Delineating Region-Specific contributions and connectivity patterns for semantic association and categorization through ROI and Granger causality analysis.

The neural mechanisms supporting semantic association and categorization are examined in this study. Semantic association involves linking concepts through shared themes, events, or scenes, while semantic categorization organizes meanings hierarchically based on defining features. Twenty-three adults participated in an fMRI study performing categorization and association judgment tasks. Results showed stronger activation in the inferior frontal gyrus during association and marginally weaker activation in the posterior middle temporal gyrus (pMTG) during categorization. Granger causality analysis revealed bottom-up connectivity from the visual cortex to the hippocampus during semantic association, whereas semantic categorization exhibited strong reciprocal connections between the pMTG and frontal semantic control regions, together with information flow from the visual association area and hippocampus to the pars triangularis. We propose that demands on semantic retrieval, precision of semantic representation, perceptual experiences and world knowledge result in observable differences between these two semantic relations.

CIDACC: Chlorella vulgaris image dataset for automated cell counting.

This CIDACC dataset was created to determine the cell population of Chlorella vulgaris microalga during cultivation. Chlorella vulgaris has diverse applications, including use as food supplement, biofuel production, and pollutant removal. High resolution images were collected using a microscope and annotated, focusing on computer vision and machine learning models creation for automatic Chlorella cell detection, counting, size and geometry estimation. The dataset comprises 628 images, organized into hierarchical folders for easy access. Detailed segmentation masks and bounding boxes were generated using external tools enhancing the dataset's utility. The dataset's efficacy was demonstrated through preliminary experiments using deep learning architecture such as object detection and localization algorithms, as well as image segmentation algorithms, achieving high precision and accuracy. This dataset is a valuable tool for advancing computer vision applications in microalgae research and other related fields. The dataset is particularly challenging due to its dynamic nature and the complex correlations it presents across various application domains, including cell analysis in medical research. Its intricacies not only push the boundaries of current computer vision algorithms but also offer significant potential for advancements in diverse fields such as biomedical imaging, environmental monitoring, and biotechnological innovations.

Interaction-knowledge semantic alignment for recommendation.

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user-item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users. In order to overcome the limitations mentioned above, we present a recommendation method named Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR). By introducing a semantic alignment module, the semantic difference between the interaction bipartite graph and the knowledge graph is reduced. The representation of user is integrated during the fusion of representations of item, which improves the quality of the fused representation of item. To validate the efficacy of the proposed approach, we perform comprehensive experiments on three datasets. The experimental results demonstrate that the IKSAR is superior to the existing methods, showcasing notable improvement.

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images.

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Automated remote speech-based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy.

Introduction: We investigated the agreement between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing. Methods: We examined 78 cases from the Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD) study, including cognitively normal individuals and individuals with subjective cognitive decline, mild cognitive impairment, and dementia. We used Bayesian Bland-Altman analysis of word count and the qualitative features of semantic cluster size, cluster switches, and word frequencies. Results: We found high levels of agreement for word count, with a 93% probability of a newly observed difference being below the minimally important difference. The qualitative features had fair levels of agreement. Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode. Discussion: Our results support the use of automated speech recognition particularly for the assessment of quantitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes. Highlights: High levels of agreement were found between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing, particularly for word count.The qualitative features had fair levels of agreement.Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode.Automated speech recognition for the assessment of quantitative and qualitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes, seems feasible and reliable.

Mind Language Disturbances and PET-Signs of Depression vs Alzheimer's Disease: Are There Any Common Patterns Identified?

BACKGROUND: There is a broad appreciation that a diagnosis of depression (D) in the elderly is a strong risk factor for incident dementia, particularly Alzheimer's disease (AD). Indeed, the two disorders might constitute a dyad, although their causal relationship is uncertain, given the likely bidirectional and compounding effects of social withdrawal and loss of previous activities, and the manifestation of language disturbances, cognitive dysfunction, and social disruption that are typical of both conditions. We argue that language declines in D and AD share common patterns and biological underpinnings, and that D/AD patients might benefit from intensive language remediation training aiming to improve the functioning of neural networks that are linked to similar cognitive impairments. METHODS: A literature search in PubMed database included topics of language disturbances, cognitive impairments, and molecular brain imaging by positron emission tomography (PET) to identify common patterns in D and AD regarding language decline and its neurobiological underpinnings. RESULTS: Language disturbances show a particular commonality in the two disorders, manifesting in simplified language and particular speech markers (e.g., lexical and semantic repetitions, arguably due to ruminations in D and memory deficits in AD). PET can reveal abnormal protein deposits that are practically diagnostic of AD, but cerebrometabolic deficits to PET with the glucose tracer FDG show a certain commonality in D and AD. Typical findings of hypometabolism in the frontal lobes doubtless underlie the executive function deficits, where frontal hypometabolism in prodromal D increases with AD progression. This may reflect overlapping changes in noradrenaline and other neurotransmitter (e.g. serotonin) changes. Cerebrometabolic deficits associated with language dysfunction may inform targeted language remediation treatments in the D/AD progression. CONCLUSIONS: Language remediation techniques targeting specific language disturbances might present an important complimentary treatment strategy along with an adjusted pharmacotherapy approach and standard psychosocial rehabilitation interventions. We see a need for investigations of language remediation informed by the overlapping pathologies and language disturbances in D and AD.

