Text Strings Research Articles

Creation of Text Vignettes Based on Patient-Reported Data to Facilitate a Better Understanding of the Patient Perspective: Design Study.

Patient-reported outcome (PRO) data refer to information systematically reported by patients, or on behalf of patients, without the influence of health care professionals. It is a focal point of the health care system's ambition toward becoming more involving and personalized. It is recognized that PROs provide valuable data. However, despite this recognition, there are challenges related to both patients' and clinicians' accurate interpretations of the quantitative data. To overcome these challenges, this study explores text vignettes as a representation of PROs. This study aimed to develop data-informed text vignettes based on data from the Readiness and Enablement Index for Health Technology (READHY) instrument as another way of representing PRO data and to examine how these are perceived as understandable and relevant for both patients and clinicians. The text vignettes were created from participant responses to the READHY instrument, which encompasses health literacy, health education, and eHealth literacy. The text vignettes were created from 13 individual text strings, each corresponding to a scale in the READHY instrument. This study consisted of 3 sequential parts. In part 1, individuals with chronic obstructive pulmonary disease completed the READHY instrument, providing data to be used to create vignettes based on cluster profiles from the READHY instrument. Part 2 focused on the development of scale-based strings representing all READHY dimensions, which were evaluated through iterative cognitive interviews. In part 3, clinicians and patients assessed the understanding and relevance of the text vignettes. Clinicians and patients both understood and related to the text vignettes. Patients viewed the text vignettes as an accurate reflection of their PRO responses, and clinicians perceived the text vignettes as aligned with their understanding of patients' experiences. Text vignettes can be developed using PRO instruments, with individual scales as input strings. This provides an opportunity to present numeric values in a text format that is understandable and recognizable to most patients and clinicians. Challenges with the vignette's language and layout require customization and clinician training to ensure meaningful interpretation. Findings also support the need to expand the study and enhance clinical relevance with alternative or contextually relevant text vignettes.

Biparsers: Exact Printing for Data Synchronisation

Parsers and printers are vital for data synchronisation between different serialisation formats. As they are tightly related, much research has been devoted to showing that both can be derived from a single definition. It, however, turns out to be challenging to extend this work with exact-printing, which recovers the original source text for the parsed data. In this paper, we propose a new approach to tackling the challenge that considers a parser-printer pair as a mechanism to synchronize the input text string with the data, and formalizes them as a bidirectional program (lens). We propose the first biparser framework to support exact-printing with non-injective parsers, provide a library of combinators for common patterns, and demonstrate its usefulness with biparsers for subsets of JSON and YAML.

Culture and ICD-11 personality disorder: Implications for clinical practice across diverse ethnic groups.

Personality disorder (PD) in ICD-11 is defined primarily by self and interpersonal dysfunction and optionally by other qualifiers. This definition is inseparable from relativism of cultural determinants. This review aimed to synthesize the relevant aspects of the influence of culture on clinical practice and health management for this condition, aligning them to the ICD-11 PD model. In Scopus, we systematically searched for studies that included the text strings: cultur* | personality AND (disorder* OR patholog*) without any restrictions on publication date or language or other exclusion criteria, up to November 2022. Evidence suggests that cultural variables in ethnic groups (Western and non-Western) such as the individualist/collectivist philosophy, historical/generational trauma, immigration, acculturation, religion, and gender influence the etiology, semiology, epidemiology, evaluation, diagnosis, treatment, and management of health services for ICD-11 PD. We discuss the limitations and propose future lines of research on this topic based on our knowledge and experience. In this review, we provide the scientific community and clinicians with relevant cultural information to guide their practice and propose strategies to manage PD from the ICD-11 model. More research is needed using mixed study methodologies on stigma, the experiences of patients, clinicians, and health agencies, to reduce the care gaps and achieve a culturally comprehensive, inclusive, and competent use of this new model.

Assessing the use of unstructured electronic health record data to identify exposure to firearm violence.

