Boule Is Present in Fish and Bisexually Expressed in Adult and Embryonic Germ Cells of Medaka

10.1371/journal.pone.0006097PLoS ONE46

How we learn social norms: a three-stage model for social norm learning

As social animals, humans are unique to make the world function well by developing, maintaining, and enforcing social norms. As a prerequisite among these norm-related processes, learning social norms can act as a basis that helps us quickly coordinate with others, which is beneficial to social inclusion when people enter into a new environment or experience certain sociocultural changes. Given the positive effects of learning social norms on social order and sociocultural adaptability in daily life, there is an urgent need to understand the underlying mechanisms of social norm learning. In this article, we review a set of works regarding social norms and highlight the specificity of social norm learning. We then propose an integrated model of social norm learning containing three stages, i.e., pre-learning, reinforcement learning, and internalization, map a potential brain network in processing social norm learning, and further discuss the potential influencing factors that modulate social norm learning. Finally, we outline a couple of future directions along this line, including theoretical (i.e., societal and individual differences in social norm learning), methodological (i.e., longitudinal research, experimental methods, neuroimaging studies), and practical issues

Differential Conservation and Divergence of Fertility Genes boule and dazl in the Rainbow Trout

10.1371/journal.pone.0015910PLoS ONE61

3640 Unique EST Clusters from the Medaka Testis and Their Potential Use for Identifying Conserved Testicular Gene Expression in Fish and Mammals

BACKGROUND: The fish medaka is the first vertebrate capable of full spermatogenesis in vitro from self-renewing spermatogonial stem cells to motile test-tube sperm. Precise staging and molecular dissection of this process has been hampered by the lack of suitable molecular markers. METHODOLOGY AND PRINCIPAL FINDINGS: We have generated a normalized medaka testis cDNA library and obtained 7040 high quality sequences representing 3641 unique gene clusters. Among these, 1197 unique clusters are homologous to known genes, and 2444 appear to be novel genes. Ontology analysis shows that the 1197 gene products are implicated in diverse molecular and cellular processes. These genes include markers for all major types of testicular somatic and germ cells. Furthermore, markers were identified for major spermatogenic stages ranging from spermatogonial stem cell self-renewal to meiosis entry, progression and completion. Intriguingly, the medaka testis expresses at least 13 homologs of the 33 mouse X-chromosomal genes that are enriched in the testis. More importantly, we show that key components of several signaling pathways known to be important for testicular function in mammals are well represented in the medaka testicular EST collection. CONCLUSIONS/SIGNIFICANCE: Medaka exhibits a considerable similarity in testicular gene expression to mammals. The medaka testicular EST collection we obtained has wide range coverage and will not only consolidate our knowledge on the comparative analysis of known genes' functions in the testis but also provide a rich resource to dissect molecular events and mechanism of spermatogenesis in vivo and in vitro in medaka as an excellent vertebrate model

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Cobalt Element Effect of Ternary Mesoporous Cerium Lanthanum Solid Solution for the Catalytic Conversion of Methanol and CO₂ into Dimethyl Carbonate

A citric acid ligand assisted self-assembly method is used for the synthesis of ternary mesoporous cerium lanthanum solid solution doped with metal elements (Co, Zr, Mg). Their textural property was characterized by X-ray diffraction, transmission electron microscopy, N2 adsorption-desorption, X-ray photoelectron spectroscopy and TPD techniques, and so on. The results of catalytic testing for synthesis of dimethyl carbonate (DMC) from CH3OH and CO2 indicated that the DMC yield reached 316 mmol/g on Ce-La-Co solid solution when the reaction temperature was 413 K and the reaction pressure was 8.0 MPa. It was found that Co had synergistic effect with La and Ce, doping of Co on the mesoporous Ce-La solid solution was helpful to increase the surface area of the catalyst, promote CO2 adsorption and activation, and improve the redox performance of solid solution catalyst. The conversion of Co2+ to Co3+ resulted in the continuous redox cycle between Ce4+ and Ce3+, and the oxygen vacancy content of the catalyst was increased. Studies have shown that the catalytic performance of Ce-La-Co solid solution is positively correlated with oxygen vacancy content. On this basis, the reaction mechanism of DMC synthesis from CO2 and CH3OH on the catalyst was speculated

A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories

Marfusion: An Attention-Based Multimodal Fusion Model for Human Activity Recognition in Real-World Scenarios

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals’ activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition