Effects of a Serious Game for Adolescent Mental Health on Cognitive Vulnerability: Pilot Usability Study.

Background: Adolescent mental health is of utmost importance. E-mental health interventions, and serious games in particular, are appealing to adolescents and can have beneficial effects on their mental health. A serious game aimed at improving cognitive vulnerability (ie, beliefs or attitudes), which can predispose an individual to mental health problems, can contribute to the prevention of these problems in adolescents. Objective: This study aimed to assess the feasibility of the prototype of a serious game called "Silver." Methods: The prototype of the serious game was developed using a user-centered participatory design. The prototype of Silver focused on 1 aspect of a serious game for improving cognitive vulnerability in adolescents, that is, the recognition and identification of cognitive distortions. Through the game, players were required to identify and classify the character's thoughts as helpful or unhelpful. Upon successful advancement to the next level, the task becomes more challenging, as players must also identify specific types of cognitive distortions. A pre- and posttest uncontrolled design was used to evaluate the game, with a 1-week intervention phase in which participants were asked to play the game. Participants aged 12-16 years were recruited in schools. The outcomes of interest were the recognition of cognitive distortions and presence of participants' cognitive distortions. The game was also evaluated on its effects, content, and usefulness. Results: A total of 630 adolescents played Silver and completed the assessments. Adolescents were significantly better at recognizing cognitive distortions at the pretest (mean 13.09, SD 4.08) compared to the posttest (mean 13.82, SD 5.09; t629=-4.00, P<.001). Furthermore, their cognitive distortions decreased significantly at the posttest (mean 38.73, SD 12.79) compared to the pretest (mean 41.43, SD 10.90; t629=7.98, P<.001). Participants also indicated that the game helped them recognize cognitive distortions. Many participants considered the game appealing (294/610, 48.2%) but boring (317/610, 52%) and preferred a more comprehensive game (299/610, 49%). Conclusions: Findings from this study suggest that a serious game may be an effective tool for improving cognitive vulnerability in adolescents. The development of such a serious game, based on the prototype, is recommended. It may be an important and innovative tool for the universal prevention of mental health problems in adolescents. Future research on the effects of the game is warranted.

Continuous production machine for separating and shaping Taiping Houkui tea.

In response to the challenges of low automation and a lack of a continuous processing system for Taiping Houkui tea, this study proposed a design scheme for a continuous processing line and built a continuous processing prototype for testing by combining the production requirements of Taiping Houkui tea, the characteristics of withered leaves, and the existing relevant production equipment. First, the physical properties of Taiping Houkui tea were determined. A simulation was performed using the Hertz-Mindlin model, and the motion states of the tea leaves were obtained under different conditions to define the parameter design range of the experimental platform and verify its structural rationality. Then, the response surface methodology was used to optimize the working parameter ranges and obtain the best working parameters for the feeding and kneading mechanisms. Finally, a continuous production prototype was constructed for further production verification. The experimental results show that the success rate of continuous production on this platform was 70.68%, with an average output of approximately 0.4 kg/h for Taiping Houkui dry tea on a single slide track, and the produced tea was similar to manually made tea. This demonstrates that the continuous production technique has high feasibility and provides a reference for continuous production of Taiping Houkui tea.

Assessing the Usability and Feasibility of Digital Assistant Tools for Direct Support Professionals: Participatory Design and Pilot-Testing.

