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Road extraction is a sub-domain of remote sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. Convolutional neural networks (CNNs), especially the DeepLab series known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects’ features, address some of these challenges caused by the varying nature of roads. The present work proposes the utilization of DeepLabV3+, the latest version of the DeepLab series, by introducing an innovative Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) module and integrating it in the place of the conventional Atrous Spatial Pyramid Pooling (ASPP) module. This modification enhances the extraction of complex road structures from satellite images. This study hypothesizes that the integration of DenseDDSSPP with a CNN backbone network and a Squeeze-and-Excitation block will generate an efficient dense feature map by focusing on relevant features, leading to more precise and accurate road extraction from remote sensing images. The Results Section presents a comparison of our model’s performance against state-of-the-art models, demonstrating better results that highlight the effectiveness and success of the proposed approach. 

Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as finite element methods, with solutions over the spatial domain. However, obtaining these solutions are often prohibitively costly, limiting the feasibility of exploring parameters in PDEs. In this article, we propose an efficient emulator that simultaneously predicts the solutions over the spatial domain, with theoretical justification of its uncertainty quantification. The novelty of the proposed method lies in the incorporation of the mesh node coordinates into the statistical model. In particular, the proposed method segments the mesh nodes into multiple clusters via a Dirichlet process prior and fits Gaussian process models with the same hyperparameters in each of them. Most importantly, by revealing the underlying clustering structures, the proposed method can provide valuable insights into qualitative features of the resulting dynamics that can be used to guide further investigations. Real examples are demonstrated to show that our proposed method has smaller prediction errors than its main competitors, with competitive computation time, and identifies interesting clusters of mesh nodes that possess physical significance, such as satisfying boundary conditions. An R package for the proposed methodology is provided in an open repository. 

Developmental plasticity is critical for plants to adapt to constantly changing environments. Plant root hairs display dramatic plasticity under different environments and therefore play crucial roles in defense against environmental stressors. Here, we report the isolation of an Arabidopsis mutant, salinity over-sensitive mutant 1-1 (som1-1), also exhibiting root hair developmental defects. Map-based cloning and allelic analyses confirmed that som1-1 is a new mutant allele of SPIRRIG (SPI), which encodes a Beige and Chediak Higashi (BEACH) domain-containing protein. SPI has been reported to facilitate actin dependent root hair development by temporally and spatially regulating the expression of BRICK1 (BRK1), a subunit of the SCAR/WAVE actin nucleating promoting complex. Our living cell imaging examinations revealed that salt stress induces an altered actin organization in root hair that mimics those in the spi mutant, implying SPI may respond to salt stress induced root hair plasticity by modulating actin cytoskeleton organization. Furthermore, we found BRK1 is also involved in root hair developmental change under salt stress, and overexpression of BRK1 resulted in root hairs over-sensitive to salt stress as those in spi mutant. Moreover, based on biochemical analyses, we found BRK1 is unstable and SPI mediates BRK1 stability. Functional loss of SPI results in the accumulation of steady-state of BRK1. 

With advances in high-throughput technology, molecular disease subtyping by high-dimensional omics data has been recognized as an effective approach for identifying subtypes of complex diseases with distinct disease mechanisms and prognoses. Conventional cluster analysis takes omics data as input and generates patient clusters with similar gene expression pattern. The omics data, however, usually contain multifaceted cluster structures that can be defined by different sets of genes. If the gene set associated with irrelevant clinical variables (e.g., sex or age) dominates the clustering process, the resulting clusters may not capture clinically meaningful disease subtypes. This motivates the development of a clustering framework with guidance from a prespecified disease outcome, such as lung function measurement or survival, in this paper. We propose two disease subtyping methods by omics data with outcome guidance using a generative model or a weighted joint likelihood. Both methods connect an outcome association model and a disease subtyping model by a latent variable of cluster labels. Compared to the generative model, weighted joint likelihood contains a data-driven weight parameter to balance the likelihood contributions from outcome association and gene cluster separation, which improves generalizability in independent validation but requires heavier computing. Extensive simulations and two real applications in lung disease and triple-negative breast cancer demonstrate superior disease subtyping performance of the outcome-guided clustering methods in terms of disease subtyping accuracy, gene selection and outcome association. Unlike existing clustering methods, the outcome-guided disease subtyping framework creates a new precision medicine paradigm to directly identify patient subgroups with clinical association. 

