A valuable tool for researchers, this allows for the swift development of knowledge bases specifically tailored to their needs.
Lightweight knowledge bases tailored to individual scientific specializations are achievable with our method, effectively improving hypothesis formulation and literature-based discovery (LBD). Researchers can devote their expertise to forming and testing hypotheses, by prioritizing post-hoc fact-checking of individual data points over preliminary verification efforts. The knowledge bases, meticulously constructed, showcase the adaptability and versatility inherent in our research approach, which caters to diverse interests. A web-based platform, accessible via the online link https://spike-kbc.apps.allenai.org, is available. The tool empowers researchers to rapidly construct knowledge bases that cater to their unique information demands and research requirements.
This paper elucidates our method for extracting medications and their attributes from clinical notes, the central theme of Track 1 within the 2022 National Natural Language Processing (NLP) Clinical Challenges (n2c2) shared task.
Using the Contextualized Medication Event Dataset (CMED), 500 notes from 296 patients were incorporated into the prepared dataset. The three fundamental components of our system were medication named entity recognition (NER), event classification (EC), and context classification (CC). The creation of these three components relied on transformer models, each employing unique architectures and input text engineering methods. A zero-shot learning solution for classifying CC was investigated.
Our leading performance systems registered micro-average F1 scores of 0.973, 0.911, and 0.909 for the tasks of NER, EC, and CC, respectively.
A deep learning-based NLP system was implemented in this study, and it was shown that the use of special tokens aids in distinguishing multiple medication references in a single context, while aggregating multiple events of a particular medication into separate labels improved the system's performance.
A deep learning NLP system was implemented and evaluated, highlighting how employing special tokens facilitated the identification of diverse medication mentions within a single contextual segment and aggregating separate events of a single medication under different labels contributed to improved model performance.
Congenital blindness profoundly alters resting-state electroencephalographic (EEG) activity. Congenital blindness in humans is frequently marked by a decline in alpha brainwave activity, which is frequently observed in tandem with an increase in gamma activity during rest. Compared to control subjects with normal sight, the results show a heightened excitatory/inhibitory (E/I) ratio in the visual cortex. It is yet to be determined if the spectral pattern of EEG during rest would return to normal if vision were re-established. This study's aim was to evaluate periodic and aperiodic components from the EEG resting-state power spectrum to test this question. Prior studies have established a correlation between aperiodic components, following a power-law distribution and measured as a linear regression on the log-log spectrum, and the cortical excitation-inhibition ratio. In consequence, a more accurate estimate of the periodic activity results from the removal of the aperiodic components from the power spectrum. Analysis of resting EEG activity from two investigations is presented here. The first study compared 27 permanently congenitally blind adults (CB) with 27 age-matched sighted controls (MCB). The second study involved 38 individuals with reversed blindness caused by bilateral dense congenital cataracts (CC) and 77 age-matched normally sighted controls (MCC). Data-driven spectral analysis was performed to extract aperiodic components at low frequencies (Lf-Slope, 15-195 Hz) and high frequencies (Hf-Slope, 20-45 Hz). The Lf-Slope of the aperiodic component demonstrated a considerably steeper, more negative gradient, while the Hf-Slope was significantly less steep, displaying a less negative slope, in CB and CC participants compared to typically sighted controls. Alpha power significantly decreased, and an increase in gamma power was evident in both the CB and CC groups. During rest, the spectral profile's typical development seems to be influenced by a sensitive period, potentially causing an irreversible change in the E/I ratio of the visual cortex, a consequence of congenital blindness. We propose that these changes are likely a result of impaired inhibitory pathways and an uneven interaction between feedforward and feedback processing in the early visual cortex of people with a history of congenital blindness.