Is semantic dementia an outdated entity?

Does it still make clinical sense to talk about semantic dementia? For more than 10 years, some researchers and clinicians have highlighted the need for new diagnostic criteria, arguing for this entity either to be redefined or, more recently, to be divided into two partially distinct entities, each with its own supposed characteristics, namely the semantic variant primary progressive aphasia and the semantic behavioral variant frontotemporal dementia. Why such a shift? Is it no longer appropriate to talk about semantic dementia? Is it really useful to divide the concept of semantic dementia into verbal and socioemotional semantic subcomponents? Does this proposal have any clinical merit or does it solely reflect theoretical considerations? To shed light on these questions, the purpose of the present review was to explore theoretical considerations on the nature of the knowledge that is disturbed in this disease which might justify such terminological changes.

Exploring Metalinguistic Awareness in School-Aged Autistic Children: Insights from Grammatical Judgment.

Metalinguistic awareness, the ability to manipulate and reflect upon language, remains largely unexplored in the autistic population. To address this gap, this observational cross-sectional study examines the metalinguistic abilities of school-aged autistic children in comparison to neurotypical peers in a novel tablet-based Grammatical Judgment Task (GJT) of reduced linguistic complexity engaging two kinds of metacognitive resources. Children had to judge non-verbally whether pre-recorded sentences were grammatically correct or not, following the traditional GJT paradigm assessing metamorphosyntactic skills. In addition, sentences with anomalous meaning that were either grammatically correct or grammatically incorrect were introduced to test metasemantic knowledge. Findings reveal no difference in performance between the groups, with participants performing on average above chance level both on the sentences assessing mere metamorphosyntactic skills and on the sentences placing an additional demand on metasemantics. This study shows that autistic individuals are able to mobilize metalinguistic resources when tested via a task of reduced linguistic complexity.

Mapping and modeling the semantic space of math concepts.

Mathematics is an underexplored domain of human cognition. While many studies have focused on subsets of math concepts such as numbers, fractions, or geometric shapes, few have ventured beyond these elementary domains. Here, we attempted to map out the full space of math concepts and to answer two specific questions: can distributed semantic models, such a GloVe, provide a satisfactory fit to human semantic judgements in mathematics? And how does this fit vary with education? We first analyzed all of the French and English Wikipedia pages with math contents, and used a semi-automatic procedure to extract the 1000 most frequent math terms in both languages. In a second step, we collected extensive behavioral judgements of familiarity and semantic similarity between them. About half of the variance in human similarity judgements was explained by vector embeddings that attempt to capture latent semantic structures based on cooccurence statistics. Participants' self-reported level of education modulated familiarity and similarity, allowing us to create a partial hierarchy among high-level math concepts. Our results converge onto the proposal of a map of math space, organized as a database of math terms with information about their frequency, familiarity, grade of acquisition, and entanglement with other concepts.

IG-Net: An Instrument-guided real-time semantic segmentation framework for prostate dissection during surgery for low rectal cancer.

BACKGROUND AND OBJECTIVE: Accurate prostate dissection is crucial in transanal surgery for patients with low rectal cancer. Improper dissection can lead to adverse events such as urethral injury, severely affecting the patient's postoperative recovery. However, unclear boundaries, irregular shape of the prostate, and obstructive factors such as smoke present significant challenges for surgeons. METHODS: Our innovative contribution lies in the introduction of a novel video semantic segmentation framework, IG-Net, which incorporates prior surgical instrument features for real-time and precise prostate segmentation. Specifically, we designed an instrument-guided module that calculates the surgeon's region of attention based on instrument features, performs local segmentation, and integrates it with global segmentation to enhance performance. Additionally, we proposed a keyframe selection module that calculates the temporal correlations between consecutive frames based on instrument features. This module adaptively selects non-keyframe for feature fusion segmentation, reducing noise and optimizing speed. RESULTS: To evaluate the performance of IG-Net, we constructed the most extensive dataset known to date, comprising 106 video clips and 6153 images. The experimental results reveal that this method achieves favorable performance, with 72.70% IoU, 82.02% Dice, and 35 FPS. CONCLUSIONS: For the task of prostate segmentation based on surgical videos, our proposed IG-Net surpasses all previous methods across multiple metrics. IG-Net balances segmentation accuracy and speed, demonstrating strong robustness against adverse factors.

