Most current models of knowledge organization are based on hierarchical or taxonomic categories (animals, tools). Another important organizational pattern is thematic categorization, i.e. categories held together by external relations, a unifying scene or event (car and garage). The goal of this study was to compare the neural correlates of these categories under automatic processing conditions that minimize strategic influences. We used fMRI to examine neural correlates of semantic priming for category members with a short stimulus onset asynchrony (SOA) of 200 ms as subjects performed a lexical decision task. Four experimental conditions were compared: thematically related words (car-garage); taxonomically related (car-bus); unrelated (car-spoon); non-word trials (car-derf). We found faster reaction times for related than for unrelated prime-target pairs for both thematic and taxonomic categories. However, the size of the thematic priming effect was greater than that of the taxonomic. The imaging data showed signal changes for the taxonomic priming effects in the right precuneus, postcentral gyrus, middle frontal and superior frontal gyri and thematic priming effects in the right middle frontal gyrus and anterior cingulate. The contrast of neural priming effects showed larger signal changes in the right precuneus associated with the taxonomic but not with thematic priming response. We suggest that the greater involvement of precuneus in the processing of taxonomic relations indicates their reduced salience in the knowledge structure compared to more prominent thematic relations.

Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

Orientation contrast is formed when some elements orient differently from their surroundings. Although orientation contrast can be processed in the absence of top-down attention, the underlying neural mechanism for this automatic processing in humans is controversial. In particular, whether automatic detection of orientation contrast occurs at the initial feedforward stage in the primary visual cortex (i.e., V1) remains unclear. Here, we used event-related potentials (ERPs) to examine the automatic processing of orientation contrast in humans. In three experiments, participants completed a task at fixation while orientation contrasts were presented in the periphery, either in the upper visual field (UVF) or the lower visual field (LVF). All experiments showed significant positive potentials evoked by orientation contrasts over occipital areas within 100 ms after stimulus onset. These contrast effects occurred 10-20 ms later than the C1 components evoked by identically located abrupt onset stimuli which indexes the initial feedforward activity in V1. Compared with those in the UVF, orientation contrasts in the LVF evoked earlier and stronger activities, probably reflecting a LVF advantage in processing of orientation contrast. Even when orientation contrasts were rendered almost invisible by backward masking (in Experiment 2), the early contrast effect in the LVF was not disrupted. These findings imply that automatic processing of orientation contrast could occur at early visual cortical processing stages, but was slightly later than the initial feedforward processing in human V1; such automatic processing may involve either recurrent processing in V1 or feedforward processing in early extrastriate visual cortex. Highlights -We examined the earliest automatic processing of orientation contrast in humans with ERPs.-Significant orientation contrast effect started within 100 ms in early visual areas.-The earliest orientation contrast effect occurred later than the C1 evoked by abrupt onset stimuli.-The earliest orientation contrast effect was independent of top-down attention and awareness.-Automatic detection of orientation contrast arises slightly after the initial feedforward processing in V1.

Thermal shift assay (TSA) is an increasingly popular technique used for identifying protein stabilizing conditions or interacting ligands in X-ray crystallography and drug discovery applications. Although the setting up and running of TSA reactions is a relatively simple process, the subsequent analysis of TSA data, especially in high-throughput format, requires substantial amount of effort if conducted manually. We therefore developed the Thermal Shift Assay-Curve Rapid and Automatic Fitting Tool (TSA-CRAFT), a freely available software that enable automatic analysis of TSA data of any throughput. TSA-CRAFT directly reads real-time PCR instrument data files and displays the analyzed results in a web browser. This software features streamlined data processing and Boltzmann equation fitting, which is demonstrated in this study to provide more accurate data analysis than the commonly used first-derivative method. TSA-CRAFT is freely available as a cross-operating system-compatible standalone tool ( https://sourceforge.net/projects/tsa-craft/ ) and also as a freely accessible web server ( http://tbtlab.org/tsacraft.html ).