BACKGROUND: The United States is experiencing a direct support professional (DSP) crisis, with demand far exceeding supply. Although generating documentation is a critical responsibility, it is one of the most wearisome aspects of DSPs' jobs. Technology that enables DSPs to log informal time-stamped notes throughout their shift could help reduce the burden of end-of-shift documentation and increase job satisfaction, which in turn could improve the quality of life of the individuals with intellectual and developmental disabilities (IDDs) whom DSPs support. However, DSPs, with varied ages, levels of education, and comfort using technology, are not likely to adopt tools that detract from caregiving responsibilities or increase workload; therefore, technological tools for them must be relatively simple, extremely intuitive, and provide highly valued capabilities. OBJECTIVE: This paper describes the development and pilot-testing of a digital assistant tool (DAT) that enables DSPs to create informal notes throughout their shifts and use these notes to facilitate end-of-shift documentation. The purpose of the pilot study was to assess the usability and feasibility of the DAT. METHODS: The research team applied an established user-centered participatory design process to design, develop, and test the DAT prototypes between May 2020 and April 2023. Pilot-testing entailed having 14 DSPs who support adults with IDDs use the first full implementation of the DAT prototypes during 2 or 3 successive work shifts and fill out demographic and usability questionnaires. RESULTS: Participants used the DAT prototypes to create notes and help generate end-of-shift reports. The System Usability Scale score of 81.79 indicates that they found the prototypes easy to use. Survey responses imply that using the DAT made it easier for participants to produce required documentation and suggest that they would adopt the DAT if this tool were available for daily use. CONCLUSIONS: Simple technologies such as the DAT prototypes, which enable DSPs to use mobile devices to log time-stamped notes throughout their shift with minimal effort and use the notes to help write reports, have the potential to both reduce the burden associated with producing documentation and enhance the quality (level of detail and accuracy) of this documentation. This could help to increase job satisfaction and reduce turnover in DSPs, both of which would help improve the quality of life of the individuals with IDDs whom they support. The pilot test results indicate that DSPs found the DAT easy to use. Next steps include (1) producing more robust versions of the DAT with additional capabilities, such as storing data locally on mobile devices when Wi-Fi is not available; and (2) eliciting input from agency directors, families, and others who use data about adults with IDDs to help care for them to ensure that data produced by DSPs are relevant and useful.

Prototype theory and the importance of literary form for moral imagination.

Prototype theory, which argues that categories have graded (and thus fuzzy) membership based on prototypes, has been used as cognitive evidence to support moral particularism because if categories (in moral rules) only have fuzzy conceptual boundaries, moral rules are not enough for moral judgment, as specific situations also need to be considered to determine how these fuzzy categories should be understood, which is what moral particularism believes. The importance of literature for ethics, especially for moral imagination, has also been extensively discussed because literature can provide vivid examples for us to imagine different moral dilemmas, the consequences of different moral choices, and the feelings of different people facing different situations. Martha Nussbaum specifically argues that the literary form is the only adequate form to imagine certain complex moral situations. By analyzing concrete literary examples as well as the related ethical discussions and empirical findings, this article argues that, building on Nussbaum's argument, prototype theory can serve as a cognitive basis for the importance of literary form for moral imagination, because the literary form's tolerance of ambiguity suits how we ambiguously categorize the world.

Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration.

Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models' outputs remains a significant challenge. This is crucial for fostering the development of trusted models and facilitating domain expert validation, thereby empowering users and domain experts to utilize them confidently in high-risk decision-making contexts (e.g., decision-support systems in healthcare). In this work, we put forward a deep prototype learning model that supports interpretable and manipulable modeling and classification of medical time series (i.e., ECG signal). Specifically, we first optimize the representation of single heartbeat data by employing a bidirectional long short-term memory and attention mechanism, and then construct prototypes during the training phase. The final classification outcomes (i.e., normal sinus rhythm, atrial fibrillation, and other rhythm) are determined by comparing the input with the obtained prototypes. Moreover, the proposed model presents a human-machine collaboration mechanism, allowing domain experts to refine the prototypes by integrating their expertise to further enhance the model's performance (contrary to the human-in-the-loop paradigm, where humans primarily act as supervisors or correctors, intervening when required, our approach focuses on a human-machine collaboration, wherein both parties engage as partners, enabling more fluid and integrated interactions). The experimental outcomes presented herein delineate that, within the realm of binary classification tasks-specifically distinguishing between normal sinus rhythm and atrial fibrillation-our proposed model, albeit registering marginally lower performance in comparison to certain established baseline models such as Convolutional Neural Networks (CNNs) and bidirectional long short-term memory with attention mechanisms (Bi-LSTMAttns), evidently surpasses other contemporary state-of-the-art prototype baseline models. Moreover, it demonstrates significantly enhanced performance relative to these prototype baseline models in the context of triple classification tasks, which encompass normal sinus rhythm, atrial fibrillation, and other rhythm classifications. The proposed model manifests a commendable prediction accuracy of 0.8414, coupled with macro precision, recall, and F1-score metrics of 0.8449, 0.8224, and 0.8235, respectively, achieving both high classification accuracy as well as good interpretability.