Research on firearm violence is largely limited to people who experienced acute bodily trauma and death which is readily gathered from Inpatient and Emergency Department settings and mortality data. Exposures to firearm violence, such as witnessing firearm violence or losing a loved one to firearm violence, are not routinely collected in health care. As a result, the true public health burden of firearm violence is underestimated. Clinical notes from electronic health records (EHRs) are a promising source of data that may expand our understanding of the impact of firearm violence on health. Pilot work was conducted on a sample of clinical notes to assess how firearm terms present in unstructured clinical notes as part of a larger initiative to develop a natural language processing (NLP) model to identify firearm exposure and injury in ambulatory care data. We used EHR data from 2012 to 2022 from a large multistate network of primary care and behavioral health clinics. A text string search of broad, gun-only, and shooting terms was applied to 9,598 patients with either/both an ICD-10 or an OCHIN-developed structured data field indicating exposure to firearm violence. A sample of clinical notes from 90 patients was reviewed to ascertain the meaning of terms. Among the 90 clinical patient notes, 13 (14%) notes reflect documentation of exposure to firearm violence or injury from firearms. Results from this study identified refinements that should be considered for NLP text classification. Unstructured clinical notes from primary and behavioral health clinics have potential to expand understanding of firearm violence.

BracketMaker: Visualization and optimization of chemical protein synthesis.

Chemical protein synthesis (CPS), in which custom peptide segments of ~20-60 aa are produced by solid-phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a "bracket" style diagram detailing the starting segments and the order of all ligation, desulfurization, and/or deprotection steps to obtain the product protein. Brackets are invaluable tools for comparing multiple possible synthetic approaches and serve as blueprints throughout a synthesis. Drawing CPS brackets by hand or in standard graphics software, however, is a painstaking and error-prone process. Furthermore, the CPS field lacks a standard bracket format, making side-by-side comparisons difficult. To address these problems, we developed BracketMaker, an open-source Python program with built-in graphic user interface (GUI) for the rapid creation and analysis of CPS brackets. BracketMaker contains a custom graphics engine which converts a text string (a protein sequence annotated with reaction steps, introduced herein as a standardized format for brackets) into a high-quality vector or PNG image. To aid with new syntheses, BracketMaker's "AutoBracket" tool automatically performs retrosynthetic analysis on a set of segments to draft and rank all possible ligation orders using standard native chemical ligation, protection, and desulfurization techniques. AutoBracket, in conjunction with an improved version of our previously reported Automated Ligator (Aligator) program, provides a pipeline to rapidly develop synthesis plans for a given protein sequence. We demonstrate the application of both programs to develop a blueprint for 65 proteins of the minimal Escherichia coli ribosome.

Visual place recognition from end-to-end semantic scene text features.

We live in a visual world where text cues are abundant in urban environments. The premise for our work is for robots to capitalize on these text features for visual place recognition. A new technique is introduced that uses an end-to-end scene text detection and recognition technique to improve robot localization and mapping through Visual Place Recognition (VPR). This technique addresses several challenges such as arbitrary shaped text, illumination variation, and occlusion. The proposed model captures text strings and associated bounding boxes specifically designed for VPR tasks. The primary contribution of this work is the utilization of an end-to-end scene text spotting framework that can effectively capture irregular and occluded text in diverse environments. We conduct experimental evaluations on the Self-Collected TextPlace (SCTP) benchmark dataset, and our approach outperforms state-of-the-art methods in terms of precision and recall, which validates the effectiveness and potential of our proposed approach for VPR.

Mining of Data in Real Time to build an Interactive Research Analytics Dashboard

The paper discusses the process of building a web-based interactive platform to assess the research productivity of the individual faculty of an institution. The application harvests the bibliographic data of researchers’ publications from the Scopus database and visualises it in real-time. The open-source environment R with different packages like the shiny, shiny dashboard for building web apps and dashboard designing; httr for data harvesting through API, jsonlite for parsing JSON data, tm for text mining, wordcloud for visualising wordcloud, textstem for lemmatization of text strings, dplyr for data manipulation, etc. were used. The data was harvested at the backend using Scopus API. The user interface of the application’s front end is the default design of the shiny dashboard, with some limited customization using CSS. The system tracks and visualizes the research productivity of the faculty. The application provides the total number of publications, citations, H Index, open access information, number of publications in open access, and closed access, in an assessment year rather than a calendar year, source-wise differentiation of publications, annual distribution of publications, common themes of the research, etc.

