VDD: Varied Drone Dataset for Semantic Segmentation

Semantic segmentation of drone images is critical to many aerial vision tasks as it provides essential semantic details that can compensate for the lack of depth information from monocular cameras. However, maintaining high accuracy of semantic segmentation models for drones requires diverse, large-scale, and high-resolution datasets, which are rare in the field of aerial image processing. Existing datasets are typically small and focus primarily on urban scenes, neglecting rural and industrial areas. Models trained on such datasets are not sufficiently equipped to handle the variety of inputs seen in drone imagery. In the VDD-Varied Drone Dataset, we offer a large-scale and densely labeled dataset comprising 400 high-resolution images that feature carefully chosen scenes, camera angles, and varied light and weather conditions. Furthermore, we have adapted existing drone datasets to conform to our annotation standards and integrated them with VDD to create a dataset 1.5 times the size of fine annotation of Cityscapes. We have developed a novel DeepLabT model, which combines CNN and Transformer backbones, to provide a reliable baseline for semantic segmentation in drone imagery. Our experiments indicate that DeepLabT performs admirably on VDD and other drone datasets. We expect that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks. VDD is freely available on our website at https://vddvdd.com

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients’ visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment.Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay.Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments.Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy

Hollow mesoporous silica nanoparticles for intracellular delivery of fluorescent dye

In this study, hollow mesoporous silica nanoparticles (HMSNs) were synthesized using the sol-gel/emulsion approach and its potential application in drug delivery was assessed. The HMSNs were characterized, by transmission electron microscopy (TEM), Scanning Electron Microscopy (SEM), nitrogen adsorption/desorption and Brunauer-Emmett-Teller (BET), to have a mesoporous layer on its surface, with an average pore diameter of about 2 nm and a surface area of 880 m2/g. Fluorescein isothiocyanate (FITC) loaded into these HMSNs was used as a model platform to assess its efficacy as a drug delivery tool. Its release kinetic study revealed a sequential release of FITC from the HMSNs for over a period of one week when soaked in inorganic solution, while a burst release kinetic of the dye was observed just within a few hours of soaking in organic solution. These FITC-loaded HMSNs was also found capable to be internalized by live human cervical cancer cells (HeLa), wherein it was quickly released into the cytoplasm within a short period of time after intracellular uptake. We envision that these HMSNs, with large pores and high efficacy to adsorb chemicals such as the fluorescent dye FITC, could serve as a delivery vehicle for controlled release of chemicals administered into live cells, opening potential to a diverse range of applications including drug storage and release as well as metabolic manipulation of cells

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

FTO genetic variants, dietary intake and body mass index: insights from 177 330 individuals

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177 330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m2, P = 1.9 × 10−105), and all participants (0.30 [0.30, 0.35] kg/m2, P = 3.6 × 10−107). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10−16), and relative weak associations with lower total energy intake (−6.4 [−10.1, −2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (−0.07 [−0.11, −0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10−9) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposit

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.National Natural Science Foundation of China [32022078]; National Key R&D Program of China [2022YFF1000900]; Local Innovative and Research Teams Project of Guangdong Province [2019BT02N630]; China Agriculture Research System [CARS-35]. Funding for open access charge: National Natural Science Foundation of China [32022078].Peer reviewe