Back in time to the Gly-rich prototype of the phosphate binding elementary function.

Binding of nucleotides and their derivatives is one of the most ancient elementary functions dating back to the Origin of Life. We review here the works considering one of the key elements in binding of (di)nucleotide-containing ligands - phosphate binding. We start from a brief discussion of major participants, conditions, and events in prebiotic evolution that resulted in the Origin of Life. Tracing back to the basic functions, including metal and phosphate binding, and, potentially, formation of primitive protein-protein interactions, we focus here on the phosphate binding. Critically assessing works on the structural, functional, and evolutionary aspects of phosphate binding, we perform a simple computational experiment reconstructing its most ancient and generic sequence prototype. The profiles of the phosphate binding signatures have been derived in form of position-specific scoring matrices (PSSMs), their peculiarities depending on the type of the ligands have been analyzed, and evolutionary connections between them have been delineated. Then, the apparent prototype that gave rise to all relevant phosphate-binding signatures had also been reconstructed. We show that two major signatures of the phosphate binding that discriminate between the binding of dinucleotide- and nucleotide-containing ligands are GxGxxG and GxxGxG, respectively. It appears that the signature archetypal for dinucleotide-containing ligands is more generic, and it can frequently bind phosphate groups in nucleotide-containing ligands as well. The reconstructed prototype's key signature GxGGxG underlies the role of glycine residues in providing flexibility and interactions necessary for binding the phosphate groups. The prototype also contains other ancient amino acids, valine, and alanine, showing versatility towards evolutionary design and functional diversification.

Unpacking public resistance to health Chatbots: a parallel mediation analysis.

Introduction: Despite the numerous potential benefits of health chatbots for personal health management, a substantial proportion of people oppose the use of such software applications. Building on the innovation resistance theory (IRT) and the prototype willingness model (PWM), this study investigated the functional barriers, psychological barriers, and negative prototype perception antecedents of individuals' resistance to health chatbots, as well as the rational and irrational psychological mechanisms underlying their linkages. Methods: Data from 398 participants were used to construct a partial least squares structural equation model (PLS-SEM). Results: Resistance intention mediated the relationship between functional barriers, psychological barriers, and resistance behavioral tendency, respectively. Furthermore, The relationship between negative prototype perceptions and resistance behavioral tendency was mediated by resistance intention and resistance willingness. Moreover, negative prototype perceptions were a more effective predictor of resistance behavioral tendency through resistance willingness than functional and psychological barriers. Discussion: By investigating the role of irrational factors in health chatbot resistance, this study expands the scope of the IRT to explain the psychological mechanisms underlying individuals' resistance to health chatbots. Interventions to address people's resistance to health chatbots are discussed.

Promoting a Patient-Centered Understanding of Safety in Acute Mental Health Wards: A User-Centered Design Approach to Develop a Real-Time Digital Monitoring Tool.

BACKGROUND: Acute mental health services report high levels of safety incidents that involve both patients and staff. The potential for patients to be involved in interventions to improve safety within a mental health setting is acknowledged, and there is a need for interventions that proactively seek the patient perspective of safety. Digital technologies may offer opportunities to address this need. OBJECTIVE: This research sought to design and develop a digital real-time monitoring tool (WardSonar) to collect and collate daily information from patients in acute mental health wards about their perceptions of safety. We present the design and development process and underpinning logic model and programme theory. METHODS: The first stage involved a synthesis of the findings from a systematic review and evidence scan, interviews with patients (n=8) and health professionals (n=17), and stakeholder engagement. Cycles of design activities and discussion followed with patients, staff, and stakeholder groups, to design and develop the prototype tool. RESULTS: We drew on patient safety theory and the concepts of contagion and milieu. The data synthesis, design, and development process resulted in three prototype components of the digital monitoring tool (WardSonar): (1) a patient recording interface that asks patients to input their perceptions into a tablet computer, to assess how the ward feels and whether the direction is changing, that is, "getting worse" or "getting better"; (2) a staff dashboard and functionality to interrogate the data at different levels; and (3) a public-facing ward interface. The technology is available as open-source code. CONCLUSIONS: Recent patient safety policy and research priorities encourage innovative approaches to measuring and monitoring safety. We developed a digital real-time monitoring tool to collect information from patients in acute mental health wards about perceived safety, to support staff to respond and intervene to changes in the clinical environment more proactively.

