The relation between children's lie-telling and their social and cognitive development was examined. Children (3-8 years) were told not to peek at a toy. Most children peeked and later lied about peeking. Children's subsequent verbal statements were not always consistent with their initial denial and leaked critical information revealing their deceit. Children's conceptual moral understanding of lies, executive functioning, and theory-of-mind understanding were also assessed. Children's initial false denials were related to their first-order belief understanding and their inhibitory control. Children's ability to maintain their lies was related to their second-order belief understanding. Children's lying was related to their moral evaluations. These findings suggest that social and cognitive factors may play an important role in children's lie-telling abilities. <<<

DNA has been pursued as a compelling medium for digital data storage during the past decade. While large-scale data storage and random access have been achieved in artificial DNA, the synthesis cost keeps hindering DNA data storage from popularizing into daily life. In this study, we proposed a more efficient paradigm for digital data compressing to DNA, while excluding arbitrary sequence constraints. Both standalone neural networks and pre-trained language models were used to extract the intrinsic patterns of data, and generated probabilistic portrayal, which was then transformed into constraint-free nucleotide sequences with a hierarchical finite state machine. Utilizing these methods, a 12%-26% improvement of compression ratio was realized for various data, which directly translated to up to 26% reduction in DNA synthesis cost. Combined with the progress in DNA synthesis, our methods are expected to facilitate the realization of practical DNA data storage. <<<

The endoplasmic reticulum (ER) presents unique properties to establishing bacterium symbiosis in eukaryotic cells since it synthesizes and glycosylates essential molecules like proteins and lipids. Tunicamycin (TM) is an antibiotic that inhibits the first step in the N-linked glycosylation in eukaryotes and has been used as an ER stress inducer to activate the Unfolded Protein Response (UPR). Mutualistic symbiosis in trypanosomatids is characterized by structural adaptations and intense metabolic exchanges, thus we investigated the effects of TM in the association between Angomonas deanei and its symbiotic bacterium, through ultrastructural and proteomic approaches. Cells treated with the inhibitor showed a decrease in proliferation, enlargement of the ER and Golgi cisternae and an increased distance between the symbiont and the ER. TM proved to be an important tool to better understand ER stress in trypanosomatids, since changes in protein composition were observed in the host protozoan, especially the expression of the Hsp90 chaperone. Furthermore, data obtained indicates the importance of the ER for the adaptation and maintenance of symbiotic associations between prokaryotes and eukaryotes, considering that this organelle has recognized importance in the biogenesis and division of cell structures. <<<

The hippocampal-entorhinal system encodes a map of space that guides spatial navigation. Goal-directed behaviour outside of spatial navigation similarly requires a representation of abstract forms of relational knowledge. This information relies on the same neural system, but it is not known whether the organisational principles governing continuous maps may extend to the implicit encoding of discrete, non-spatial graphs. Here, we show that the human hippocampal-entorhinal system can represent relationships between objects using a metric that depends on associative strength. We reconstruct a map-like knowledge structure directly from a hippocampal-entorhinal functional magnetic resonance imaging adaptation signal in a situation where relationships are non-spatial rather than spatial, discrete rather than continuous, and unavailable to conscious awareness. Notably, the measure that best predicted a behavioural signature of implicit knowledge and blood oxygen level-dependent adaptation was a weighted sum of future states, akin to the successor representation that has been proposed to account for place and grid-cell firing patterns. <<<

Schizophrenia is a heterogeneous psychiatric disorder that is poorly treated with current therapies. In this brief review, we provide an update regarding the use of animal models to study schizophrenia in an attempt to understand its aetiology and develop novel therapeutic strategies. Tremendous progress has been made developing and validating rodent models that replicate the aetiologies, brain pathologies, and behavioural abnormalities associated with schizophrenia in humans. Here, models are grouped into 3 categories-developmental, drug induced, and genetic-to reflect the heterogeneous risk factors associated with schizophrenia. Each of these models is associated with varied but overlapping pathophysiology, endophenotypes, behavioural abnormalities, and cognitive impairments. Studying schizophrenia using multiple models will permit an understanding of the core features of the disease, thereby facilitating preclinical research aimed at the development and validation of better pharmacotherapies to alter the progression of schizophrenia or alleviate its debilitating symptoms. <<<

Abstract<br> There are various indicators i.e. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD) , Stochastic Oscillator which have advantages in applications to determine not only market movements with buying and selling decisions in Computational Finance, but have significant drawbacks that discrepancies are easy to match against the best trading times due to fixed order-triggering boundaries and delay problems. For example, RSI ’s 70 and 30 overbuy and oversell are fixed boundaries. Orders can only be triggered when RSI’s value exceeds one of the boundaries. Its computation only considers past market situation prompting indicators like RSI to trigger orders with delay. In this paper, we proposed a method to reduce these problems with advanced AI technologies to generate indicators’ buy and sell signals executed in the best trading time. Recurrent Neural Network (RNN) has outstanding performance to learn time-series data automatic with long-time sequences but ordinary RNN units such as Long-Short-Term-Memory(LSTM) are unable to decipher the relationships between time units, so-called context. Hence, researchers have proposed an algorithm based on RNNs’ Attention Mechanism allowing RNNs to learn information such as chaotic attributes and Quantum properties contained in time sequences. Chaos Theory and Quantum Finance Theory (QFT) are also proposed to simulate these two features. One of the well-performed QFT models is Quantum Price Level (QPL) to simulate all possible vibration levels to locate price. The system used in this paper consists of two components - neural network and fuzzy logic. Neural networks are used to predict future data and to solve indicators lagging problem whereas fuzzy logic is used to solve fixed order-triggering boundaries problem. By combining these two core components, the proposed model has obtained remarkable results in backtesting previous data that it is possible for these methods to make better investment decisions when market changes constantly. <<<

PURPOSE: To evaluate the diagnostic utility of publicly funded clinical exome sequencing (ES) for patients with suspected rare genetic diseases.<br>METHODS: We prospectively enrolled 297 probands who met eligibility criteria and received ES across 5 sites in Ontario, Canada, and extracted data from medical records and clinician surveys. Using the Fryback and Thornbury Efficacy Framework, we assessed diagnostic accuracy by examining laboratory interpretation of results and assessed diagnostic thinking by examining the clinical interpretation of results and whether clinical-molecular diagnoses would have been achieved via alternative hypothetical molecular tests.<br>RESULTS: Laboratories reported 105 molecular diagnoses and 165 uncertain results in known and novel genes. Of these, clinicians interpreted 102 of 105 (97%) molecular diagnoses and 6 of 165 (4%) uncertain results as clinical-molecular diagnoses. The 108 clinical-molecular diagnoses were in 104 families (35% diagnostic yield). Each eligibility criteria resulted in diagnostic yields of 30% to 40%, and higher yields were achieved when >2 eligibility criteria were met (up to 45%). Hypothetical tests would have identified 61% of clinical-molecular diagnoses.<br>CONCLUSION: We demonstrate robustness in eligibility criteria and high clinical validity of laboratory results from ES testing. The importance of ES was highlighted by the potential 40% of patients that would have gone undiagnosed without this test. <<<

Double-negative T (DNT) cells are a rare and unconventional T-lymphocyte subpopulation lacking both CD4 and CD8 markers. Their immunopathological roles and clinical relevance have yet to be elucidated. Beyond autoimmune lymphoproliferative syndrome (ALPS), these cells may also play a role in rheumatic disorders, including systemic lupus erythematosus (SLE); indeed, these two diseases share several autoimmune manifestations (including nephritis). Moreover, one of the main experimental murine models used to investigate lupus, namely the MRL/lpr mouse, is characterized by an expansion of DNT cells, which can support the production of pathogenic autoantibodies and/or modulate the immune response in this context. However, lupus murine models are not completely consistent with their human SLE counterpart, of course. In this mini review, we summarize and analyze the most relevant clinical studies investigating the DNT cell population in SLE patients. Overall, based on the present literature review and analysis, DNT cell homeostasis seems to be altered in patients with SLE. Indeed, most of the available clinical studies (which include both adults and children) reported an increased DNT cell percentage in SLE patients, especially during the active phases, even though no clear correlation with disease activity and/or inflammatory parameters has been clearly established. Well-designed, standardized, and longitudinal clinical studies focused on DNT cell population are needed, in order to further elucidate the actual contribution of these cells in SLE pathogenesis and their interactions with other immune cells (also implicated and/or altered in SLE, such as basophils), and clarify whether their expansion and/or immunophenotypic aspects may have any immunopathological relevance (and, then, represent potential disease markers and, in perspective, even therapeutic targets) or are just an unspecific epiphenomenon of autoimmunity. <<<

