Dictionary

Computing k-mer frequencies in a collection of reads is a common procedure in many genomic applications. Several state-of-the-art k-mer counters rely on hash tables to carry out this task but they are often optimised for small k as a hash table keeping keys explicitly (i.e., k-mer sequences) takes <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>N</mml:mi> <mml:mfrac><mml:mi>k</mml:mi> <mml:mi>w</mml:mi></mml:mfrac> <mml:mo>)</mml:mo></mml:mrow> </mml:math> computer words, N being the number of distinct k-mers and w the computer word size, which is impractical for long values of k...

PURPOSE: Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac. METHODS: MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching...

Short texts generated by individuals in online environments can provide social and behavioral scientists with rich insights into these individuals' internal states. Trained manual coders can reliably interpret expressions of such internal states in text. However, manual coding imposes restrictions on the number of texts that can be analyzed, limiting our ability to extract insights from large-scale textual data. We evaluate the performance of several automatic text analysis methods in approximating trained human coders' evaluations across four coding tasks encompassing expressions of motives, norms, emotions, and stances...

Data-driven approximations of the Koopman operator are promising for predicting the time evolution of systems characterized by complex dynamics. Among these methods, the approach known as extended dynamic mode decomposition with dictionary learning (EDMD-DL) has garnered significant attention. Here, we present a modification of EDMD-DL that concurrently determines both the dictionary of observables and the corresponding approximation of the Koopman operator. This innovation leverages automatic differentiation to facilitate gradient descent computations through the pseudoinverse...

BACKGROUND: This study analyzed the bleeding adverse events (AEs) resulting from the treatment of B-cell lymphoma with Bruton tyrosine kinase (BTK) inhibitors, according to reports in the US Food and Drug Administration's Adverse Event Reporting System (FAERS). METHODS: Bleeding AEs associated with BTK inhibitors (including ibrutinib, zanubrutinib, and acalabrutinib) from the first quarter of 2013 to the third quarter of 2023 were extracted. Reporting odds ratio (ROR) and proportional reporting ratio (PRR) were reported...

The anatomical terms plexus chor(i)oideus (CP) and tela chor(i)oidea (TC) are listed without explanations in the official nomenclature handbooks Terminologia Neuroanatomica and Nomina Anatomica Veterinaria. Definitions of CP and TC exhibit discrepancies in medical dictionaries and anatomy handbooks. The aim of our study was to analyse this problem in detail and to discuss a possible unified use of the terms in science and teaching. We conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, identifying and analysing relevant scholarly articles...

Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology. Science textbooks, science dictionaries, and science institutions often state that scientists follow, and should follow, the universal scientific method of testing hypotheses using observation and experimentation. Yet, scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study...

OBJECTIVE: Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with future suicide events. These are often captured in narrative clinical notes in electronic health records (EHRs). Collaboratively, Weill Cornell Medicine (WCM), Northwestern Medicine (NM), and the University of Florida (UF) developed and validated deep learning (DL)-based natural language processing (NLP) tools to detect PSH and FSH from such notes...

Single-cell transcriptomics, in conjunction with genetic and compound perturbations, offers a robust approach for exploring cellular behaviors in diverse contexts. Such experiments allow un-covering cell-state-specific responses to perturbations, a crucial aspect in unraveling the intricate molecular mechanisms governing cellular behavior and potentially discovering novel regulatory pathways and therapeutic targets. However, prevailing computational methods predominantly focus on predicting average cellular responses, disregarding the inherent response heterogeneity associated with cell state diversity...

BACKGROUND: Fall prevention is crucial for older adults. Enhanced fall risk perception can encourage older adults to participate in fall prevention programs. However, there is still no unified definition of the concept of fall risk perception. OBJECTIVE: To explore the concept of fall risk perception in older adults. DESIGN: A concept analysis. DATA SOURCES: The literature was searched using online databases including PubMed, Cochrane Library, Embase, CINAHL Complete, PsycINFO, Web of Science, China National Knowledge Infrastructure, WangFang and SinoMed...

BACKGROUND AND OBJECTIVES: Clozapine shows higher efficacy against treatment-resistant schizophrenia than other antipsychotics. This study aimed to investigate whether clozapine is associated with the risk of non-hematological malignant tumors, utilizing the US Food and Drug Administration (FDA) Adverse Event Report System (FAERS) database. METHODS: The records from the first quarter of 2004 to the third quarter of 2012 were used for disproportionality analysis, and patients who developed non-hematological malignant tumors were identified by the Standardized Medical Dictionary for Regulatory Activities Queries (SMQ)...

With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information. However, due to the use of natural language by physicians when composing these records, the effectiveness of traditional methods such as dictionaries, rule matching, and machine learning in the extraction of information from these unstructured texts falls short of clinical standards...

Traumatic brain injury (TBI) is a significant source of disability in the United States and around the world and may lead to long-lasting cognitive deficits and a decreased quality of life for patients across injury severities. Following the primary injury phase, TBI is characterized by complex secondary cascades that involve altered homeostasis and metabolism, faulty signaling, neuroinflammation, and lipid dysfunction. The objectives of the present study were to (1) assess potential correlations between lipidome and cytokine changes after closed-head mild TBI (mTBI), and (2) examine the reproducibility of our acute lipidomic profiles following TBI...

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy...

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The purpose of this study was to consider the core of the mental lexicon of the Kazakh language based on the analysis of associative dictionaries, to determine the basic lexico-semantic groups of words and to compare the basic lexical layer with value categories. This study uses the following methods of linguocultural, comparative, lexico-semantic, and conceptual analysis, as well as analytical and synthetic analysis, with the help of which the main conceptual values of the Kazakh people were studied: spiritual, mental and material...

Anatomy, the study of human structure, is foundational to medicine. Its language has a long history, with contributions from authors hailing from diverse cultures and countries, adhering to various scientific traditions, speaking different languages, and practicing medicine across a wide gamut of specialties. The resultant disparity in terms provides challenges both for students in learning and for interdisciplinary communication. We report here on a user-friendly look-up web site, "AnatomicalTerms.info" that links a Terminologica Anatomica term to alternative terms in usage: synonyms, polysemes, eponyms, homonyms, and terms in other languages...

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training...

As social media booms, abusive online practices such as hate speech have unfortunately increased as well. As letters are often repeated in words used to construct social media messages, these types of words should be eliminated or reduced in number to enhance the efficacy of hate speech detection. Although multiple models have attempted to normalize out-of-vocabulary (OOV) words with repeated letters, they often fail to determine whether the in-vocabulary (IV) replacement words are correct or incorrect. Therefore, this study developed an improved model for normalizing OOV words with repeated letters by replacing them with correct in-vocabulary (IV) replacement words...

BACKGROUND: Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. OBJECTIVE: This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security...