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites. The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

Obfuscated Computer Malware Classification Based on Significant Opcode

Computer malware has greatly impacted the computer network securities and even personal computer users. Signature-based detection is incapable to recognize the obfuscated computer malware since it is being covered by the obfuscation techniques. Therefore, machine learning is being explored and equipped in the malware detection to withstand the threaten of malware. In fact, there are many features available, i.e., text string to be implemented for malware classification. Nevertheless, opcode could be one of the features owing to its relative smaller data size compared to the text string. In this research, the significant opcodes of executable malware files which referring to the prevalent content from malware-to-malware generation are extracted as training dataset. Several machine learning classifiers are generated and compared in terms of classification accuracy and speed, as well as the comparison is done with text string-based detection and signature-based detection. From the finding, it is shown that machine learning detection performs more than 2 times better than signature based and machine learning generated based-on significant opcode features is able to detect obfuscated malware over 10 times faster than text string feature and still achieve up to 98% of accuracy.

Linear time online algorithms for constructing linear-size suffix trie

The suffix trees are fundamental data structures for various kinds of string processing. The suffix tree of a text string T of length n has O(n) nodes and edges, and the string label of each edge is encoded by a pair of positions in T. Thus, even after the tree is built, the input string T needs to be kept stored and random access to T is still needed. The linear-size suffix tries (LSTs), proposed by Crochemore et al. [Linear-size suffix tries, TCS 638:171-178, 2016], are a “stand-alone” alternative to the suffix trees. Namely, the LST of an input text string T of length n occupies O(n) total space, and supports pattern matching and other tasks with the same efficiency as the suffix tree without the need to store the input text string T. Crochemore et al. proposed an offline algorithm which transforms the suffix tree of T into the LST of T in O(nlog⁡σ) time and O(n) space, where σ is the alphabet size. In this paper, we present two types of online algorithms which “directly” construct the LST, from right to left, and from left to right, without constructing the suffix tree as an intermediate structure. Both algorithms construct the LST incrementally when a new symbol is read, and do not access the previously read symbols. Both of the right-to-left construction algorithm and the left-to-right construction algorithm work in O(nlog⁡σ) time and O(n) space. The main feature of our algorithms is that the input text string does not need to be stored.

Use of Generative AI to Identify Helmet Status Among Patients With Micromobility-Related Injuries From Unstructured Clinical Notes

Large language models (LLMs) have potential to increase the efficiency of information extraction from unstructured clinical notes in electronic medical records. To assess the utility and reliability of an LLM, ChatGPT-4 (OpenAI), to analyze clinical narratives and identify helmet use status of patients injured in micromobility-related accidents. This cross-sectional study used publicly available, deidentified 2019 to 2022 data from the US Consumer Product Safety Commission's National Electronic Injury Surveillance System, a nationally representative stratified probability sample of 96 hospitals in the US. Unweighted estimates of e-bike, bicycle, hoverboard, and powered scooter-related injuries that resulted in an emergency department visit were used. Statistical analysis was performed from November 2023 to April 2024. Patient helmet status (wearing vs not wearing vs unknown) was extracted from clinical narratives using (1) a text string search using researcher-generated text strings and (2) the LLM by prompting the system with low-, intermediate-, and high-detail prompts. The level of agreement between the 2 approaches across all 3 prompts was analyzed using Cohen κ test statistics. Fleiss κ was calculated to measure the test-retest reliability of the high-detail prompt across 5 new chat sessions and days. Performance statistics were calculated by comparing results from the high-detail prompt to classifications of helmet status generated by researchers reading the clinical notes (ie, a criterion standard review). Among 54 569 clinical notes, moderate (Cohen κ = 0.74 [95% CI, 0.73-0.75) and weak (Cohen κ = 0.53 [95% CI, 0.52-0.54]) agreement were found between the text string-search approach and the LLM for the low- and intermediate-detail prompts, respectively. The high-detail prompt had almost perfect agreement (κ = 1.00 [95% CI, 1.00-1.00]) but required the greatest amount of time to complete. The LLM did not perfectly replicate its analyses across new sessions and days (Fleiss κ = 0.91 across 5 trials; P < .001). The LLM often hallucinated and was consistent in replicating its hallucinations. It also showed high validity compared with the criterion standard (n = 400; κ = 0.98 [95% CI, 0.96-1.00]). This study's findings suggest that although there are efficiency gains for using the LLM to extract information from clinical notes, the inadequate reliability compared with a text string-search approach, hallucinations, and inconsistent performance significantly hinder the potential of the currently available LLM.