Disorders of consciousness are marked by persistent lack of responsiveness as a consequence of significant brain injury, a complex condition. The diagnostic problems and restricted treatment possibilities that are presented highlight a pressing need for a more thorough grasp of the origin of human consciousness from coordinated neural activity. 2-MeOE2 concentration With the rise in availability of multimodal neuroimaging data, a spectrum of clinically and scientifically motivated modeling endeavors has emerged, focused on improving patient stratification using data, discovering causative mechanisms for patient pathophysiology and more broadly, unconsciousness, and developing simulations to test potential treatments for regaining consciousness in a computational environment. The Working Group of clinicians and neuroscientists, part of the international Curing Coma Campaign, proposes a framework and vision for comprehending the divergent statistical and generative computational modelling techniques in this fast-evolving field. A comparison of the current leading-edge techniques in statistical and biophysical computational modeling within human neuroscience with the aspiration of a well-developed field dedicated to modeling consciousness disorders reveals areas where improvements could lead to better outcomes and treatments in the clinic. Finally, we present several suggestions on strategies for unified action among the field at large to overcome these concerns.
Significant repercussions for social communication and educational development are linked to memory impairments in children with autism spectrum disorder (ASD). However, a comprehensive understanding of memory difficulties in children with autism, and the neuronal pathways involved, is still lacking. Cognitive function and memory are closely associated with the default mode network (DMN), a brain network, and dysfunction of this network is a highly replicable and powerful brain signature for diagnosing autism spectrum disorder.
A study involving 25 8- to 12-year-old children with ASD and 29 typically developing controls used a comprehensive battery of standardized episodic memory assessments along with functional circuit analyses.
Children with ASD demonstrated a lower memory performance than their neurotypical peers. General memory and facial recognition ability emerged as independent dimensions of memory impairment in ASD cases. In children with ASD, the reduced capacity for episodic memory was consistently found in analyses of two separate and independent datasets. oncologic outcome A study scrutinizing the DMN's intrinsic functional circuits indicated a relationship between general memory and face memory deficits, each linked to unique, hyper-connected neural patterns. Significantly, a disrupted hippocampal-posterior cingulate cortex network was frequently observed in ASD individuals with diminished general and facial memory.
Our findings on episodic memory in children with ASD comprehensively evaluate and show consistent and substantial declines, linked to dysfunction in specific DMN-related circuits. These findings indicate a broader role of DMN dysfunction in ASD, affecting not only the ability to recall faces but also general memory performance.
Our research offers a comprehensive look at episodic memory function in children with autism spectrum disorder (ASD), identifying significant and reproducible patterns of reduced memory capacity linked to dysfunctions in distinct default mode network circuits. The findings underscore the importance of considering DMN dysfunction in ASD as a causative factor not only for face-related memory issues but also for more general memory deficits.
Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF), a growing field, supports the analysis of multiple simultaneous protein expressions at a single-cell resolution, ensuring the integrity of the tissue's structure. While these approaches reveal great potential for biomarker discovery, many difficulties still need to be surmounted. Importantly, the optimized cross-registration of multiplex immunofluorescence images with concurrent imaging techniques and immunohistochemistry (IHC) can potentially increase plex formation and/or enhance the quality of the generated data stream, particularly in downstream processes like cell isolation. A fully automated process, featuring hierarchical, parallelizable, and deformable registration, was implemented to address the issue of multiplexed digital whole-slide images (WSIs). Our generalization of the mutual information calculation, used as a registration guideline, spans arbitrary dimensions, making it highly applicable to situations requiring multi-view imaging. mediolateral episiotomy Our strategy for selecting optimal registration channels also included the utilization of self-information from a specific IF channel. Precise labeling of cell membranes within their native context is critical for accurate cell segmentation. A pan-membrane immunohistochemical staining method was developed accordingly, for incorporation into mIF panels or as a standalone IHC procedure followed by cross-registration. This study illustrates this procedure by registering whole-slide 6-plex/7-color mIF images with corresponding whole-slide brightfield mIHC images, encompassing a CD3 and pan-membrane stain. The WSI mutual information registration (WSIMIR) algorithm demonstrated highly accurate registration, enabling the retrospective generation of an 8-plex/9-color WSI. It significantly outperformed two alternative automated cross-registration methods, as measured by the Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, p < 0.01 for both comparisons).