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

A blockchain-based healthcare data marketplace: prototype and demonstration.

Objectives: This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods: We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results: We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion: Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion: The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

Contrastive Prototype-Guided Generation for Generalized Zero-Shot Learning.

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes, while only samples from seen classes are available for training. The mainstream methods mitigate the lack of unseen training data by simulating the visual unseen samples. However, the sample generator is actually learned with just seen-class samples, and semantic descriptions of unseen classes are just provided to the pre-trained sample generator for unseen data generation, therefore, the generator would have bias towards seen categories, and the unseen generation quality, including both precision and diversity, is still the main learning challenge. To this end, we propose a Prototype-Guided Generation for Generalized Zero-Shot Learning (PGZSL), in order to guide the sample generation with unseen knowledge. First, unseen data generation is guided and rectified in PGZSL by contrastive prototypical anchors with both class semantic consistency and feature discriminability. Second, PGZSL introduces Certainty-Driven Mixup for generator to enrich the diversity of generated unseen samples, while suppress the generation of uncertain boundary samples as well. Empirical results over five benchmark datasets show that PGZSL significantly outperforms the SOTA methods in both ZSL and GZSL tasks.

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new "evidence" with previously memorized "experience", we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data. In conclusion, MoViT provides a simple plug-in for transformer architectures which may contribute to reducing the training data needed to achieve acceptable models for a broad range of medical image analysis tasks.

Computational assessment of carbon fabric reinforced polymer made prosthetic knee: Mechanics, finite element simulations and experimental evaluation.

A prosthetic knee is designed to replace the functionality of an anatomical knee in transfemoral amputees. The purpose of a prosthetic knee is to restore mobility and compensate amputees for their impairment. In the present research numerical modelling and simulation of a carbon fabric reinforced polymer made polycentric prosthetic knee with four-bar mechanism was performed. Virtual prototyping with computer-aided design and computer-aided engineering software ensured geometric and structural stability of the knee design. The linkage mechanism, instantaneous centre's location and trajectory were investigated using multibody dynamics and analytical formulations. Computational simulations with a non-linear finite element model were employed with joints, contact formulations and an orthotropic material model to predict the displacement, stress formulated and life of the knee prosthesis under static and cyclic loading conditions. Finite element analysis assessed the strength and durability of knee in accordance to standards. Maximum Principal stress of 155 MPa and life expectancy of 3.1 × 106 cycles were determined for the composite knee through numerical simulations ensuring a safe design. Experimental testing was also conducted as per standards and the percentage error was estimated to be 2.52%, thereby establishing the validity of the finite element model deployed. This type of simulation-based approach can be implemented to efficiently and affordably design and prototype a prosthetic knee with desired functioning criteria.

Electrical safety assessment of a prototype device for electromagnetic stimulation of the ear in patients with tinnitus.

<b><br>Aim:</b> The aim of the study was to evaluate the results of electrical safety results of a prototype electromagnetic ear stimulation device in patients with tinnitus.</br> <b><br>Material and methods:</b> The electrical safety tests of the prototype device for electro- and magnetostimulation of the hearing organ were carried out at the Center for Attestation and Certification Tests in Gliwice. The tests concerned selected parameters including the PN-EN standard.</br> <b><br>Results:</b> Safety studies of the prototype electrical stimulation device for the ear in patients with tinnitus were necessary to perform the planned further preclinical studies. Obtained results regarding: identification and labeling of the device; protection against electric shock; checking protective earthing, functional earthing and potential equalization; checking the leakage current and auxiliary currents of the patient; checking the distances through the solid insulation and the use of thin insulating spacers; checking the electrical strength of the device insulation; checking protection against mechanical hazards of the device; checking the risk associated with surfaces, corners and edges, and checking the protection against excessive temperatures and other threats comply with the standard PN-EN.</br> <b><br>Conclusions:</b> No risk to the patient and medical staff. Tests of protection against mechanical hazards of the device have shown that the only movable part whose contact with the patient could cause an unacceptable risk is the fan installed inside the housing.</br>.

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain-Computer Interface.

