Harmonizing Output Imbalance for semantic segmentation on extremely-imbalanced input data

Semantic segmentation is a high level computer vision task that assigns a label for each pixel of an image. It is challenging to deal with extremely-imbalanced data in which the ratio of target pixels to background pixels is lower than 1:1000. Such severe input imbalance leads to output imbalance for poor model training. This paper considers three issues for extremely-imbalanced data: inspired by the region-based Dice loss, an implicit measure for the output imbalance is proposed, and an adaptive algorithm is designed for guiding the output imbalance hyperparameter selection; then it is generalized to distribution-based loss for dealing with output imbalance; and finally a compound loss with our adaptive hyperparameter selection algorithm can keep the consistency of training and inference for harmonizing the output imbalance. With four popular deep architectures on our private dataset from three different input imbalance scales and three public datasets, extensive experiments demonstrate the competitive/promising performance of the proposed method.Comment: 18 pages, 13 figures, 2 appendixe

Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks

As a fundamental computer vision task, crowd counting plays an important role in public safety. Currently, deep learning based head detection is a promising method for crowd counting. However, the highly concerned object detection networks cannot be well applied to this problem for three reasons: (1) Existing loss functions fail to address sample imbalance in highly dense and complex scenes; (2) Canonical object detectors lack spatial coherence in loss calculation, disregarding the relationship between object location and background region; (3) Most of the head detection datasets are only annotated with the center points, i.e. without bounding boxes. To overcome these issues, we propose a novel Mask Focal Loss (MFL) based on heatmap via the Gaussian kernel. MFL provides a unifying framework for the loss functions based on both heatmap and binary feature map ground truths. Additionally, we introduce GTA_Head, a synthetic dataset with comprehensive annotations, for evaluation and comparison. Extensive experimental results demonstrate the superior performance of our MFL across various detectors and datasets, and it can reduce MAE and RMSE by up to 47.03% and 61.99%, respectively. Therefore, our work presents a strong foundation for advancing crowd counting methods based on density estimation.Comment: The manuscript is accepted by Multimedia Tools and Application

Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition

Additional file 11: Table S7. Summary of de novo assembly results (37 k)

A missing link in the estuarine nitrogen cycle?: coupled nitrification-denitrification mediated by suspended particulate matter

In estuarine and coastal ecosystems, the majority of previous studies have considered coupled nitrification-denitrification (CND) processes to be exclusively sediment based, with little focus onsuspended particulate matter (SPM) in the water column. Here, we present evidence of CND processes in the water column of Hangzhou Bay, one of the largest macrotidal embayments in the world

A Control Algorithm of Active Wave Compensation System Based on the Stewart Platform

Aim at the actual engineering requirements of wind power operation and maintenance under complex sea conditions, a control method of the active wave compensation system for maintenance ships based on the Stewart platform is presented. The kinematics of the platform is analyzed, and the coordinate transformation, pose, and inverse solutions are analyzed and calculated. The multi-body dynamics simulation model is established by using MATLAB. For the problem of the load nonlinearity and strong coupling of the nonlinear Stewart platform, an active wave compensation active disturbance rejection control (ADRC) is built to attain the high-precision control of the six-degree-of-freedom (6-DOF) Stewart platform. The numerical simulation shows that the proposed control scheme has good tracking accuracy and strong anti-interference ability.publishedVersio

Mapping the Galactic disk with the LAMOST and Gaia Red clump sample: I: precise distances, masses, ages and 3D velocities of ∼\sim 140000 red clump stars

We present a sample of $\sim$ 140,000 primary red clump (RC) stars of spectral signal-to-noise ratios higher than 20 from the LAMOST Galactic spectroscopic surveys, selected based on their positions in the metallicity-dependent effective temperature--surface gravity and color--metallicity diagrams, supervised by high-quality $Kepler$ asteroseismology data. The stellar masses and ages of those stars are further determined from the LAMOST spectra, using the Kernel Principal Component Analysis method, trained with thousands of RCs in the LAMOST-$Kepler$ fields with accurate asteroseismic mass measurements. The purity and completeness of our primary RC sample are generally higher than 80 per cent. For the mass and age, a variety of tests show typical uncertainties of 15 and 30 per cent, respectively. Using over ten thousand primary RCs with accurate distance measurements from the parallaxes of Gaia DR2, we re-calibrate the $K_{\rm s}$ absolute magnitudes of primary RCs by, for the first time, considering both the metallicity and age dependencies. With the the new calibration, distances are derived for all the primary RCs, with a typical uncertainty of 5--10 per cent, even better than the values yielded by the Gaia parallax measurements for stars beyond 3--4 kpc. The sample covers a significant volume of the Galactic disk of $4 \leq R \leq 16$ kpc, $|Z| \leq 5$ kpc, and $-20 \leq \phi \leq 50^{\circ}$. Stellar atmospheric parameters, line-of-sight velocities and elemental abundances derived from the LAMOST spectra and proper motions of Gaia DR2 are also provided for the sample stars. Finally, the selection function of the sample is carefully evaluated in the color-magnitude plane for different sky areas. The sample is publicly available.Comment: 16 pages, 19 figures, 3 tables, accepted for publication in ApJ