Do Language Models’ Words Refer?

Abstract What do language models (LMs) do with language? They can produce sequences of (mostly) coherent strings closely resembling English. But do those sentences mean something, or are LMs simply babbling in a convincing simulacrum of language use? We address one aspect of this broad question: whether LMs’ words can refer, that is, achieve “word-to-world” connections. There is prima facie reason to think they do not, since LMs do not interact with the world in the way that ordinary language users do. Drawing on the externalist tradition in philosophy of language, we argue that those appearances are misleading: Even if the inputs to LMs are simply strings of text, they are strings of text with natural histories, and that may suffice for LMs’ words to refer.

Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain

BackgroundSystematically screening published literature to determine the relevant publications to synthesize in a review is a time-consuming and difficult task. Large language models (LLMs) are an emerging technology with promising capabilities for the automation of language-related tasks that may be useful for such a purpose.MethodsLLMs were used as part of an automated system to evaluate the relevance of publications to a certain topic based on defined criteria and based on the title and abstract of each publication. A Python script was created to generate structured prompts consisting of text strings for instruction, title, abstract, and relevant criteria to be provided to an LLM. The relevance of a publication was evaluated by the LLM on a Likert scale (low relevance to high relevance). By specifying a threshold, different classifiers for inclusion/exclusion of publications could then be defined. The approach was used with four different openly available LLMs on ten published data sets of biomedical literature reviews and on a newly human-created data set for a hypothetical new systematic literature review.ResultsThe performance of the classifiers varied depending on the LLM being used and on the data set analyzed. Regarding sensitivity/specificity, the classifiers yielded 94.48%/31.78% for the FlanT5 model, 97.58%/19.12% for the OpenHermes-NeuralChat model, 81.93%/75.19% for the Mixtral model and 97.58%/38.34% for the Platypus 2 model on the ten published data sets. The same classifiers yielded 100% sensitivity at a specificity of 12.58%, 4.54%, 62.47%, and 24.74% on the newly created data set. Changing the standard settings of the approach (minor adaption of instruction prompt and/or changing the range of the Likert scale from 1–5 to 1–10) had a considerable impact on the performance.ConclusionsLLMs can be used to evaluate the relevance of scientific publications to a certain review topic and classifiers based on such an approach show some promising results. To date, little is known about how well such systems would perform if used prospectively when conducting systematic literature reviews and what further implications this might have. However, it is likely that in the future researchers will increasingly use LLMs for evaluating and classifying scientific publications.

