Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks\' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Semantic memory representations are generally well maintained in aging, whereas semantic control is thought to be more affected. To explain this phenomenon, this study tested the predictions of the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH), focusing on task demands in aging as a possible framework. The CRUNCH effect would manifest itself in semantic tasks through a compensatory increase in neural activation in semantic control network regions but only up to a certain threshold of task demands. This study compares 39 younger (20-35 years old) with 39 older participants (60-75 years old) in a triad-based semantic judgment task performed in an fMRI scanner while manipulating task demand levels (low versus high) through semantic distance. In line with the CRUNCH predictions, differences in neurofunctional activation and behavioral performance (accuracy and response times) were expected in younger versus older participants in the low- versus high-demand conditions, which should be manifested in semantic control Regions of Interest (ROIs). Our older participants had intact behavioral performance, as proposed in the literature for semantic memory tasks (maintained accuracy and slower response times (RTs)). Age-invariant behavioral performance in the older group compared to the younger one is necessary to test the CRUNCH predictions. The older adults were also characterized by high cognitive reserve, as our neuropsychological tests showed. Our behavioral results confirmed that our task successfully manipulated task demands: error rates, RTs and perceived difficulty increased with increasing task demands in both age groups. We did not find an interaction between age group and task demand, or a statistically significant difference in activation between the low- and high-demand conditions for either RTs or accuracy. As for brain activation, we did not find the expected age group by task demand interaction, or a significant main effect of task demand. Overall, our results are compatible with some neural activation in the semantic network and the semantic control network, largely in frontotemporoparietal regions. ROI analyses demonstrated significant effects (but no interactions) of task demand in the left and right inferior frontal gyrus, the left posterior middle temporal gyrus, the posterior inferior temporal gyrus and the prefrontal gyrus. Overall, our test did not confirm the CRUNCH predictions.

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation-identifying crops from the background-crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.

We report the first use of ERP measures to identify text engagement differences when reading digitally or in print. Depth of semantic encoding is key for reading comprehension, and we predicted that deeper reading of expository texts would facilitate stronger associations with subsequently-presented related words, resulting in enhanced N400 responses to unrelated probe words and a graded attenuation of the N400 to related and moderately related words. In contrast, shallow reading would produce weaker associations between probe words and text passages, resulting in enhanced N400 responses to both moderately related and unrelated words, and an attenuated response to related words. Behavioral research has shown deeper semantic encoding of text from paper than from a screen. Hence, we predicted that the N400 would index deeper reading of text passages that were presented in print, and shallower reading of texts presented digitally. Middle-school students (n = 59) read passages in digital and print formats and high-density EEG was recorded while participants completed single-word semantic judgment tasks after each passage. Following digital text presentation, the N400 response pattern to moderately-related words indicated shallow reading, tracking with responses to words that were unrelated to the text. Following print reading, the N400 responses to moderately-related words patterned instead with responses to related words, interpreted as an index of deeper reading. These findings provide evidence of differences in brain responses to texts presented in print and digital media, including deeper semantic encoding for print than digital texts.

Spelling in any writing system is governed by fundamental principles. We examined the processing of two types of pseudohomophones constructed from words whose spellings are based on different principles - on the traditional principle of writing, requiring memorization of their spelling, and on the morphological principle, allowing the determination of their spelling from another word with the same morpheme (root) to examine the dependence of the occurrence of orthography-phonology conflict on spelling principles. Event-related potentials were recorded from 22 volunteers during silent reading. Pseudohomophones based on the morphological principle increased the N400 amplitude, emphasizing semantic and morphological processing importance. The P600 component showed significant effects for differentiating words and pseudohomophones based on the traditional principle, predominantly indicating the involvement of memory and reanalysis processes. Source reconstruction demonstrates that both pseudohomophones activate the left inferior frontal gyrus. However, pseudohomophones based on the traditional principle additionally activate the right and left postcentral gyrus, indicating the involvement of additional areas in the differentiation process. The earlier differences for stimuli based on the morphological principle indicate access to smaller units (morphemes), whereas stimuli based on the traditional principle require whole word processing. Our findings underscore the significant role of spelling principles in orthographic processing.