Object and spatial discrimination makes weakly supervised local feature better.

Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precision of keypoints. To address these challenges, this study introduces a novel local feature extraction pipeline named OSDFeat (Object and Spatial Discrimination Feature). OSDFeat employs a decoupling strategy, training descriptor and detection networks independently. Inspired by semantic correspondence, we propose an Object and Spatial Discrimination ResUNet (OSD-ResUNet). OSD-ResUNet captures features from the feature map that differentiate object appearance and spatial context, thus enhancing descriptor performance. To further improve the discriminative capability of descriptors, we propose a Discrimination Information Retained Normalization module (DIRN). DIRN complementarily integrates spatial-wise normalization and channel-wise normalization, yielding descriptors that are more distinguishable and informative. In the detection network, we propose a Cross Saliency Pooling module (CSP). CSP employs a cross-shaped kernel to aggregate long-range context in both vertical and horizontal dimensions. By enhancing the saliency of keypoints, CSP enables the detection network to effectively utilize descriptor information and achieve more precise localization of keypoints. Compared to the previous best local feature extraction methods, OSDFeat achieves Mean Matching Accuracy of 79.4% in local feature matching task, improving by 1.9% and achieving state-of-the-art results. Additionally, OSDFeat achieves competitive results in Visual Localization and 3D Reconstruction. The results of this study indicate that object and spatial discrimination can improve the accuracy and robustness of local feature, even in challenging environments. The code is available at https://github.com/pandaandyy/OSDFeat.

Semantic and phonemic verbal fluency tests: Normative data for the Turkish population.

Semantic and phonemic verbal fluency tests are widely used neuropsychological assessments of executive functions and language skills and are easy to administer. The aim of this study was to determine the impact of age, education, and gender on semantic and phonemic verbal fluency tests and to establish normative data for Turkish adults aged between 18 and 86 years. The results revealed significant main effects of age and education on all subscores of verbal fluency tests. Furthermore, an interaction effect between age and education was observed on semantic fluency and letter K fluency scores. While no significant differences were found among the 18-29, 30-39, and 40-49 age groups in any of the subscores, performance on the tests decreased with increasing age. Significant differences were observed among all education groups in all subscores. No main or interaction effects of gender were found on any subscore. These normative data could prove useful in clinical and research settings for the assessment of cognitive impairment.

What we mean when we say semantic: Toward a multidisciplinary semantic glossary.

Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, "concept" has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate semantic theories remain underspecified and/or opaque. Weak construct specificity is among the leading causes of the replication crisis now facing psychology and related fields. Term ambiguity hinders cross-disciplinary communication, falsifiability, and incremental theory-building. Numerous cognitive subdisciplines (e.g., vision, affective neuroscience) have recently addressed these limitations via the development of consensus-based guidelines and definitions. The project to follow represents our effort to produce a multidisciplinary semantic glossary consisting of succinct definitions, background, principled dissenting views, ratings of agreement, and subjective confidence for 17 target constructs (e.g., abstractness, abstraction, concreteness, concept, embodied cognition, event semantics, lexical-semantic, modality, representation, semantic control, semantic feature, simulation, semantic distance, semantic dimension). We discuss potential benefits and pitfalls (e.g., implicit bias, prescriptiveness) of these efforts to specify a common nomenclature that other researchers might index in specifying their own theoretical perspectives (e.g., They said X, but I mean Y).

Age differences in BOLD modulation to task difficulty as a function of amyloid burden.

Effective cognitive performance often requires the allocation of additional neural resources (i.e. blood-oxygen-level-dependent [BOLD] activation) as task demands increase, and this demand-related modulation is affected by amyloid-beta deposition and normal aging. The present study investigated these complex relationships between amyloid, modulation, and cognitive function (i.e. fluid ability). Participants from the Dallas Lifespan Brain Study (DLBS, n = 252, ages 50-89) completed a semantic judgment task during functional magnetic resonance imaging (fMRI) where the judgments differed in classification difficulty. Amyloid burden was assessed via positron emission tomography (PET) using 18F-florbetapir. A quadratic relationship between amyloid standardized value uptake ratios (SUVRs) and BOLD modulation was observed such that modulation was weaker in those with moderately elevated SUVRs (e.g. just reaching amyloid-positivity), whereas those with very high SUVRs (e.g. SUVR > 1.5) showed strong modulation. Greater modulation was related to better fluid ability, and this relationship was strongest in younger participants and those with lower amyloid burden. These results support the theory that effective demand-related modulation contributes to healthy cognitive aging, especially in the transition from middle age to older adulthood, whereas high modulation may be dysfunctional in those with substantial amyloid deposition.