FIRE 6.5: Feynman integral reduction with new simplification library

FIRE is a program which performs integration-by-parts (IBP) reductions of Feynman integrals. Originally, the C++ version of FIRE relies on the computer algebra system Fermat by Robert Lewis to simplify rational functions. We present an upgrade of FIRE which incorporates a new library FUEL initially described in a separate publication, which enables a flexible choice of third-party computer algebra systems as simplifiers, as well as efficient communications with some of the simplifiers as C++ libraries rather than through Unix pipes. We achieve significant speedups for IBP reductions of Feynman integrals involving many kinematic variables, when using an open source backend based on FLINT newly added in this work, or the Symbolica backend developed by Ben Ruijl as a potential successor of FORM. Program summaryProgram title: FIRE, version 6.5 (FIRE 6.5)CPC Library link to program files:https://doi.org/10.17632/cy6k69pb3y.2Developer's repository link:https://gitlab.com/feynmanintegrals/fire.gitLicensing provisions: GPLv2Programming language:Wolfram Mathematica 8.0 or higher, C++17Supplementary material: See linked repository for installation instructions.Journal reference of previous version: Comput. Phys. Commun. 247 (2020) 106877Does the new version supersede the previous version?: Yes.Reasons for the new version: The new version no longer relies on a single computer algebra system, Fermat [1], but instead allows a flexible choice of several systems, some of which offer higher performance, especially when the number of variables is large.Summary of revisions: A new library FUEL [2] is used as a core component of the new version of FIRE to access different computer algebra systems as simplifiers of rational function expressions. Since the first release of FUEL described elsewhere, FUEL version 1.0 here has been enhanced with a new backend based on the open source library FLINT [3] that provides highly performant simplification of rational functions.Nature of problem: Feynman integrals of a given family are reduced to a finite set of master integrals, by solving linear equations arising from integration by parts, using Gaussian elimination. The coefficients of the linear equations are generally rational functions in kinematic variables and the spacetime dimension, and the simplification of such rational functions during Gaussian elimination is a key task that is improved in this upgrade of FIRE.Solution method: Computer algebra systems with state-of-the-art capabilities for polynomial GCD computations are used as simplification backends, or simplifiers in short. Due to the design of FIRE, text strings are used as the exchange format for rational functions before and after simplification. A fast C++ parser is written to parse strings into the internal format of an external simplifer, FLINT [3], with state-of-the-art performance for multivariate polynomial calculations. Similarly, the simplifier Symbolica [4] has high performance in both GCD computations and parsing, and has been integrated into FIRE.

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

Heat shock protein (HSP) and its correlation to cocaine-related death: a systematic review.

Investigating deaths related to chronic cocaine abuse can be a difficult task, particularly when they occur suddenly and without explanation. Cocaine abuse can trigger biological effects similar to physiological stressors, causing the body to produce heat-shock proteins (HSPs). However, there is still limited information on the specific levels of each HSP type. This systematic review aims to comprehensively collect and analyze all existing literature data regar-ding the relationship between HSPs and cocaine abuse to investigate whether HSPs can be utilized as forensic markers for accurately dia-gnosing cocaine-related deaths. The Authors conducted the literature search using PubMed and Scopus databases, searching for articles published between 1 January 1992 and 1 April 2024 using the text string: "heat shock protein" AND "cocaine". Twenty articles were collected, but only nine were included in the systematic review. The data gathered pertained to both human and murine species. The majority of the analyzed articles revealed an elevation in HSP25, HSP27, HSP60, HSP70, HSP72, and HSP73 levels in the brain, cerebellum, and liver, indicating cocaine-induced stress. The relationship between HSP and cocaine has been unclear over time. However, recent studies have shown that cocaine consumption leads to an increase in HSP levels, particularly in the central nervous system. This correlation can also be observed in certain types of liver cells that are capable of binding cocaine metabolites. In conclusion, HSP brain levels, along with other biomarkers, may be used to diagnose sudden, unexpected death related to cocaine abuse.

Detecting phishing websites through improving convolutional neural networks with Self-Attention mechanism

Emerging technologies have made internet connection a vital activity facilitating access to many services. However, internet connection raises many security concerns, such as illegally acquiring private information, passwords, and identifiers. Phishing websites are the first choice for attackers that try to have users' private space. Social engineering attacks are performed by designing fake websites similar to real ones and inviting the victim to access those websites to collect their sensitive information and then redirect them to the actual site. Due to the importance of detecting phishing websites, building a robust detector that filters them and blocks their activity on the Internet is necessary. In this paper, we proposed a phishing website detector based on improving the convolutional neural network (CNN) with a self-attention mechanism. The proposed detector collects phishing Uniform Resource Locators (URLs) by treating them as strings. CNN models have proved their efficiency when dealing with text strings compared to Long Short-Term Memory (LSTM) which focuses on temporal features. Using CNN allows learning comprehensive features of the URLs and facilitates the detection of phishing ones. The self-attention mechanism was added to enhance the model's focus and detection accuracy. Besides, the training dataset was balanced by generating phishing URLs using a Generative Adversarial Network (GAN). A set of experiments has proved the robustness of the proposed detector by achieving high detection accuracy on the test set. Besides, the proposed detector was tested using unknown URLs and achieved excellent results. The improved CNN's detection precision of 99.7 is higher than the regular CNN model by 2.74%. The reported results show that using the self-attention mechanism has improved the detection accuracy and made the CNN model more efficient for detecting phishing websites.