SUMMARY Cell differentiation and morphogenesis are crucial for the establishment of diverse cell types and organs in multicellular organisms. Trichome cells offer an excellent paradigm for dissecting the regulatory mechanisms of plant cell differentiation and morphogenesis due to their unique growth characteristics. Here, we report the isolation of an Arabidopsis mutant,aberrantlybranchedtrichome 3–1(abt3‐1), with a reduced trichome branching phenotype. Positional cloning and molecular complementation experiments confirmed thatabt3‐1is a new mutant allele ofAuxin resistant 1(AXR1), which encodes the N‐terminal half of ubiquitin‐activating enzyme E1 and functions in auxin signaling pathway. Meanwhile, we found that transgenic plants expressing constitutively active version ofROP2(CA‐ROP2) caused a reduction of trichome branches, resembling that ofabt3‐1. ROP2 is a member of Rho GTPase of plants (ROP) family, serving as versatile signaling switches involved in a range of cellular and developmental processes. Our genetic and biochemical analyses showedAXR1genetically interacted withROP2and mediated ROP2 protein stability. The loss ofAXR1aggravated the trichome defects ofCA‐ROP2and induced the accumulation of steady‐state ROP2. Consistently, elevatedAXR1expression levels suppressedROP2expression and partially rescued trichome branching defects inCA‐ROP2plants. Together, our results presented a new mutant allele ofAXR1, uncovered the effects ofAXR1andROP2during trichome development, and revealed a pathway ofROP2‐mediated regulation of plant cell morphogenesis in Arabidopsis. 

Distributed denial-of-service (DDoS) attack is a malicious cybersecurity attack that has become a global threat. Machine learning (ML) as an advanced technology has been proven to be an effective way against DDoS attacks. Feature selection is a crucial step in ML, and researchers have put endless efforts to mitigate the “Curse of Dimensionality”. Feature selection is also causing problems to ML models, such as a decrease in prediction accuracy. Four supervised classification techniques, namely, Decision Tree (DT), k-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF), are tested using mutual information score ranking to study the necessity of feature selection in DDoS detection. 

Abstract The Arabidopsis (Arabidopsis thaliana) TRANSPARENT TESTA GLABRA2 (TTG2) gene encodes a WRKY transcription factor that regulates a range of development events like trichome, seed coat, and atrichoblast formation. Loss-of-function of TTG2 was previously shown to reduce or eliminate trichome specification and branching. Here, we report the identification of an allele of TTG2, ttg2-6. In contrast to the ttg2 mutants described before, ttg2-6 displayed unique trichome phenotypes. Some ttg2-6 mutant trichomes were hyper-branched, whereas others were hypo-branched, distorted, or clustered. Further, we found that in addition to specifically activating R3 MYB transcription factor TRIPTYCHON (TRY) to modulate trichome specification, TTG2 also integrated cytoskeletal signaling to regulate trichome morphogenesis. The ttg2-6 trichomes displayed aberrant cortical microtubules (cMTs) and actin filaments (F-actin) configurations. Moreover, genetic and biochemical analyses showed that TTG2 could directly bind to the promoter and regulate the expression of BRICK1 (BRK1), which encodes a subunit of the actin nucleation promoting complex suppressor of cyclic AMP repressor (SCAR)/Wiskott–Aldrich syndrome protein family verprolin homologous protein (WAVE). Collectively, taking advantage of ttg2-6, we uncovered a function for TTG2 in facilitating cMTs and F-actin cytoskeleton-dependent trichome development, providing insight into cellular signaling events downstream of the core transcriptional regulation during trichome development in Arabidopsis.