FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation

Curvilinear object segmentation is critical for many applications. However, manually annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently available annotated datasets for existing supervised methods and domain adaptation methods. This paper proposes a self-supervised curvilinear object segmentation method that learns robust and distinctive features from fractals and unlabeled images (FreeCOS). The key contributions include a novel Fractal-FDA synthesis (FFS) module and a geometric information alignment (GIA) approach. FFS generates curvilinear structures based on the parametric Fractal L-system and integrates the generated structures into unlabeled images to obtain synthetic training images via Fourier Domain Adaptation. GIA reduces the intensity differences between the synthetic and unlabeled images by comparing the intensity order of a given pixel to the values of its nearby neighbors. Such image alignment can explicitly remove the dependency on absolute intensity values and enhance the inherent geometric characteristics which are common in both synthetic and real images. In addition, GIA aligns features of synthetic and real images via the prediction space adaptation loss (PSAL) and the curvilinear mask contrastive loss (CMCL). Extensive experimental results on four public datasets, i.e., XCAD, DRIVE, STARE and CrackTree demonstrate that our method outperforms the state-of-the-art unsupervised methods, self-supervised methods and traditional methods by a large margin. The source code of this work is available at https://github.com/TY-Shi/FreeCOS.Comment: Accepted by ICCV 202

Astaxanthin production by Phaffia rhodozyma fermentation of cassava residues substrate

Cassava residues as main materials were fermented with Phaffia rhodozyma to produce astaxanthin.  Using the Box-Behnken design, the effects of sugar content, initial pH and nitrogen content were studied with the yield of astaxanthin as response value, which was evaluated to optimize the fermentation conditions of astaxanthin production.  The optimal fermentation conditions have been reached by the study: sugar content was 40 g/L, the initial pH was at 4 and nitrogen content was 8 g/L.  By validation test, the astaxanthin yield under the optimal condition, which was basically corresponded to the model prediction, was 96.83%.Keywords: astaxanthin, cassava recidues, Phaffia rhodozym

Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily. Data and code are available at https://github.com/albertwy/SWRM.Comment: Findings of ACL 202

Clinical, neurophysiological evaluation and genetic features of axonal Charcot–Marie–Tooth disease in a Chinese family

Charcot–Marie–Tooth disease (CMT) is a group of inherited peripheral neuropathies related to variants in the mitochondrial transfer RNA (mt-tRNAval) gene. Here, we report a Chinese family harboring the m.1661A>G variant in the mt-tRNAval gene. Clinical evaluation, neuroelectrodiagnostic testing, and nerve biopsy were performed on four affected family members. Weakness, spasms, and pain in the limbs (especially in the lower limbs) were the main complaints of the proband. Physical examination revealed atrophy and weakness in the distal limbs, increased muscle tone, and hyperreflexia in four limbs. Neuroelectrodiagnostic tests and nerve biopsy supported an axonal polyneuropathy. This study furthers the understanding of phenotype diversity caused by variants in the mt-tRNAval gene in CMT

Natural Environment Suitability of China and Its Relationship with Population Distributions

The natural environment factor is one of the main indexes for evaluating human habitats, sustained economic growth and ecological health status. Based on Geographic Information System (GIS) technology and an analytic hierarchy process method, this article presents the construction of the Natural Environment Suitability Index (NESI) model of China by using natural environment data including climate, hydrology, surface configuration and ecological conditions. The NESI value is calculated in grids of 1 km by 1 km through ArcGIS. The spatial regularity of NESI is analyzed according to its spatial distribution and proportional structure. The relationship of NESI with population distribution and economic growth is also discussed by analyzing NESI results with population distribution data and GDP data in 1 km by 1 km grids. The study shows that: (1) the value of NESI is higher in the East and lower in the West in China; The best natural environment area is the Yangtze River Delta region and the worst are the northwest of Tibet and southwest of Xinjiang. (2) There is a close correlation among natural environment, population distribution and economic growth; the best natural environment area, the Yangtze River Delta region, is also the region with higher population density and richer economy. The worst natural environment areas, Northwest and Tibetan Plateau, are also regions with lower population density and poorer economies

Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

BackgroundBladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.MethodsPredictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.ResultsPyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.ConclusionsBLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets

Pressure-stabilized divalent ozonide CaO3 and its impact on Earth's oxygen cycles.

High pressure can drastically alter chemical bonding and produce exotic compounds that defy conventional wisdom. Especially significant are compounds pertaining to oxygen cycles inside Earth, which hold key to understanding major geological events that impact the environment essential to life on Earth. Here we report the discovery of pressure-stabilized divalent ozonide CaO3 crystal that exhibits intriguing bonding and oxidation states with profound geological implications. Our computational study identifies a crystalline phase of CaO3 by reaction of CaO and O2 at high pressure and high temperature conditions; ensuing experiments synthesize this rare compound under compression in a diamond anvil cell with laser heating. High-pressure x-ray diffraction data show that CaO3 crystal forms at 35 GPa and persists down to 20 GPa on decompression. Analysis of charge states reveals a formal oxidation state of -2 for ozone anions in CaO3. These findings unravel the ozonide chemistry at high pressure and offer insights for elucidating prominent seismic anomalies and oxygen cycles in Earth's interior. We further predict multiple reactions producing CaO3 by geologically abundant mineral precursors at various depths in Earth's mantle

Transcriptome sequencing of olfactory-related genes in olfactory transduction of large yellow croaker (Larimichthy crocea) in response to bile salts

Fish produce and release bile salts as chemical signalling substances that act as sensitive olfactory stimuli. To investigate how bile salts affect olfactory signal transduction in large yellow croaker (Larimichthy crocea), deep sequencing of olfactory epithelium was conducted to analyse olfactory-related genes in olfactory transduction. Sodium cholates (SAS) have typical bile salt chemical structures, hence we used four different concentrations of SAS to stimulate L. crocea, and the fish displayed a significant behavioural preference for 0.30% SAS. We then sequenced olfactory epithelium tissues, and identified 9938 unigenes that were significantly differentially expressed between SAS-stimulated and control groups, including 9055 up-regulated and 883 down-regulated unigenes. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses found eight categories linked to the olfactory transduction pathway that was highly enriched with some differentially expressed genes (DEGs), including the olfactory receptor (OR), Adenylate cyclase type 3 (ADCY3) and Calmodulin (CALM). Genes in these categories were analysed by RT-qPCR, which revealed aspects of the pathway transformation between odor detection, and recovery and adaptation. The results provide new insight into the effects of bile salt stimulation in olfactory molecular mechanisms in fishes, and expands our knowledge of olfactory transduction, and signal generation and decline