Information Technology Intelligent Search of Content in E-commerce Systems

The article describes the process of developing intelligent search technology for content for the implementation of the module of e-commerce systems for forming a list of recommendations for regular users. Intelligent search of content is based on methods of linguistic analysis, modern algorithms for parsing and finding words, and recommendations based on user preferences. The main components of such a search are the parsing of text strings, the selection of keywords, the spelling check, the recognition of common abbreviations and acronyms, the semantic analysis of the text, the search by relevance with the extraction of synonyms, filters and sorting. A web application based on Java and Elasticsearch was developed with the implementation of a recommender system based on a collaborative filtering algorithm. The purpose of the work is to develop the technology of intelligent product search with the formation of a list of recommendations for the user. The object of the research is the processes of intelligent search with the possibility of generating recommendations for users in the field of any e-commerce without reference to the categorization of goods/services, etc. The subject of research is the methods and means of intelligent search of recommender systems based on the Collaborative Filtering algorithm for the formation of product recommendations for users, which is oriented on general coincidences of the choices of similar users. During the experimental testing of the developed system, a number of search queries were conducted with and without the NLP algorithm, the results of which demonstrated an improvement in system performance within the range of 15–95 % depending on the keyword and the presence/absence of errors in the search words. A comparison of the speed of execution of requests with already existing systems was also carried out. Yes, the amount of data in the storage may differ (error when comparing 60–70 ms). For example, a query that consists of 1 or 2 words will be found much faster by 20–70 ms compared to its counterparts. But for 3 and more, results are about the same – 9–20 ms faster.

MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal-Organic Frameworks.

The remarkable versatility of metal-organic frameworks (MOFs) stems from their rich chemical information, leading to numerous successful applications. However, identifying optimal MOFs for specific tasks necessitates a thorough assessment of their chemical attributes. Conventional machine learning approaches for MOF prediction have relied on intricate chemical and structural details, hampering rapid evaluations. Drawing inspiration from recent advancements exemplified by Snurr et al., wherein a text string was used to represent a MOF (MOFid), we introduce a MOFid-aided deep learning model, named the MOF-GRU model. This model, founded on natural language processing principles and utilizing the gated recurrent unit architecture, leverages the serialized text string representation of metal-organic frameworks (MOFs) to forecast gas separation performance. Through a focused study on CH4/N2 separation, we substantiate the efficacy of this approach. Comparative assessments against traditional machine learning techniques underscore our model's superior predictive accuracy and its capacity to handle extensive data sets adeptly. The MOF-GRU model remarkably uncovers latent structure-performance relationships with only MOF sequences, obviating the necessity for intricate three-dimensional (3D) structural information. Overall, this model's judicious design empowers efficient data utilization, thereby hastening the discovery of high-performance materials tailored for gas separation applications.

Anti-Hypertensive Drugs Special References with Their Classification of Calcium Channel Blocker

Objective: Determine the distribution and longitudinal changes in antihypertensive medication regimens in the Systolic Blood Pressure Intervention Trial (SPRINT). Methods: We used antihypertensive medication data collected by pill bottle review at each visit to categorize antihypertensive regimens by medication class. Free text string variables of medication names were independently reviewed by two clinical pharmacists to create standardized generic medication names and classes. Results: Figure 1 illustrates longitudinal changes in class combinations and number of drugs at the randomization, 6, 12, and 18-month visits. Fifty-six percent of participants modified their initial regimen by the 6-month visits; 43% of participants made additional modifications to their regimens from the 6-month to the 18-month visit. The most common initial regimens, and least likely regimens to be changed over time, were combinations with an ACEI/ARB and diuretics ± other classes (42% of initial regimens). Participants in the intensive arm added a mean (standard deviation) of 0.6 (0.9) medications to their initial regimens in the first 18-months compared to -0.1 (0.9) in the standard arm. Conclusion: Intensive blood pressure treatment requires more medication complexity in terms of class and dose. Further study of distinct regimens may reveal if certain class and dose combinations provide better SBP control, safety, or patient satisfaction.