The rapid serial visual presentation-based brain-computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

Operational criteria application does not change clinicians' opinion on the diagnosis of mental disorder: a pre- and post-intervention validity study.

Objective: Our objective was to check if the ICD-10 operational criteria application changes non-operational, prototype-based diagnoses obtained in a real-life scenario. Methods: Psychiatry residents applied the diagnostic criteria of the ICD-10 as a "diagnostic test" to five outpatient patients they were already following who had a prototype-based diagnosis. Tests were used to ascertain whether changes in opinion were significant and if any of the diagnostic groups were more prone to change than others. The present paper is part of the study with UTN U1111-1260-1212. Results: Seventeen residents reviewed their last five case files, retrieving 85 diagnostic pairs of non-operational-based vs. operational-based diagnoses. The Stuart-Maxwell test did not indicate a significant opinion change (χ2 = 5.25, p = 0.39; power = 0.94) besides 30% of diagnostic changes. Despite not being statistically significant, 20.2% of all evaluations resulted in a change that would affect treatment choices. Using ICD-10 operational criteria slightly increased the number of observed diagnoses, but probably without clinical relevance. None of the non-operational diagnoses have a higher tendency to change with operational criteria application (χ2 = 11.6, p = 0.07). The female gender was associated with a higher diagnostic change tendency. Conclusion: Applying ICD-10 operational criteria as a diagnostic test does not induce a statistically significant diagnostic opinion change in residents and no diagnostic group seems more sensible to diagnostic change. Gender-related differences in diagnostic opinion changes might be evidence of sunk cost bias. Although not statistically significant, using operational criteria after diagnostic elaboration might help to deal with subjects without adequate treatment response.

scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.

MOTIVATION: Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. RESULTS: To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.

Prototype isochoric preservation device for large organs.

This paper presents the design and prototype of a constant volume (isochoric) vessel that can be used for the preservation of large organs in a supercooled state. This prototype is a preliminary version of a more advanced design. The device consists of a cooling bath operated by a mechanical vapor compression refrigeration unit and an isochoric chamber made of stainless steel. The preservation of organs using supercooling technology in an isochoric chamber requires a continuous temperature and pressure monitoring. While the device was initially designed for pig liver experiments, its innovative design and preservation capabilities suggest potential applications for preserving other organs as well. The isochoric reactor may be used to accommodate a variety of organ types, opening the door for further research into its multi-organ preservation capabilities. All the design details are presented in this study with the purpose of encouraging researchers in the field to build their own devices, and by this to improve the design. We chose to design the device for isochoric supercooling as the method of preservation to avoid the ice formation.

The role of category valence in prototype preference.

People prefer prototypical stimuli over atypical stimuli. The dominant explanation for this prototype preference effect is that prototypical stimuli are processed more fluently. However, a more recent account proposes that prototypes are more strongly associated with their category's valence, leading to a reversed prototype preference effect for negative categories. One critical but untested assumption of this category-valence account is that no prototype preference should emerge for entirely neutral categories. We tested this prediction by conditioning categories of dot patterns positively, negatively, or neutrally. In line with previous findings on the category-valence account, prototype preference reversed for negatively conditioned categories. However, prototype preference was similarly strong for positive and neutral categories. These findings imply that prototype preferences do not only reflect a transfer of category valence to exemplars. Instead, the results suggest that prototype preference is a multi-process phenomenon arising from the activated category valence and a fluency-based process. We discuss further implications for theories on fluency and prototype preference.

Oblique warping: A general distortion of spatial perception.

There are many putatively distinct phenomena related to perception in the oblique regions of space. For instance, the classic oblique effect describes a deficit in visual acuity for oriented lines in the obliques, and classic "prototype effects" reflect a bias to misplace objects towards the oblique regions of space. Yet these effects are explained in very different terms: The oblique effect itself is often understood as arising from orientation-selective neurons, whereas prototype effects are described as arising from categorical biases. Here, we explore the possibility that these effects (and others) may stem from a single underlying spatial distortion. We show that there is a general distortion of (angular) space in the oblique regions that influences not only orientation judgments, but also location, extent, and size. We argue that these findings reflect oblique warping, a general distortion of spatial representations in the oblique regions which may be the root cause of many oblique effects.