GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://sites.google.com/view/graspgpt/.Comment: 15 pages, 8 figure

TSPAN8 promotes cancer cell stemness via activation of sonic Hedgehog signaling

Cancer stem cells (CSCs) represent a major source of treatment resistance and tumor progression. However, regulation of CSCs stemness is not entirely understood. Here, we report that TSPAN8 expression is upregulated in breast CSCs, promotes the expression of the stemness gene NANOG, OCT4, and ALDHA1, and correlates with therapeutic resistance. Mechanistically, TSPAN8 interacts with PTCH1 and inhibits the degradation of the SHH/PTCH1 complex through recruitment of deubiquitinating enzyme ATXN3. This results in the translocation of SMO to cilia, downstream gene expression, resistance of CSCs to chemotherapeutic agents, and enhances tumor formation in mice. Accordingly, expression levels of TSPAN8, PTCH1, SHH, and ATXN3 are positively correlated in human breast cancer specimens, and high TSPAN8 and ATXN3 expression levels correlate with poor prognosis. These findings reveal a molecular basis of TSPAN8-enhanced Sonic Hedgehog signaling and highlight a role for TSPAN8 in promoting cancer stemness

Global land surface temperature influenced by vegetation cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 �g/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72�N and 48�S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

WPPG Net: A Non-contact Video Based Heart Rate Extraction Network Framework with Compatible Training Capability

Our facial skin presents subtle color change known as remote Photoplethysmography (rPPG) signal, from which we could extract the heart rate of the subject. Recently many deep learning methods and related datasets on rPPG signal extraction are proposed. However, because of the time consumption blood flowing through our body and other factors, label waves such as BVP signals have uncertain delays with real rPPG signals in some datasets, which results in the difficulty on training of networks which output predicted rPPG waves directly. In this paper, by analyzing the common characteristics on rhythm and periodicity of rPPG signals and label waves, we propose a whole set of training methodology which wraps these networks so that they could remain efficient when be trained at the presence of frequent uncertain delay in datasets and gain more precise and robust heart rate prediction results than other delay-free rPPG extraction methods

A Numerical Investigation of Enhancing Microfluidic Heterogeneous Immunoassay on Bipolar Electrodes Driven by Induced-Charge Electroosmosis in Rotating Electric Fields

A unique approach is proposed to boost on-chip immuno-sensors, for instance, immunoassays, wherein an antibody immobilized on the walls of a microfluidic channel binds specifically to an antigen suspended freely within a working fluid. The performance of these sensors can be limited in both susceptibility and response speed by the slow diffusive mass transfer of the analyte to the binding surface. Under appropriate conditions, the binding reaction of these heterogeneous immuno-assays may be enhanced by electroconvective stirring driven by external AC electric fields to accelerate the translating motion of antigens towards immobilized antibodies. To be specific, the phenomenon of induced-charge electroosmosis in a rotating electric field (ROT-ICEO) is fully utilized to stir analyte in the vicinity of the functionalized surface of an ideally polarizable floating electrode in all directions inside a tri-dimensional space. ROT-ICEO appears as a consequence of the action of a circularly-polarized traveling wave signal on its own induced rotary Debye screening charge within a bipolar induced double layer formed on the central floating electrode, and thereby the pertinent electrokinetic streamlines exhibit a radially converging pattern that greatly facilitates the convective transport of receptor towards the ligand. Numerical simulations indicate that ROT-ICEO can enhance the antigen–antibody binding reaction more effectively than convectional nonlinear electroosmosis driven by standing wave AC signals. The effectiveness of ROT-ICEO micro-stirring is strongly dependent on the Damkohler number as well as the Peclet number if the antigens are carried by a continuous base flow. Our results provide a promising way for achieving a highly efficient heterogeneous immunoassay in modern micro-total-analytical systems

DataSheet1_A novel angiogenesis-based molecular signature related to prognosis and tumor immune interactions of pancreatic cancer.zip

Angiogenesis, a hallmark of cancer, is related to prognosis, tumor progression, and treatment response. Nevertheless, the correlation of angiogenesis-based molecular signature with clinical outcome and immune cell infiltration has not been thoroughly studied in pancreatic cancer. In this study, multiple bioinformatics methods were combined to evaluate prognosis, immune cell infiltration, and the alterations of angiogenesis-related genes (ARGs) in PC samples, and further establish a novel angiogenesis-related gene signature. Moreover, the protein and mRNA expression levels of four angiogenesis risk genes were determined by Human Protein Atlas (HPA) database and qPCR analysis, respectively. Here, we recognized two distinct angiogenesis subtypes and two gene subtypes, and revealed the critical roles of ARGs in the tumor immune microenvironment (TIME), clinical features, and prognosis. Consequently, we established an ARGs score to predict prognosis and therapeutic response of PC patients, and validated its robust predictive ability. Additionally, the ARGs score was markedly associated with clinical outcomes, tumor mutation burden (TMB), and chemotherapeutic drug sensitivity. In brief, our findings imply that the ARGs score is a robust prognostic indicator and may contribute to the development of effective individualized therapies for PC.</p