Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT

Characterization of Natural Dye Extracted from Wormwood and Purple Cabbage for Dye-Sensitized Solar Cells

This study used natural dyes as sensitizers of dye-sensitized solar cells (DSSCs) to replace expensive chemical synthetic dyes. We prepared two natural dyes, chlorophyll dye and anthocyanin dye, by extracting them from wormwood and purple cabbage, respectively. Moreover, we mixed the prepared chlorophyll dye and anthocyanin dye at 5 different volume ratios to form cocktail dyes. For preparation of photoelectrode, P25 TiO2 nanoparticles were used to prepare paste, which was coated on fluorine-doped tin oxide (FTO) conductive glass by the spin coating method at different spin coating speeds in order to form TiO2 thin films with different thicknesses. The DSSC prepared by the cocktail dye achieves photoelectric conversion efficiency (η) of 1.95%, open-circuit voltage (VOC) of 0.765 V, and short-circuit current density (JSC) of 5.83 mA/cm2. Moreover, the prepared DSSC sensitized solely by chlorophyll extract of wormwood achieved a photoelectric conversion efficiency (η) of 0.9%, whereas the DSSC sensitized solely by anthocyanin extract of purple cabbage achieved a photoelectric conversion efficiency of 1.47%, achieving the longest lifetime of electrons amongst these three dyes

ATP Released by Astrocytes Mediates Glutamatergic Activity-Dependent Heterosynaptic Suppression

AbstractExtracellular ATP released from axons is known to assist activity-dependent signaling between neurons and Schwann cells in the peripheral nervous system. Here we report that ATP released from astrocytes as a result of neuronal activity can also modulate central synaptic transmission. In cultures of hippocampal neurons, endogenously released ATP tonically suppresses glutamatergic synapses via presynaptic P2Y receptors, an effect that depends on the presence of cocultured astrocytes. Glutamate release accompanying neuronal activity also activates non-NMDA receptors of nearby astrocytes and triggers ATP release from these cells, which in turn causes homo- and heterosynaptic suppression. In CA1 pyramidal neurons of hippocampal slices, a similar synaptic suppression was also produced by adenosine, an immediate degradation product of ATP released by glial cells. Thus, neuron-glia crosstalk may participate in activity-dependent synaptic modulation

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles

CXCL9 Is a Potential Biomarker of Immune Infiltration Associated With Favorable Prognosis in ER-Negative Breast Cancer

The chemokine CXCL9 (C-X-C motif chemokine ligand 9) has been reported to be required for antitumour immune responses following immune checkpoint blockade. In this study, we sought to investigate the potential value of CXCL9 according to immune responses in patients with breast cancer (BC). A variety of open-source databases and online tools were used to explore the expression features and prognostic significance of CXCL9 in BC and its correlation with immune-related biomarkers followed by subsequent verification with immunohistochemistry experiments. The CXCL9 mRNA level was found to be significantly higher in BC than in normal tissue and was associated with better survival outcomes in patients with ER-negative tumours. Moreover, CXCL9 is significantly correlated with immune cell infiltration and immune-related biomarkers, including CTLA4, GZMB, LAG3, PDCD1 and HAVCR2. Finally, we performed immunohistochemistry with breast cancer tissue samples and observed that CXCL9 is highly expressed in the ER-negative subgroup and positively correlated with the immune-related factors LAG3, PD1, PDL1 and CTLA4 to varying degrees. These findings suggest that CXCL9 is an underlying biomarker for predicting the status of immune infiltration in ER-negative breast cancer

Mapping the distribution of Anthrax in Mainland China, 2005-2013

Anthrax in China was characterized by significant seasonality and spatial clustering. The spatial distribution of human anthrax was largely driven by livestock husbandry, human density, land cover, elevation, topsoil features and climate. Enhanced surveillance and intervention for livestock and human anthrax in the high-risk regions, particularly on the Qinghai-Tibetan Plateau, is the key to the prevention of human infections