Microplastics have been detected in various media including water, sediment, and seafood, whereas there are few studies focusing on microplastics in take-out containers. In this study, we collected take-out containers made of common polymer materials (polypropylene, PP; polystyrene, PS; polyethylene, PE; polyethylene terephthalate, PET) from five cities in China. Microplastics in the containers were analyzed after different treatments (direct flushing and flushing after immersing with hot water). Our results showed that microplastics were found in all take-out containers and abundance ranged from 3 to 29 items/container. The highest abundance occurred in PS containers with rough surface. The polymer types of some detected particles were the same as those of original containers, accounting for 30% of the total microplastics; other types included polyester, rayon, acrylic, and nylon. Treating the containers with hot water did not influence microplastic abundance. Our study indicates that microplastics in take-out containers come from atmospheric fallout and flakes from container's inner surfaces. Under slight mechanical force, loose structure and rough surface of PS containers can flake off microplastics, entering water more easily. Based on the microplastic abundance in take-out containers, people who order take-out food 4-7 times weekly may ingest 12-203 pieces of microplastics through containers. <<<

AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.<br>DISCUSSIONS: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.<br>CONCLUSIONS: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care. <<<

Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects based on eight genome-wide association study cohorts. Population genetic analyses revealed that domestication, local adaptation and heterosis are all associated with QTN allele frequency changes. A genome navigation system, RiceNavi, was developed for QTN pyramiding and breeding route optimization, and implemented in the improvement of a widely cultivated indica variety. This work presents an efficient platform that bridges ever-increasing genomic knowledge and diverse improvement needs in rice. <<<

Within clinical, biomedical, and translational science, an increasing number<br>of projects are adopting graphs for knowledge representation. Graph-based data<br>models elucidate the interconnectedness between core biomedical concepts,<br>enable data structures to be easily updated, and support intuitive queries,<br>visualizations, and inference algorithms. However, knowledge discovery across<br>these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity<br>and complexity; the proliferation of ad hoc data formats; poor compliance with<br>guidelines on findability, accessibility, interoperability, and reusability;<br>and, in particular, the lack of a universally-accepted, open-access model for<br>standardization across biomedical KGs has left the task of reconciling data<br>sources to downstream consumers. Biolink Model is an open source data model<br>that can be used to formalize the relationships between data structures in<br>translational science. It incorporates object-oriented classification and<br>graph-oriented features. The core of the model is a set of hierarchical,<br>interconnected classes (or categories) and relationships between them (or<br>predicates), representing biomedical entities such as gene, disease, chemical,<br>anatomical structure, and phenotype. The model provides class and edge<br>attributes and associations that guide how entities should relate to one<br>another. Here, we highlight the need for a standardized data model for KGs,<br>describe Biolink Model, and compare it with other models. We demonstrate the<br>utility of Biolink Model in various initiatives, including the Biomedical Data<br>Translator Consortium and the Monarch Initiative, and show how it has supported<br>easier integration and interoperability of biomedical KGs, bringing together<br>knowledge from multiple sources and helping to realize the goals of<br>translational science. <<<

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets. <<<

Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure. <<<

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data structures, computational methods and visualization tools available in Bioconductor to investigate Hi-C, micro-C and other 3C-related data, in R. An online book ( https://bioconductor.org/books/OHCA/ ) further provides prospective end users with a number of workflows to process, import, analyze and visualize any type of chromosome conformation capture data. <<<

Flow-based propagation and spatiotemporal Transformer are two mainstream<br>mechanisms in video inpainting (VI). Despite the effectiveness of these<br>components, they still suffer from some limitations that affect their<br>performance. Previous propagation-based approaches are performed separately<br>either in the image or feature domain. Global image propagation isolated from<br>learning may cause spatial misalignment due to inaccurate optical flow.<br>Moreover, memory or computational constraints limit the temporal range of<br>feature propagation and video Transformer, preventing exploration of<br>correspondence information from distant frames. To address these issues, we<br>propose an improved framework, called ProPainter, which involves enhanced<br>ProPagation and an efficient Transformer. Specifically, we introduce<br>dual-domain propagation that combines the advantages of image and feature<br>warping, exploiting global correspondences reliably. We also propose a<br>mask-guided sparse video Transformer, which achieves high efficiency by<br>discarding unnecessary and redundant tokens. With these components, ProPainter<br>outperforms prior arts by a large margin of 1.46 dB in PSNR while maintaining<br>appealing efficiency. <<<

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference. <<<