Automating parasite egg detection: insights from the first AI-KFM challenge.

In the field of veterinary medicine, the detection of parasite eggs in the fecal samples of livestock animals represents one of the most challenging tasks, since their spread and diffusion may lead to severe clinical disease. Nowadays, the scanning procedure is typically performed by physicians with professional microscopes and requires a significant amount of time, domain knowledge, and resources. The Kubic FLOTAC Microscope (KFM) is a compact, low-cost, portable digital microscope that can autonomously analyze fecal specimens for parasites and hosts in both field and laboratory settings. It has been shown to acquire images that are comparable to those obtained with traditional optical microscopes, and it can complete the scanning and imaging process in just a few minutes, freeing up the operator's time for other tasks. To promote research in this area, the first AI-KFM challenge was organized, which focused on the detection of gastrointestinal nematodes (GINs) in cattle using RGB images. The challenge aimed to provide a standardized experimental protocol with a large number of samples collected in a well-known environment and a set of scores for the approaches submitted by the competitors. This paper describes the process of generating and structuring the challenge dataset and the approaches submitted by the competitors, as well as the lessons learned throughout this journey.

The effects of cross-language overlap and semantic transparency on the processing of L2 collocations.

Although extensive research has been carried out on collocation processing, it is still unclear how cross-language overlap and transparency influence the processing of collocations by L2 learners. In the current study, a phrase judgment task was used to investigate the processing of congruent (i.e., exist in both English and Arabic) and incongruent collocations (i.e., exist only in English) by Arabic non-native speakers of English. The semantic transparency of the items was controlled for. Results demonstrated the effect of congruency on processing: congruent items yielded more correct responses and faster response times than incongruent items. The effect of congruency was modulated by proficiency, with congruency having a stronger effect on lower-proficiency learners than higher-proficiency learners. Transparency had no effect, with no differences in response times and accuracy between transparent and opaque collocations. The findings have implications for the learning and teaching of L2 collocations.

Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome.

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

The retrograde effects of negative emotion on memory for conceptually related events: a registered report.

Emotional events are often remembered better than neutral ones; however, emotion can also spill over and affect our memory for neutral experiences that precede an emotional event. Theories suggest that emotion can retroactively enhance memory for preceding neutral events that are considered high-priority while impairing memory for events deemed low-priority. However, the impact of conceptual relationships (i.e., semantic connections) between preceding neutral information and emotional events on memory for the preceding information has received little attention. This study investigated the influence of conceptual relatedness on the retroactive effects of emotion on memory. Participants sequentially encoded pairs of images that were high or low in conceptual relatedness, each comprising a neutral object followed by either a negative or neutral image. Participants returned the next day for a recognition memory assessment. The results indicated an interactive effect of emotion and conceptual relatedness on memory: In a "discovery" sample, memory was poorer for images preceding conceptually unrelated negative (vs. neutral) images, while the opposite pattern was seen for conceptually related images. In a "replication" sample, these effects were partially replicated, with the former impairment effect statistically observed but not the latter augmentation effect. Hence, conceptual relatedness affects how negative emotion influences memory.

DAEiS-Net: Deep Aggregation Network with Edge Information Supplement for Tunnel Water Stain Segmentation.

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder-decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation.

Semantic Interaction Meta-Learning Based on Patch Matching Metric.

Metric-based meta-learning methods have demonstrated remarkable success in the domain of few-shot image classification. However, their performance is significantly contingent upon the choice of metric and the feature representation for the support classes. Current approaches, which predominantly rely on holistic image features, may inadvertently disregard critical details necessary for novel tasks, a phenomenon known as "supervision collapse". Moreover, relying solely on visual features to characterize support classes can prove to be insufficient, particularly in scenarios involving limited sample sizes. In this paper, we introduce an innovative framework named Patch Matching Metric-based Semantic Interaction Meta-Learning (PatSiML), designed to overcome these challenges. To counteract supervision collapse, we have developed a patch matching metric strategy based on the Transformer architecture to transform input images into a set of distinct patch embeddings. This approach dynamically creates task-specific embeddings, facilitated by a graph convolutional network, to formulate precise matching metrics between the support classes and the query image patches. To enhance the integration of semantic knowledge, we have also integrated a label-assisted channel semantic interaction strategy. This strategy merges word embeddings with patch-level visual features across the channel dimension, utilizing a sophisticated language model to combine semantic understanding with visual information. Our empirical findings across four diverse datasets reveal that the PatSiML method achieves a classification accuracy improvement of 0.65% to 21.15% over existing methodologies, underscoring its robustness and efficacy.