Kind representations

The hippocampus (HPC) and retrosplenial cortex (RSC) are key components of the brain's memory and navigation systems. Lesions of either region produce profound deficits in spatial cognition and HPC neurons exhibit well-known spatial firing patterns (place fields). Recent studies have also identified an array of navigation-related firing patterns in the RSC. However, there has been little work comparing the response properties and information coding mechanisms of these two brain regions. In the present study, we examined the firing patterns of HPC and RSC neurons in two tasks which are commonly used to study spatial cognition in rodents, open field foraging with an environmental context manipulation and continuous T-maze alternation...

State-of-the-art model for zero-shot cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning to achieve the label-agnostic semantic alignment between each utterance and its code-switched data. However, it ignores the precious intent/slot labels, whose label information is promising to help capture the label-aware semantics structure and then leverage supervised contrastive learning to improve both source and target languages' semantics. In this paper, we propose Hybrid and Cooperative Contrastive Learning to address this problem...

Humans effortlessly use vision to plan and guide navigation through the local environment, or "scene". A network of three cortical regions responds selectively to visual scene information, including the occipital (OPA), parahippocampal (PPA), and medial place areas (MPA) - but how this network supports visually-guided navigation is unclear. Recent evidence suggests that one region in particular, the OPA, supports visual representations for navigation, while PPA and MPA support other aspects of scene processing...

This article is concerned with the dynamics of change in protein consumption practices from the perspective of the consumer. It is based on a model, informed by social representation theory, that aims to understand the role played by various types of representation of alternative proteins in the process of changing food consumption practices. It discusses the reception, by consumers, of the representations associated with alternative proteins on Instagram. Methodologically, three focus groups were organized with different consumer segments (omnivorous, flexitarian and vegetarian and vegan consumers), as well as seven individual interviews...

Revealing the mechanisms that influence transcription factor binding specificity is the key to understanding gene regulation. In previous studies, DNA double helix structure and one-hot embedding have been used successfully to design computational methods for predicting transcription factor binding sites (TFBSs). However, DNA sequence as a kind of biological language, the method of word embedding representation in natural language processing, has not been considered properly in TFBS prediction models. In our work, we integrate different types of features of DNA sequence to design a multichanneled deep learning framework, namely MulTFBS, in which independent one-hot encoding, word embedding encoding, which can incorporate contextual information and extract the global features of the sequences, and double helix three-dimensional structural features have been trained in different channels...

BACKGROUND: Opioid and substance misuse has become a widespread problem in the United States, leading to the "opioid crisis." The relationship between substance misuse and mental health has been extensively studied, with one possible relationship being that substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. OBJECTIVES: This study aims to analyze social media posts related to substance use and opioids being sold through cryptomarket listings...

PURPOSE: Verbal working memory is poorer for children with hearing loss than for peers with normal hearing (NH), even with cochlear implantation and early intervention. Poor verbal working memory can affect academic performance, especially in higher grades, making this deficit a significant problem. This study examined the stability of verbal working memory across middle childhood, tested working memory in adolescents with NH or cochlear implants (CIs), explored whether signal enhancement can improve verbal working memory, and tested two hypotheses proposed to explain the poor verbal working memory of children with hearing loss: (a) Diminished auditory experience directly affects executive functions, including working memory; (b) degraded auditory inputs inhibit children's abilities to recover the phonological structure needed for encoding verbal material into storage...

BACKGROUND: A growing body of research has analysed the representations of alcohol and other drugs (AOD) in policy-making, but few studies have focused on the representations reproduced in law-making processes, especially in the context of the regulation of the rights of social and health care service users. This study examined what kind of representations of AOD use are reproduced in the legislative reform of social and health care service users' rights in Finland. The purpose of the reform is to strengthen social and health care service users' rights to self-determination and to reduce the use of restrictive measures...

OBJECTIVE: Existing literature documents high rates of experienced violence in asylum seekers. Despite this high prevalence, experiences of traumatic stress are neither necessary nor sufficient grounds for claiming asylum, without documented experiences of persecution. The aim of the current study is to better understand the role of co-occurring pre-migratory social determinants, stigma, and trauma on the experiences of persecution among asylum seekers in the United States. METHOD: We conducted a retrospective file review of legal declarations submitted by 25 asylum seekers who participated in forensic mental health evaluations at a pro-bono asylum clinic...

BACKGROUND: Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis. METHODS: In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM)...

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations...

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints...

Theories of semantic organization have historically prioritized investigation of concrete concepts pertaining to inanimate objects and natural kinds. As a result, accounts of the conceptual representation of emotions have almost exclusively focused on their juxtaposition with concrete concepts. The present study aims to fill this gap by deriving a large set of normative feature data for emotion concepts and assessing similarities and differences between the featural representation of emotion, nonemotion abstract, and concrete concepts...

With the emergence of critical heritage studies, scholars show that 'bottom up' initiatives that blur the boundaries between private, civil, and state have arisen not as a modernising vision to legitimise national authority but as 'rooted in identification with local community', linking past and future. In China, such studies demonstrate the emergence of a different kind of museology - with 'private' heritage initiatives on behalf of individuals and groups - tolerated by the state authorities through investments that link heritage tourism to development...

Artificial intelligence (AI) systems are increasingly being used not only to classify and analyze but also to generate images and text. As recent work on the content produced by text and image Generative AIs has shown (e.g., Cheong et al., 2024, Acerbi & Stubbersfield, 2023), there is a risk that harms of representation and bias, already documented in prior AI and natural language processing (NLP) algorithms may also be present in generative models. These harms relate to protected categories such as gender, race, age, and religion...

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables visualization of cellular tissue morphology during surgical procedures. To capture high-quality pCLE images during tissue scanning, it is important to maintain close contact between the probe and the tissue, while also keeping the probe perpendicular to the tissue surface. Existing robotic pCLE tissue scanning systems, which rely on macroscopic vision, struggle to accurately place the probe at the optimal position on the tissue surface...

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task...

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models...

A finite difference numerical simulation scrutiny is executed to evaluate the combined impacts of heat generation, buoyancy forces, viscous dissipation and Joule heating in unsteady hydro-magnetic mixed convective chemically reactive and radiative Casson fluid flowing along an exponentially accelerating permeable vertical plate engrossed in a porous media by considering ramp surface concentration and temperature. The dimensionless non-linear coupled PDEs describing the flow model are dealt numerically by adopting the competent implicit Crank-Nicolson finite difference procedure...

Medical multi-modal pre-training has revealed promise in computer-aided diagnosis by leveraging large-scale unlabeled datasets. However, existing methods based on masked autoencoders mainly rely on data-level reconstruction tasks, but lack high-level semantic information. Furthermore, two significant heterogeneity challenges hinder the transfer of pre-trained knowledge to downstream tasks, i.e., the distribution heterogeneity between pre-training data and downstream data, and the modality heterogeneity within downstream data...