AutoFocusFormer: Image Segmentation off the Grid

Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small objects are represented in very few spatial locations, leading to worse results in tasks such as segmentation. Intuitively, retaining more pixels representing small objects during downsampling helps to preserve important information. To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task. Since adaptive downsampling generates a set of pixels irregularly distributed on the image plane, we abandon the classic grid structure. Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) improves significantly over baseline models of similar sizes.Comment: CVPR 202

Metabolic Engineering to Improve Docosahexaenoic Acid Production in Marine Protist Aurantiochytrium sp. by Disrupting 2,4-Dienoyl-CoA Reductase

Docosahexaenoic acid (DHA) has attracted attention from researchers because of its pharmacological and nutritional importance. Currently, DHA production costs are high due to fermentation inefficiency; however, improving DHA yield by metabolic engineering in thraustochytrids is one approach to reduce these costs. In this study, a high-yielding (53.97% of total fatty acids) DHA production strain was constructed by disrupting polyunsaturated fatty acid beta-oxidation via knockout of the 2,4-dienyl-CoA reductase (DECR) gene (KO strain) in Aurantiochytrium sp. Slight differences in cell growth was observed in the wild-type and transformants (OE and KO), with cell concentrations in stationary of 2.65×106, 2.36×106 and 2.56×106 cells mL-1 respectively. Impressively, the KO strain yielded 21.62% more neutral lipids and 57.34% greater DHA production; moreover, the opposite was observed when overexpressing DECR (OE strain), with significant decreases of 30.49% and 64.61%, respectively. Furthermore, the KO strain showed a prolonged DHA production period with a sustainable increase from 63 to 90 h (170.03 to 203.27 mg g−1 DCW), while that of the wildtype strain decreased significantly from 150.58 to 140.10 mg g−1 DCW. This new approach provides an advanced proxy for the construction of sustainable DHA production strains for industrial purposes and deepens our understanding of the metabolic pathways of Aurantiochytrium sp

Ciliary parathyroid hormone signaling activates transforming growth factor-β to maintain intervertebral disc homeostasis during aging

© 2018 The Author(s). Degenerative disc disease (DDD) is associated with intervertebral disc degeneration of spinal instability. Here, we report that the cilia of nucleus pulposus (NP) cells mediate mechanotransduction to maintain anabolic activity in the discs. We found that mechanical stress promotes transport of parathyroid hormone 1 receptor (PTH1R) to the cilia and enhances parathyroid hormone (PTH) signaling in NP cells. PTH induces transcription of integrin αvβ6 to activate the transforming growth factor (TGF)-β-connective tissue growth factor (CCN2)-matrix proteins signaling cascade. Intermittent injection of PTH (iPTH) effectively attenuates disc degeneration of aged mice by direct signaling through NP cells, specifically improving intervertebral disc height and volume by increasing levels of TGF-β activity, CCN2, and aggrecan. PTH1R is expressed in both mouse and human NP cells. Importantly, knockout PTH1R or cilia in the NP cells results in significant disc degeneration and blunts the effect of PTH on attenuation of aged discs. Thus, mechanical stress-induced transport of PTH1R to the cilia enhances PTH signaling, which helps maintain intervertebral disc homeostasis, particularly during aging, indicating therapeutic potential of iPTH for DDD

Defining Global Gene Expression Changes of the Hypothalamic-Pituitary-Gonadal Axis in Female sGnRH-Antisense Transgenic Common Carp (Cyprinus carpio)

BACKGROUND: The hypothalamic-pituitary-gonadal (HPG) axis is critical in the development and regulation of reproduction in fish. The inhibition of neuropeptide gonadotropin-releasing hormone (GnRH) expression may diminish or severely hamper gonadal development due to it being the key regulator of the axis, and then provide a model for the comprehensive study of the expression patterns of genes with respect to the fish reproductive system. METHODOLOGY/PRINCIPAL FINDINGS: In a previous study we injected 342 fertilized eggs from the common carp (Cyprinus carpio) with a gene construct that expressed antisense sGnRH. Four years later, we found a total of 38 transgenic fish with abnormal or missing gonads. From this group we selected the 12 sterile females with abnormal ovaries in which we combined suppression subtractive hybridization (SSH) and cDNA microarray analysis to define changes in gene expression of the HPG axis in the present study. As a result, nine, 28, and 212 genes were separately identified as being differentially expressed in hypothalamus, pituitary, and ovary, of which 87 genes were novel. The number of down- and up-regulated genes was five and four (hypothalamus), 16 and 12 (pituitary), 119 and 93 (ovary), respectively. Functional analyses showed that these genes involved in several biological processes, such as biosynthesis, organogenesis, metabolism pathways, immune systems, transport links, and apoptosis. Within these categories, significant genes for neuropeptides, gonadotropins, metabolic, oogenesis and inflammatory factors were identified. CONCLUSIONS/SIGNIFICANCE: This study indicated the progressive scaling-up effect of hypothalamic sGnRH antisense on the pituitary and ovary receptors of female carp and provided comprehensive data with respect to global changes in gene expression throughout the HPG signaling pathway, contributing towards improving our understanding of the molecular mechanisms and regulative pathways in the reproductive system of teleost fish