The use of taboo words represents one of the most common and arguably universal linguistic behaviors, fulfilling a wide range of psychological and social functions. However, in the scientific literature, taboo language is poorly characterized, and how it is realized in different languages and populations remains largely unexplored. Here we provide a database of taboo words, collected from different linguistic communities (Study 1, N = 1046), along with their speaker-centered semantic characterization (Study 2, N = 455 for each of six rating dimensions), covering 13 languages and 17 countries from all five permanently inhabited continents. Our results show that, in all languages, taboo words are mainly characterized by extremely low valence and high arousal, and very low written frequency. However, a significant amount of cross-country variability in words\' tabooness and offensiveness proves the importance of community-specific sociocultural knowledge in the study of taboo language.

Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the \"respect for tissue.\" The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety.

BACKGROUND: Recent developments in esophageal cancer treatment, including studies exploring active surveillance following chemoradiotherapy, have led to a need for clear terminology and definitions regarding different multimodal treatment options.
OBJECTIVE: The aim of this study was to reach worldwide consensus on the definitions and semantics of multimodal esophageal cancer treatment.
METHODS: In total, 72 experts working in the field of multimodal esophageal cancer treatment were invited to participate in this Delphi study. The study comprised three Delphi surveys sent out by email and one online meeting. Input for the Delphi survey consisted of terminology obtained from a systematic literature search. Participants were asked to respond to open questions and to indicate whether they agreed or disagreed with different statements. Consensus was reached when there was ≥75% agreement among respondents.
RESULTS: Forty-nine of 72 invited experts (68.1%) participated in the first online Delphi survey, 45 (62.5%) in the second survey, 21 (46.7%) of 45 in the online meeting, and 39 (86.7%) of 45 in the final survey. Consensus on neoadjuvant and definitive chemoradiotherapy with or without surgery was reached for 27 of 31 items (87%). No consensus was reached on follow-up after treatment with definitive chemoradiotherapy.
CONCLUSIONS: Consensus was reached on most statements regarding terminology and definitions of multimodal esophageal cancer treatment. Implementing uniform criteria facilitates comparison of studies and promotes international research collaborations.

Words, unlike images, are symbolic representations. The associative details inherent within a word\'s meaning and the visual imagery it generates, are inextricably connected to the way words are processed and represented. It is well recognised that the hippocampus associatively binds components of a memory to form a lasting representation, and here we show that the hippocampus is especially sensitive to abstract word processing. Using fMRI during recognition, we found that the increased abstractness of words produced increased hippocampal activation regardless of memory outcome. Interestingly, word recollection produced hippocampal activation regardless of word content, while the parahippocampal cortex was sensitive to concreteness of word representations, regardless of memory outcome. We reason that the hippocampus has assumed a critical role in the representation of uncontextualized abstract word meaning, as its information-binding ability allows the retrieval of the semantic and visual associates that, when bound together, generate the abstract concept represented by word symbols. These insights have implications for research on word representation, memory, and hippocampal function, perhaps shedding light on how the human brain has adapted to encode and represent abstract concepts.

The present research adopts ERP (Event-Related Potentials) technology to investigate whether there exists a concreteness effect in the processing of Chinese action verbs within metaphorical context. The mean amplitudes of N400 activated by action metaphors were compared with those activated by literal verbs and abstract verbs. The findings indicated that the Met verbs evoked a significantly larger N400 response at frontal brain region compared to the Abs verbs at a time window 200-500 ms, while the Met verbs elicited a notably greater N400 amplitude specifically at the posterior brain region in comparison to the Lit verbs at 300-500 ms time window. These results may be interpreted as indicating that the comprehension of the Met verbs is based on the concrete action semantics.