This study investigated neural processes underlying automatic imitation and its modulation by ethnically diverse hand stimuli (Black, White) using event-related brain potentials (ERPs). Automatic imitation relies on motor stimulus-response compatibility (SRC), i.e., response conflict caused by motoric (in)congruency between task-irrelevant hand stimuli and the required response. Our novel task aimed to separate two distinct neuro-cognitive processing stages of automatic imitation and its modulation by ethnicity: the stage of stimulus processing (i.e. perception), comprising presentation of stimulus ethnicity and SRC, and the stage of response execution (i.e. action). Effects of ethnicity were observed in ERPs of different stages of stimulus processing - during presentation of ethnicity (LPP) and SRC (N190, P3). ERPs at response execution, Pre-Motion Positivity (PMP) and Reafferent Potential (RAP), were only sensitive to congruency. The N190 results may index visual self-other distinction, while the neural timecourse of P3 and PMP variation could reflect a dynamical decision process linking perception to action, with motor initiation reflected in the PMP component. The PMP might further index motor-related self-other distinction regardless of ethnicity. Importantly, overt motor execution was not influenced by ethnically diverse stimuli, which suggests generalizability of the automatic imitation effect across ethnicities.

The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images.

Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results. MAIT generates output files with the statistical results, peak annotation and metabolite identification.

As the impact of Alzheimer's disease (AD) is projected to grow in the coming decades as the world's population ages, the development of noninvasive and cost-effective methods of detecting AD is essential for the early prevention and mitigation of the progressive disease, alleviating its expected global impact. This study analyzes audio processing techniques and transcription methodologies to optimize the detection of AD through the natural language processing (NLP) of spontaneous speech. We enhanced audio fidelity using Boll Spectral Subtraction and evaluated the transcription accuracy of state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-against traditional manual transcription methods. The choice between local and cloud-based solutions hinges on a trade-off between privacy, ongoing costs, and computational requirements. Leveraging OpenAI's GPT for word embeddings, we enhanced the training of Support Vector Machine (SVM) classifiers, which were crucial in analyzing transcripts and refining detection accuracy. Our findings reveal that AI-driven transcriptions significantly outperform manual counterparts when classifying AD and Control samples, with Wav2Vec using enhanced audio exhibiting the highest accuracy and F-1 scores (0.99 for both metrics) for locally based systems and Rev AI using unenhanced audio leading cloud-based methods with comparable precision (0.96 for both metrics). The study also uncovers the detrimental effect of including interviewer speech in recordings on model performance, advocating for the exclusion of such interactions to improve data quality for AD classification algorithms. Our comprehensive evaluation demonstrates that AI transcription (both Cloud and Local) and NLP technologies in their current forms can classify AD, as well as probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, accurately but suffer from a lack of available training data. The insights garnered from this research lay the groundwork for future advancements in the noninvasive monitoring and early detection of cognitive impairments through linguistic analysis.

Dual process theories propose that the brain uses 2 types of thinking to influence behavior: automatic processing and reflective processing. Automatic processing is fast, immediate, nonconscious, and unintentional, whereas reflective processing focuses on logical reasoning, and it is slow, step by step, and intentional. Most digital psychological health interventions tend to solely target the reflective system, although the automatic processing pathway can have strong influences on behavior. Laboratory-based research has highlighted that automatic processing tasks can create behavior change; however, there are substantial gaps in the field on the design, implementation, and delivery of automatic processing tasks in real-world settings. It is important to identify and summarize the existing literature in this area to inform the translation of laboratory-based research to real-world settings.

Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences. Using the Twitter public streaming application programming interface, we collected tweets containing certain stress-related keywords (eg, "chronic," "constant," "stress") and then filtered the data using pre-defined text patterns. We manually annotated tweets with (without) self-report of chronic stress as positive (negative). We trained multiple classifiers and tested them via accuracy and F1 score. We annotated 4195 tweets (1560 positives, 2635 negatives), achieving an inter-annotator agreement of 0.83 (Cohen's kappa). The classifier based on Bidirectional Encoder Representation from Transformers performed the best (accuracy of 83.6% [81.0-86.1]), outperforming the second best-performing classifier (support vector machines: 76.4% [73.5-79.3]). The past tweets from the authors of positive tweets contained useful information, including sources and health impacts of chronic stress. Our study demonstrates that users' self-reported chronic stress experiences can be automatically identified on Twitter, which has a high potential for surveillance and large-scale intervention.

Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging (PMA) in this paper), based on the likelihoods of the distances between body points. This method has the advantage of being automatic, while allowing an efficient gap data recovery. To support and validate the proposed method, we use a set of four individual recovery models, based on linear/nonlinear regression in local coordinate systems. Finally, we propose two heuristic algorithms to enforce skeleton constraints in the reconstructed motion, which can be used on any individual recovery model. For validation purposes, random gaps were introduced into motion-capture sequences, and the effects of factors such as the number of simultaneous gaps, gap length and sequence duration were analyzed. Results show that the proposed probabilistic averaging method yields better recovery than (i) each of the four individual models and (ii) two recent state-of-the-art models, regardless of gap length, sequence duration and number of simultaneous gaps. Moreover, both of our heuristic skeleton-constraint algorithms significantly improve the recovery for 7 out of 8 tested motion-capture sequences (p < 0.05), for 10 simultaneous gaps of 5 seconds. The code is available for free download at: https://github.com/numediart/MocapRecovery.

Molecular evolution analyses, such as detection of adaptive/purifying selection or ancestral protein reconstruction, typically require three inputs for a target gene (or gene family) in a particular group of organisms: sequence alignment, model of evolution, and phylogenetic tree. While modern advances in high-throughput sequencing techniques have led to rapid accumulation of genomic-scale data in public repositories and databases, mining such vast amount of information often remains a challenging enterprise. Here, we describe a comprehensive, versatile workflow aimed at the preparation of genome-extracted datasets readily available for molecular evolution research. The workflow involves: (1) fishing (searching and capturing) specific gene sequences of interest from taxonomically diverse genomic data available in databases at variable levels of annotation, (2) processing and depuration of retrieved sequences, (3) production of a multiple sequence alignment, (4) selection of best-fit model of evolution, and (5) solid reconstruction of a phylogenetic tree.

The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.

Dimensionality reduction is a key step in the analysis of single-cell RNA-sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single-cell data. However, with no linear relation between gene and embedding coordinate, there is no way to extract the identity of genes driving any cell's position in the low-dimensional embedding, making it difficult to characterize the underlying biological processes.

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.

Our study recalculated the use factor of linear accelerators (LINACs) by using an in-house program based on Digital Imaging and Communications in Medicine radiation therapy (DICOM-RT). We considered the impact of advancements and changes in treatment trends, including modality, technology, and radiation dose, on the use factor, which is one of the shielding parameters. In accordance with the methodology described in the NCRP 151 report, we computed the use factor for four linear accelerators (LINACs) across three hospitals. We analyzed the results based on the treatment techniques and treatment sites for three-dimensional conformal radiation therapy (3D-CRT) and intensity modulated radiation therapy or volumetric modulated arc therapy. Our findings revealed that the use factors obtained at 45° and 90° were 14.8% and 13.5% higher than those of the NCRP 151 report. In treatment rooms with a high 3D-CRT ratio, the use factor at a specific angle differed by up to 14.6% relative to the NCRP 151 report value. Our results showed a large difference in the use factor for specific sites such as the breast and spine, so it is recommended that each institution recalculate the use factor using patient's data.

The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to enable automatic classification of unstructured information on smoking status from Swedish EMR data.

A thorough understanding of the impact of the 3D meso-structure on damage and failure patterns is essential for revealing the failure conditions of composite rock materials such as coal, concrete, marble, and others. This paper presents a 3D XCT dataset of coal rock with 1372 slices (each slice contains 1720 × 1771 pixels in x × y direction). The 3D XCT datasets were obtained by MicroXMT-400 using the 225/320kv Nikon Metris custom bay. The raw datasets were processed by an automatic semantic segmentation method based on the Swin Transformer (Swin-T) architecture, which aims to overcome the issue of large errors and low efficiency for traditional methods. The hybrid loss function proposed can also effectively mitigate the influence of large volume features in the training process by incorporating modulation terms into the cross entropy loss, thereby enhancing the accuracy of segmentation for small volume features. This dataset will be available to the related researchers for further finite element analysis or microstructural statistical analysis, involving complex physical and mechanical behaviors at different scales.

Obstructive sleep apnea syndrome (OSAS) is a prevalent chronic disease characterized by sleep fragmentation and intermittent hypoxemia. Several studies suggested that electrophysiological changes and neurocognitive abnormalities occurred in OSAS patients. In this study, we compared automatic processing of emotional facial expressions schematic in OSAS patients and matched healthy controls via assessing expression-related mismatch negativity (EMMN).