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

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel \textbf{H}ierarchical \textbf{C}ontrastive \textbf{V}isual \textbf{P}rompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols

A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.Comment: 39 pages, 14 figure

Assessment of hydrological connectivity characteristics of riparian zones and their correlation with root–soil composites at different bank heights of a first-class river in China

Under the combined effects of topography and vegetation, hydrological connectivity characteristics of riverbank slopes become complex and unclear, which limit the utilization and protection of riparian zones. To quantify the hydrological connectivity in root–soil composites, we conducted dyeing and tracing experiments in a high elevation plot and a low elevation plot on the bank of the Fenhe River. Soil and root properties and hydrological connectivity indexes in the plots were measured and analyzed. The results showed that the soil dyeing area ratio was approximate 1 in the soil depth of 0–5 cm and then decreased to 0.1 from 5 cm to 25 cm. The dyeing area ratio, maximum dyed depth, length index, peak value and non-uniformity coefficient of the high plot (Pc2) were 27%, 26%, 5%, 40% and 45% greater than those of the low plot (Pc1). The index of hydrological connectivity (IHC) of Pc2 was 7%, 44% and 71% greater than Pc1 in the soil depths 0–10 cm, 10–20 cm and 20–30 cm respectively. There was no significant correlation between the IHC and the physical properties of the soil at different depths, and the soil hydrological connectivity was closely related to the plant roots with diameter less than 1mm. The study primarily explored the characteristics of hydrological connectivity in root–soil composites. The results provide a scientific basis for exploring hydrological connectivity of riparian zones, which can support future riparian zone protection and restoration efforts in similar regions

Experimental study on the mechanisms of flow and sediment transport in a vegetated channel

Laboratory experiments were conducted in a flume with 3 types of artificial flexible submerged and emergent vegetation. Detailed velocity and sediment concentration in the channel were measured. The results show that submerged and emergent vegetation generates a much greater resistance to flow, and significantly alters the vertical distributions of velocity, especially in the vegetated and downstream regions. In comparison with the non-vegetated case, the turbulence kinetic energy and Reynolds stresses in the vegetated and downstream regions are much higher, indicating strong flow turbulence and momentum exchange in these areas. The high turbulence also results in a nearly constant fine suspended sediment concentration in the water column for all cases, while the increased resistance causes the coarser suspended sediment concentration to decrease. In addition, the sediment retention by the vegetation with small height is insignificant, but for the canopy with large height, the significant sediment deposition is found at the upstream region of the vegetated region

Evidence for an Excitonic Insulator State in Ta2_2Pd3_3Te5_5

The excitonic insulator (EI) is an exotic ground state of narrow-gap semiconductors and semimetals arising from spontaneous condensation of electron-hole pairs bound by attractive Coulomb interaction. Despite research on EIs dating back to half a century ago, their existence in real materials remains a subject of ongoing debate. In this study, through systematic experimental and theoretical investigations, we provide evidence for the existence of an EI ground state in a van der Waals compound Ta$_2$Pd$_3$Te$_5$. Density-functional-theory calculations suggest that it is a semimetal with a small band overlap, whereas various experiments exhibit an insulating ground state with a clear band gap. Upon incorporating electron-hole Coulomb interaction into our calculations, we obtain an EI phase where the electronic symmetry breaking opens a many-body gap. Angle-resolved photoemission spectroscopy measurements exhibit that the band gap is closed with a significant change in the dispersions as the number of thermally excited charge carriers becomes sufficiently large in both equilibrium and nonequilibrium states. Structural measurements reveal a slight breaking of crystal symmetry with exceptionally small lattice distortion in the insulating state, which cannot account for the significant gap opening. Therefore, we attribute the insulating ground state with a gap opening in Ta$_2$Pd$_3$Te$_5$ to exciton condensation, where the coupling to the symmetry-breaking electronic state induces a subtle change in the crystal structure.Comment: 10 pages, 5 figure

MEIS2C and MEIS2D promote tumor progression via Wnt/β-catenin and hippo/YAP signaling in hepatocellular carcinoma

Abstract Background MEIS2 has been identified as one of the key transcription factors in the gene regulatory network in the development and pathogenesis of human cancers. Our study aims to identify the regulatory mechanisms of MEIS2 in hepatocellular carcinoma (HCC), which could be targeted to develop new therapeutic strategies. Methods The variation of MEIS2 levels were assayed in a cohort of HCC patients. The proliferation, clone-formation, migration, and invasion abilities of HCC cells were measured to analyze the effects of MEIS2C and MEIS2D (MEIS2C/D) knockdown with small hairpin RNAs in vitro and in vivo. Chromatin immunoprecipitation (ChIP) was performed to identify MEIS2 binding site. Immunoprecipitation and immunofluorescence assays were employed to detect proteins regulated by MEIS2. Results The expression of MEIS2C/D was increased in the HCC specimens when compared with the adjacent noncancerous liver (ANL) tissues. Moreover, MEIS2C/D expression negatively correlated with the prognosis of HCC patients. On the other hand, knockdown of MEIS2C/D could inhibit proliferation and diminish migration and invasion of hepatoma cells in vitro and in vivo. Mechanistically, MESI2C activated Wnt/β-catenin pathway in cooperation with Parafibromin (CDC73), while MEIS2D suppressed Hippo pathway by promoting YAP nuclear translocation via miR-1307-3p/LATS1 axis. Notably, CDC73 could directly either interact with MEIS2C/β-catenin or MEIS2D/YAP complex, depending on its tyrosine-phosphorylation status. Conclusions Our studies indicate that MEISC/D promote HCC development via Wnt/β-catenin and Hippo/YAP signaling pathways, highlighting the complex molecular network of MEIS2C/D in HCC pathogenesis. These results suggest that MEISC/D may serve as a potential novel therapeutic target for HCC.https://deepblue.lib.umich.edu/bitstream/2027.42/152244/1/13046_2019_Article_1417.pd

Strategies to improve energy and carbon efficiency of luxury hotels in Iran

Luxury hotels generate substantial carbon footprint and scholarly research is urgently required to better understand how it could be effectively mitigated. This study adopts a method of life cycle energy analysis (LCEA) to assess the energy and carbon performance of six luxury, five star, hotels located in Iran. The results of the energy and carbon assessment of luxury hotels in Iran are compared against the energy and carbon values reported in past hotel research. This current study finds that luxury hotels in Iran are up to 3–4 times more energy- and 7 times more carbon-intense than similar hotels examined in past research. Low cost of fossil fuels, international trade sanctions and the lack of governmental and corporate energy conservation targets discourage Iranian hoteliers from carbon footprint mitigation. To counteract poor energy and carbon efficiency of luxury hotels in Iran, it is important to relax economic sanctions, develop alternative energy sources, refine corporate energy conservation targets, regularly benchmark hotel energy performance and enable exchange of good practices amongst Iranian hoteliers