compared with the Ensemble Optimal Interpolation scheme (EnOI) as a counterpart of the standard Kalman filter method and it is shown that the proposed general method has several advantages, in particular, it provides a better forecast and requires less computational consumptions.