SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x 6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research. We will launch and consistently maintain a leaderboard to provide a platform for the community to assess and investigate model capability.Comment: Technical Report; Project released at: https://github.com/AILab-CVC/SEED-Benc

Screening and identification of the dominant antigens of the African swine fever virus

African swine fever is a highly lethal contagious disease of pigs for which there is no vaccine. Its causative agent African swine fever virus (ASFV) is a highly complex enveloped DNA virus encoding more than 150 open reading frames. The antigenicity of ASFV is still unclear at present. In this study, 35 proteins of ASFV were expressed by Escherichia coli, and ELISA was developed for the detection of antibodies against these proteins. p30, p54, and p22 were presented as the major antigens of ASFV, positively reacting with all five clinical ASFV-positive pig sera, and 10 pig sera experimentally infected by ASFV. Five proteins (pB475L, pC129R, pE199L, pE184L, and pK145R) reacted well with ASFV-positive sera. The p30 induced a rapid and strong antibody immune response during ASFV infection. These results will promote the development of subunit vaccines and serum diagnostic methods against ASFV

Crystal structure of diisopropylaminium dichloroacetate

In the title compound, C6H16N+·C2HCl2O2−, the cation exhibits non-crystallographic C2 symmetry. In the crystal, the components are linked by N—H...O and C—H...O hydrogen bonds into chains propagating along [010]

7-{4-[(1,3-Benzodioxol-5-yl)methyl]piperazin-1-yl}-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid

In the title structure, C25H24FN3O5, a strong intramolecular O—H...O hydrogen bond is present between the carboxy group at the 3-position and the carbonyl group at the 4-position. In the crystal, molecules are held together by weak C—H...O, C—H...F and π–π [centroid–centroid distance 3.6080 (8) Å] interactions. The 1,4-dihydroquinoline ring and cyclopropyl group are not in the same plane, making an interplanar angle of 57.52 (8)°

N-(2,6-Dimethoxypyridin-3-yl)-9-methyl-9H-carbazole-3-sulfonamide

In the title compound, C20H19N3O4S, a novel tubulin ligand active against human cancer, the dihedral angle between the pyridine ring and the carbazole ring system is 42.87 (10)°. In the crystal, the molecules are held together by N—H...O and C—H...O hydrogen bonds into layers, which are assembled into a three-dimensional network via π–π stacking interactions between inversion-related pyridine rings, with centroid–centroid distances of 3.5101 (12) Å

Halogenase-Targeted Genome Mining Leads to the Discovery of (±) Pestalachlorides A1a, A2a, and Their Atropisomers

Genome mining has become an important tool for discovering new natural products and identifying the cryptic biosynthesis gene clusters. Here, we utilized the flavin-dependent halogenase GedL as the probe in combination with characteristic halogen isotope patterns to mine new halogenated secondary metabolites from our in-house fungal database. As a result, two pairs of atropisomers, pestalachlorides A1a (1a)/A1b (1b) and A2a (2a)/A2b (2b), along with known compounds pestalachloride A (3) and SB87-H (4), were identified from Pestalotiopsis rhododendri LF-19-12. A plausible biosynthetic assembly line for pestalachlorides involving a putative free-standing phenol flavin-dependent halogenase was proposed based on bioinformatics analysis. Pestalachlorides exhibited antibacterial activity against sensitive and drug-resistant S. aureus and E. faecium with MIC values ranging from 4 μg/mL to 32 μg/mL. This study indicates that halogenase-targeted genome mining is an efficient strategy for discovering halogenated compounds and their corresponding halogenases

Application of Transmission Raman Spectroscopy in Combination with Partial Least-Squares (PLS) for the Fast Quantification of Paracetamol

In recent years, transmission Raman spectroscopy (TRS) has emerged as a potent new tool for rapid, nondestructive quantitation in pharmaceutical manufacturing. In order to expand the applicability of TRS and enhance its use in product quality monitoring during drug production, we aimed, in the present study, to apply partial least-squares (PLS) approaches to build a model consisting of 150 handmade tablets and covering 15 levels through the use of a multifactor orthogonal design of experiment (DOE), which was used to predict concentrations of validation tablets made by hand. The difference between results according to HPLC and TRS were negligible. The model was used to predict the active pharmaceutical ingredient (API) content in four random commercial paracetamol tablets, and corrected with the spectra of the commercial tablets to obtain four corresponding models. The results show that the content relative error in the model’s predictions after correction with commercially available tablets was significantly lower than that before correction. The corrected model was used to make predictions for 20 tablets from the brand Panadol. Compared with the HPLC results, the prediction relative error was basically less than 4.00%, and the relative standard deviation (RSD) of the content was 0.86%

Strikingly distinctive NH3-SCR behavior over Cu-SSZ-13 in the presence of NO2.

Commercial Cu-exchanged small-pore SSZ-13 (Cu-SSZ-13) zeolite catalysts are highly active for the standard selective catalytic reduction (SCR) of NO with NH3. However, their activity is unexpectedly inhibited in the presence of NO2 at low temperatures. This is strikingly distinct from the NO2-accelerated NOx conversion over other typical SCR catalyst systems. Here, we combine kinetic experiments, in situ X-ray absorption spectroscopy, and density functional theory (DFT) calculations to obtain direct evidence that under reaction conditions, strong oxidation by NO2 forces Cu ions to exist mainly as CuII species (fw-Cu2+ and NH3-solvated CuII with high CNs), which impedes the mobility of Cu species. The SCR reaction occurring at these CuII sites with weak mobility shows a higher energy barrier than that of the standard SCR reaction on dynamic binuclear sites. Moreover, the NO2-involved SCR reaction tends to occur at the Brønsted acid sites (BASs) rather than the CuII sites. This work clearly explains the strikingly distinctive selective catalytic behavior in this zeolite system