Stability Switching Curves and Hopf Bifurcation of a Fractional Predator–Prey System with Two Nonidentical Delays

In this paper, we propose and analyze a three-dimensional fractional predator–prey system with two nonidentical delays. By choosing two delays as the bifurcation parameter, we first calculate the stability switching curves in the delay plane. By judging the direction of the characteristic root across the imaginary axis in stability switching curves, we obtain that the stability of the system changes when two delays cross the stability switching curves, and then, the system appears to have bifurcating periodic solutions near the positive equilibrium, which implies that the trajectory of the system is the axial symmetry. Secondly, we obtain the conditions for the existence of Hopf bifurcation. Finally, we give one example to verify the correctness of the theoretical analysis. In particular, the geometric stability switch criteria are applied to the stability analysis of the fractional differential predator–prey system with two delays for the first time

Unloading Mechanics and Energy Characteristics of Sandstone under Different Intermediate Principal Stress Conditions

The TRW-3000 true triaxial rock testing machine was used to conduct loading and unloading tests of sandstone under different σ2, and the true triaxial lateral unloading mechanics and energy characteristics of sandstone under different σ2 were studied. The experimental results show the following: (1) compared with the results of the loading test, the peak strength of the sandstone under the unloading σ3 path is reduced, the unloading direction has obvious expansion and deformation, and the amount of expansion increases significantly with the increase of σ2; sudden brittle failure occurs at the end of unloading. E gradually decreases with the increase of H, and it performs well to use the cubic polynomial to fit the curve of E-H. (2) The Mogi–Coulomb strength criterion can accurately describe the true triaxial strength characteristics of sandstone under loading and unloading conditions. Compared with the results of the loading test, the values of c and φ obtained based on this criterion under the unloading σ3 path are reduced. (3) Under the condition of unloading σ3, U,Ue, and Ud, when the specimen is broken, are all linearly positively correlated with σ2. Ud increases nonlinearly with the increase of H, and as σ2 increases, the slope of the Ud-H curve becomes larger, and the specimen consumes more energy under the same unloading amount. Most of the energy absorbed by the specimen under the unloading σ3 path is converted into Ue, but as σ2 increases, Ud /U increases, and the energy consumed when the specimen is broken is greater

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Landslide Susceptibility Assessment (LSA) is a fundamental component of landslide risk management and a substantial area of geospatial research. Previous researchers have considered the spatial non-stationarity relationship between landslide occurrences and Landslide Conditioning Factors (LCFs) as fixed effects. The fixed effects consider the spatial non-stationarity scale between different LCFs as an average value, which is represented by a single bandwidth in the Geographically Weighted Regression (GWR) model. The present study analyzes the non-stationarity scale effect of the spatial relationship between LCFs and landslides and explains the influence of factor correlation on the LSA. A Principal-Component-Analysis-based Multiscale GWR (PCAMGWR) model is proposed for landslide susceptibility mapping, in which hexagonal neighborhoods express spatial proximity and extract LCFs as the model input. The area under the receiver operating characteristic curve and other statistical indicators are used to compare the PCAMGWR model with other GWR-based models and global regression models, and the PCAMGWR model has the best prediction effect. Different spatial non-stationarity scales are obtained and improve the prediction accuracy of landslide susceptibility compared to a single spatial non-stationarity scale

Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language. This one-to-one mapping between words in different languages is too strict. Also the context is not considered at all. In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary. We conduct experiments on NLP&CC 2013 cross-lingual sentiment analysis dataset, employing English as source language, and Chinese as target language. Our method does not rely on the parallel corpora and the experimental results show that our approach is more competitive than the state-of-the-art methods in cross-lingual sentiment classification