A missing link in the estuarine nitrogen cycle?: coupled nitrification-denitrification mediated by suspended particulate matter

In estuarine and coastal ecosystems, the majority of previous studies have considered coupled nitrification-denitrification (CND) processes to be exclusively sediment based, with little focus onsuspended particulate matter (SPM) in the water column. Here, we present evidence of CND processes in the water column of Hangzhou Bay, one of the largest macrotidal embayments in the world

Association between maternal lipid profiles and vitamin D status in second trimester and risk of LGA or SGA: a retrospective study

BackgroundAccumulating evidence has linked dyslipidemia during pregnancy to the risk of delivering infants born either large for gestational age (LGA) or small for gestational age (SGA). However, the effects of the vitamin D status on these relationships require further investigation. This study investigated whether the relationship between lipid profiles and the risk of LGA or SGA was influenced by vitamin D levels during the second trimester.MethodsMaternal lipid profile levels, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and vitamin D levels, were measured in a cohort of 6,499 pregnant women during the second trimester. Multivariate regression models and subgroup analyses were employed to evaluate the potential associations between maternal lipid profiles, vitamin D levels, and the risk of LGA or SGA.ResultsThe prevalence of SGA infants was 9.8% (n=635), whereas that of LGA infants was 6.9% (n=447). Maternal TG levels were found to be positively associated with the risk of LGA (odds ratio [OR] = 1.41, 95% confidence interval [CI]:1.17–1.70), whereas a negative association was observed between maternal TG, TC, LDL-C levels, and risk of SGA. Additionally, mothers with higher HDL-C levels were less likely to give birth to an LGA infant (OR=0.58, 95% CI:0.39–0.85). Importantly, associations between TG, TC, LDL-c, and SGA as well as between TG and LGA were primarily observed among pregnant women with insufficient vitamin D levels. As for HDL-C, the risk of LGA was lower in mothers with sufficient vitamin D (OR = 0.42, 95% CI:0.18–0.98) compared to those with insufficient vitamin D (OR = 0.65, 95% CI:0.42–0.99).ConclusionVitamin D status during the second trimester exerts a modifying effect on the association between lipid profiles and the risk of LGA and SGA infants

NormExpression: An R Package to Normalize Gene Expression Data Using Evaluated Methods

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets

APOC1 predicts a worse prognosis for esophageal squamous cell carcinoma and is associated with tumor immune infiltration during tumorigenesis

Background: Esophageal carcinoma (ESCA), a common malignant tumor of the digestive tract with insidious onset, is a serious threat to human health. Despite multiple treatment modalities for patients with ESCA, the overall prognosis remains poor. Apolipoprotein C1 (APOC1) is involved in tumorigenesis as an inflammation-related molecule, and its role in esophageal cancer is still unknown.Methods: We downloaded documents and clinical data using The Cancer Genome Atlas (TCGA)and Gene Expression Omnibus (GEO) databases. We also conducted bioinformatics studies on the diagnostic value, prognostic value, and correlation between APOC1 and immune infiltrating cells in ESCA through STRING (https://cn.string-db.org/), the TISIDB (http://cis.hku.hk/TISIDB/) website, and various other analysis tools.Results: In patients with ESCA, APOC1 was significantly more highly expressed in tumor tissues than in normal tissues (p < 0.001). APOC1 could diagnose ESCA more accurately and determine the TNM stage and disease classification with high accuracy (area under the curve, AUC≥0.807). The results of the Kaplan–Meier curve analysis showed that APOC1 has prognostic value for esophageal squamous carcinoma (ESCC) (p = 0.043). Univariate analysis showed that high APOC1 expression in ESCC was significantly associated with worse overall survival (OS) (p = 0.043), and multivariate analysis shows that high APOC1 expression was an independent risk factor for the OS of patients with ESCC (p = 0.030). In addition, the GO (gene ontology)/KEGG (Kyoto encyclopedia of genes and genomes) analysis showed a concentration of gene enrichment in the regulation of T-cell activation, cornification, cytolysis, external side of the plasma membrane, MHC protein complex, MHC class II protein complex, serine-type peptidase activity, serine-type endopeptidase activity, Staphylococcus aureus infection, antigen processing and presentation, and graft-versus-host disease (all p < 0.001). GSEA (gene set enrichment analysis) showed that enrichment pathways such as immunoregulatory-interactions between a lymphoid and non-lymphoid cell (NES = 1.493, p. adj = 0.023, FDR = 0.017) and FCERI-mediated NF-KB activation (NES = 1.437, p. adj = 0.023, FDR = 0.017) were significantly enriched in APOC1-related phenotypes. In addition, APOC1 was significantly associated with tumor immune infiltrating cells and immune chemokines.Conclusion: APOC1 can be used as a prognostic biomarker for esophageal cancer. Furthermore, as a novel prognostic marker for patients with ESCC, it may have potential value for further investigation regarding the diagnosis and treatment of this group of patients