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

CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model

Traditional alignment-based methods meet serious challenges in genome sequence comparison and phylogeny reconstruction due to their high computational complexity. Here, we propose a new alignment-free method to analyze the phylogenetic relationships (classification) among species. In our method, the dynamical language (DL) model and the chaos game representation (CGR) method are used to characterize the frequency information and the context information of k-mers in a sequence, respectively. Then for each DNA sequence or protein sequence in a dataset, our method converts the sequence into a feature vector that represents the sequence information based on CGR weighted by the DL model to infer phylogenetic relationships. We name our method CGRWDL. Its performance was tested on both DNA and protein sequences of 8 datasets of viruses to construct the phylogenetic trees. We compared the Robinson-Foulds (RF) distance between the phylogenetic tree constructed by CGRWDL and the reference tree by other advanced methods for each dataset. The results show that the phylogenetic trees constructed by CGRWDL can accurately classify the viruses, and the RF scores between the trees and the reference trees are smaller than that with other methods

Boundary domain genes were recruited to suppress bract growth and promote branching in maize

Grass inflorescence development is diverse and complex and involves sophisticated but poorly understood interactions of genes regulating branch determinacy and leaf growth. Here, we use a combination of transcript profiling and genetic and phylogenetic analyses to investigate tasselsheath1 (tsh1) and tsh4, two maize genes that simultaneously suppress inflorescence leaf growth and promote branching. We identify a regulatory network of inflorescence leaf suppression that involves the phase change gene tsh4 upstream of tsh1 and the ligule identity gene liguleless2 (lg2). We also find that a series of duplications in the tsh1 gene lineage facilitated its shift from boundary domain in nongrasses to suppressed inflorescence leaves of grasses. Collectively, these results suggest that the boundary domain genes tsh1 and lg2 were recruited to inflorescence leaves where they suppress growth and regulate a nonautonomous signaling center that promotes inflorescence branching, an important component of yield in cereal grasses

Increased intraocular inflammation in retinal vein occlusion is independent of circulating immune mediators and is involved in retinal oedema

We aim to understand the link between systemic and intraocular levels of inflammatory mediators in treatment-naïve retinal vein occlusion (RVO) patients, and the relationship between inflammatory mediators and retinal pathologies. Twenty inflammatory mediators were measured in this study, including IL-17E, Flt-3 L, IL-3, IL-8, IL-33, MIP-3β, MIP-1α, GRO β, PD-L1, CD40L, IFN-β, G-CSF, Granzyme B, TRAIL, EGF, PDGF-AA, PDGF-AB/BB, TGF-α, VEGF, and FGFβ. RVO patients had significantly higher levels of Flt-3 L, IL-8, MIP-3β, GROβ, and VEGF, but lower levels of EGF in the aqueous humor than cataract controls. The levels of Flt-3 L, IL-3, IL-33, MIP-1α, PD-L1, CD40 L, G-CSF, TRAIL, PDGF-AB/BB, TGF-α, and VEGF were significantly higher in CRVO than in BRVO. KEGG pathway enrichment revealed that these mediators affected the PI3K-Akt, Ras, MAPK, and Jak/STAT signaling pathways. Protein–Protein Interaction (PPI) analysis showed that VEGF is the upstream cytokine that influences IL-8, G-CSF, and IL-33 in RVO. In the plasma, the level of GROβ was lower in RVO than in controls and no alterations were observed in other mediators. Retinal thickness [including central retinal thickness (CRT) and inner limiting membrane to inner plexiform layer (ILM-IPL)] positively correlated with the intraocular levels of Flt-3 L, IL-33, GROβ, PD-L1, G-CSF, and TGF-α. The size of the foveal avascular zone positively correlated with systemic factors, including the plasma levels of IL-17E, IL-33, INF-β, GROβ, Granzyme B, and FGFβ and circulating high/low-density lipids and total cholesterols. Our results suggest that intraocular inflammation in RVO is driven primarily by local factors but not circulating immune mediators. Intraocular inflammation may promote macular oedema through the PI3K-Akt, Ras, MAPK, and Jak/STAT signaling pathways in RVO. Systemic factors, including cytokines and lipid levels may be involved in retinal microvascular remodeling

Nuciferine induces autophagy to relieve vascular cell adhesion molecule 1 activation via repressing the Akt/mTOR/AP1 signal pathway in the vascular endothelium

Pro-inflammatory factor-associated vascular cell adhesion molecule 1 (VCAM1) activation initiates cardiovascular events. This study aimed to explore the protective role of nuciferine on TNFα-induced VCAM1 activation. Nuciferine was administrated to both high-fat diet (HFD)-fed mice and the TNFα-exposed human vascular endothelial cell line. VCAM1 expression and further potential mechanism(s) were explored. Our data revealed that nuciferine intervention alleviated VCAM1 activation in response to both high-fat diet and TNFα exposure, and this protective effect was closely associated with autophagy activation since inhibiting autophagy by either genetic or pharmaceutical approaches blocked the beneficial role of nuciferine. Mechanistical studies revealed that Akt/mTOR inhibition, rather than AMPK, SIRT1, and p38 signal pathways, contributed to nuciferine-activated autophagy, which further ameliorated TNFα-induced VCAM1 via repressing AP1 activation, independent of transcriptional regulation by IRF1, p65, SP1, and GATA6. Collectively, our data uncovered a novel biological function for nuciferine in protecting VCAM1 activation, implying its potential application in improving cardiovascular events

Hot spot prediction in protein-protein interactions by an ensemble system

© 2018 The Author(s). Background: Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features. Results: This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance. Conclusion: The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction. Availability:http://deeplearner.ahu.edu.cn/web/HotspotEL.htm

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

Gene Expression in Transformed Lymphocytes Reveals Variation in Endomembrane and HLA Pathways Modifying Cystic Fibrosis Pulmonary Phenotypes

Variation in cystic fibrosis (CF) phenotypes, including lung disease severity, age of onset of persistent Pseudomonas aeruginosa (P. aeruginosa) lung infection, and presence of meconium ileus (MI), has been partially explained by genome-wide association studies (GWASs). It is not expected that GWASs alone are sufficiently powered to uncover all heritable traits associated with CF phenotypic diversity. Therefore, we utilized gene expression association from lymphoblastoid cells lines from 754 p.Phe508del CF-affected homozygous individuals to identify genes and pathways. LPAR6, a G protein coupled receptor, associated with lung disease severity (false discovery rate q value = 0.0006). Additional pathway analyses, utilizing a stringent permutation-based approach, identified unique signals for all three phenotypes. Pathways associated with lung disease severity were annotated in three broad categories: (1) endomembrane function, containing p.Phe508del processing genes, providing evidence of the importance of p.Phe508del processing to explain lung phenotype variation; (2) HLA class I genes, extending previous GWAS findings in the HLA region; and (3) endoplasmic reticulum stress response genes. Expression pathways associated with lung disease were concordant for some endosome and HLA pathways, with pathways identified using GWAS associations from 1,978 CF-affected individuals. Pathways associated with age of onset of persistent P. aeruginosa infection were enriched for HLA class II genes, and those associated with MI were related to oxidative phosphorylation. Formal testing demonstrated that genes showing differential expression associated with lung disease severity were enriched for heritable genetic variation and expression quantitative traits. Gene expression provided a powerful tool to identify unrecognized heritable variation, complementing ongoing GWASs in this rare disease