3D-LLM: Injecting the 3D World into Large Language Models

Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs. Project Page: : https://vis-www.cs.umass.edu/3dllm/.Comment: Project Page: : https://vis-www.cs.umass.edu/3dllm

Corrigendum:Epigenetic inactivation of the CpG demethylase TET1 as a DNA methylation feedback loop in human cancers

Scientific Reports 6: Article number: 26591; published online: 26 May 2016; updated: 06 October 2016. This Article contains errors in Figure 2D where the Hodgkin lymphoma ‘TET1-MSP’ methylated and unmethylated MSP bands are incorrect. The correct Figure 2D appears below as Figure 1.</jats:p

Mechanisms of receptor tyrosine kinase activation in cancer

Abstract Receptor tyrosine kinases (RTKs) play an important role in a variety of cellular processes including growth, motility, differentiation, and metabolism. As such, dysregulation of RTK signaling leads to an assortment of human diseases, most notably, cancers. Recent large-scale genomic studies have revealed the presence of various alterations in the genes encoding RTKs such as EGFR, HER2/ErbB2, and MET, amongst many others. Abnormal RTK activation in human cancers is mediated by four principal mechanisms: gain-of-function mutations, genomic amplification, chromosomal rearrangements, and / or autocrine activation. In this manuscript, we review the processes whereby RTKs are activated under normal physiological conditions and discuss several mechanisms whereby RTKs can be aberrantly activated in human cancers. Understanding of these mechanisms has important implications for selection of anti-cancer therapies

A weight vector generation based on normal distribution for preference-based multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkIn researching multi-objective evolutionary algorithms (MOEAs), the decision-maker (DM) may not need the entire Pareto optimal front searched and may only be interested in the region of interest (ROI). Most existing preference-based research focuses on determining the location of the ROI and controlling its size. Those research typically ignores the preference information provided by the DM when solving problems. Since the convergence region and diversity of the population are determined according to the DM’s preference information, so we propose a preference-based MOEA that uses a normal distribution (ND) to generate a weight vector, called MOEA/D-ND. The generation of the weight vector uses the DM’s preference information to guide the solution to converge to the vicinity of the preference information. Because the randomness of the normal distribution can lead to a loss of diversity, an angle-based niche selection strategy is adopted. This strategy prevents the population from falling into a local optimum during the search process. Although the reference vector generated by MOEA/D-ND using the normal distribution will make the final solution set no longer uniformly distributed in the ROI, still, the closer region to the reference point, the more solution sets are obtained. The experimental results show that this algorithm has advantages in various benchmark problems with 2 to 15 goals

Structure-Guided Strategies of Targeted Therapies for Patients with <i>EGFR</i>-Mutant Non–Small Cell Lung Cancer

Oncogenic mutations within the EGFR kinase domain are well-established driver mutations in non–small cell lung cancer (NSCLC). Small-molecule tyrosine kinase inhibitors (TKIs) specifically targeting these mutations have improved treatment outcomes for patients with this subtype of NSCLC. The selectivity of these targeted agents is based on the location of the mutations within the exons of the EGFR gene, and grouping mutations based on structural similarities has proved a useful tool for conceptualizing the heterogeneity of TKI response. Structure-based analysis of EGFR mutations has influenced TKI development, and improved structural understanding will inform continued therapeutic development and further improve patient outcomes. In this review, we summarize recent progress on targeted therapy strategies for patients with EGFR-mutant NSCLC based on structure and function analysis

Improved Optical and Electrochromic Properties of NiOx Films by Low-Temperature Spin-Coating Method Based on NiOx Nanoparticles

Solution approaches to NiOx films for electrochromic applications are problematic due to the need of an additional high-temperature annealing treatment step in inert gas. In this study, nanostructured NiOx powder with grain size of about 10.1 nm was synthesized for fabrication of NiOx films for electrochromic application. Non-toxic dispersants of isopropanol and deionized water were used and the whole process was carried out in air. The effects of the number of spin-coating layers, annealing temperature, and the volume ratios of isopropanol to deionized water were systematically investigated. Large transmittance change of 62.3% at 550 nm, high coloration efficiency (42.8 cm2/C), rapid switching time (coloring time is 4 s, bleaching time is 3 s), and good stability were achieved in the optimized NiOx film. The optimized process only required a low processing temperature of 150 &deg;C in air with spin-coating three times and 1:2 volume ratio of isopropanol to deionized water. Finally, good cycle durability of up to 2000 cycles without obvious degradation was demonstrated by cyclic voltammetry tests in a LiClO4/propylene carbonate electrolyte. This study provides a simple and effective approach for fabrication of NiOx films at low temperature in air, which is attractive for further commercialization of electrochromic devices

Structure-Guided Strategies of Targeted Therapies for Patients with EGFR-Mutant Non&ndash;Small Cell Lung Cancer

Oncogenic mutations within the EGFR kinase domain are well-established driver mutations in non&ndash;small cell lung cancer (NSCLC). Small-molecule tyrosine kinase inhibitors (TKIs) specifically targeting these mutations have improved treatment outcomes for patients with this subtype of NSCLC. The selectivity of these targeted agents is based on the location of the mutations within the exons of the EGFR gene, and grouping mutations based on structural similarities has proved a useful tool for conceptualizing the heterogeneity of TKI response. Structure-based analysis of EGFR mutations has influenced TKI development, and improved structural understanding will inform continued therapeutic development and further improve patient outcomes. In this review, we summarize recent progress on targeted therapy strategies for patients with EGFR-mutant NSCLC based on structure and function analysis

Determination and Correlation of Solubility of Quetiapine Fumarate in Nine Pure Solvents and Two Aqueous Binary Solvents

A gravimetric method was used to determine the solubility of quetiapine fumarate (QF) in nine pure solvents and two aqueous binary solvents (water + methanol/ethanol) at different temperatures from 283.15 to 323.15 K. The solubility of QF increases with the increase of temperature in nine pure solvents, and it is in the order DMF > methanol > ethanol >1-butanol > isopropyl alcohol > (acetone > ethyl acetate > isobutyl alcohol) > water at low temperature, and in the order DMF > methanol > ethanol >1-butanol > isopropyl alcohol > (acetone > isobutyl alcohol > ethyl acetate) > water at relatively high temperature at a given temperature. The solubility of QF in the binary solvents also shows temperature dependence, while at a given temperature the solubility is mainly influenced by the solvent composition with the presence of maximum, reflecting cosolvency. Also the solubility of QF increases with the increase of temperature in binary solvents in a given composition. The Hansen solubility parameters were used to explain the cosolvency and maxima shift, confirming that for large values (>25 MPa^1/2) of solute, the solubility shows a peak in the range of 35 to 31 MPa^1/2 of solubility parameters of alcohol mixtures. The experimental solubility of QF in pure and binary solvents is well correlated by modified Apelblat equation, the nonrandom two-liquid model, and the CNIBS/R-K equation, respectively