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

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

SARS-CoV-2 Host Receptor ACE2 Protein Expression Atlas in Human Gastrointestinal Tract

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through interactions with its receptor, Angiotensin-converting enzyme 2 (ACE2), causing severe acute respiratory syndrome and death in a considerable proportion of people. Patients infected with SARS-CoV-2 experience digestive symptoms. However, the precise protein expression atlas of ACE2 in the gastrointestinal tract remains unclear. In this study, we aimed to explore the ACE2 protein expression pattern and the underlying function of ACE2 in the gastrointestinal tract, including the colon, stomach, liver, and pancreas.MethodsWe measured the protein expression of ACE2 in the gastrointestinal tract using immunohistochemical (IHC) staining with an ACE2-specific antibody of paraffin-embedded colon, stomach, liver, and pancreatic tissues. The correlation between the protein expression of ACE2 and the prognosis of patients with gastrointestinal cancers was analyzed by the log-rank (Mantel–Cox) test. The influence of ACE2 on colon, stomach, liver, and pancreatic tumor cell line proliferation was tested using a Cell Counting Kit 8 (CCK-8) assay.ResultsACE2 presented heterogeneous expression patterns in the gastrointestinal tract, and it showed a punctate distribution in hepatic cells. Compared to that in parallel adjacent non-tumor tissues, the protein expression of ACE2 was significantly increased in colon cancer, stomach cancer, and pancreatic cancer tissues but dramatically decreased in liver cancer tissues. However, the expression level of the ACE2 protein was not correlated with the survival of patients with gastrointestinal cancers. Consistently, ACE2 did not affect the proliferation of gastrointestinal cancer cells in vitro.ConclusionThe ACE2 protein is widely expressed in the gastrointestinal tract, and its expression is significantly altered in gastrointestinal tumor tissues. ACE2 is not an independent prognostic marker of gastrointestinal cancers

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

Study on Point Mutations of K-ras Gene in Non-small Cell Lung Cancer in Guangxi

Background and objective Recent studies indicated that non-small cell lung cancer (NSCLC) patients with mutant K-ras were resistant to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). The aim of this study is to explore the relationship between the mutation of K-ras gene and NSCLC in Guangxi by detecting the point mutations in codon 12, 13 and 61 of K-ras gene in NSCLC. Methods The point mutations in codon 12, 13 and 61 of K-ras gene were detected by single-strand conformation polymorphism (SSCP) analysis of polymerase chain reaction (PCR) products and DNA sequencing analysis in 105 cases of NSCLC tissues and 30 cases of adjacent normal tissues. Results No point mutation in codon 12, 13 and 61 of K-ras gene was found in 105 cases of NSCLC tissues and 30 cases of adjacent normal tissues. In this study, the mutation frequency of K-ras gene in NSCLC was 0 (0/105). Conclusion The high proportion of K-ras gene in wild-type indicates that patients with NSCLC in Guangxi could take more benefits from the therapy with EGFR-TKIs

Intelligent Dendritic Neural Model for Classification Problems

In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed

Intelligent Dendritic Neural Model for Classification Problems

In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed

Ubiquitin-modifying enzymes in thyroid cancer：Mechanisms and functions

Thyroid cancer is the most common malignant tumor of the endocrine system, and evidence suggests that post-translational modifications (PTMs) and epigenetic alterations play an important role in its development. Recently, there has been increasing evidence linking dysregulation of ubiquitinating enzymes and deubiquitinases with thyroid cancer. This review aims to summarize our current understanding of the role of ubiquitination-modifying enzymes in thyroid cancer, including their regulation of oncogenic pathways and oncogenic proteins. The role of ubiquitination-modifying enzymes in thyroid cancer development and progression requires further study, which will provide new insights into thyroid cancer prevention, treatment and the development of novel agents

Academic Career Progression of Chinese-Origin Pharmacy Faculty Members in Western Countries

Background: The field of Pharmacy education is experiencing a paucity of underrepresented minorities (URMs) faculty worldwide. The aim of this study is to investigate the current professional status of Chinese-origin pharmacy faculty members, who are considered as a good model of URMs at pharmacy academia in western countries, and identify the influencing factors to their academic career progression in academic careers. Methods: An online questionnaire was sent to Chinese-origin academic staffs at pharmacy schools in US, UK, Canada, Australia, and New Zealand. The survey comprised demographic information, educational background, and the influencing factors to academic career progression. Results: The vast majority of Chinese faculty members who worked in US were male. Individuals with junior academic title comprised the largest proportion. Over 75% of Chinese-origin pharmacy academics were involved in scientific disciplines (e.g., pharmaceutics, pharmacology, and medicinal chemistry). Usually, Chinese-origin academic members spent 4 years obtaining their first academic jobs after finishing PhD degree, and need 5–6 years to get academic promotion. The contributing factors of academic promotion were high quality publications and external funding. Conclusion: Our research offers a deep insight into academic career progression for URMs and give some valuable advice for their pharmacy academic paths

Propagation Laws of Blasting Seismic Waves in Weak Rock Mass: A Case Study of Muzhailing Tunnel

In order to study the propagation laws of blasting vibration waves in weak rock tunnels, the longitudinal and circumferential blasting vibration tests in Muzhailing Tunnel were carried out, and the measured data were analyzed and studied using the methods of Sadov’s nonlinear regression, Fourier transform, and Hilbert–Huang transform (HHT) to provide a reference for the optimization of blasting design of Muzhailing Tunnel or similar weak rock tunnels. The results showed that the tangential main frequency decreases rapidly and the radial main frequency decreases slowly with the increase of proportionate charge quantity. Under a certain charge quantity, as the distance from the explosion source increases, the spectrum width of the blasting vibration frequency becomes narrower, the overall energy is more concentrated, and the vibration frequency tends to be closer to the low frequency. At a certain distance from the explosive source, the frequency of blasting vibration decreases gradually, and the amplitude of low-frequency region increases with the increase of charge quantity. The vibration velocity on the left side of the tunnel is larger than that on the right side, and the vibration velocity at the vault and the arch foot of lower bench decreases rapidly, while the vibration velocity at the arch feet of upper bench and middle bench decreases slowly. The vibration frequencies of the left arch foot of the middle bench and the right arch foot of the upper bench are higher than those of other positions, while the frequencies of the left arch foot of the upper bench are the lowest. During tunnel blasting, the energy input to the strata media is mainly concentrated in the stage of the blasting of the cut hole. The blasting has more energy input to the left arch foot of the upper bench and the tunnel vault, which is consistent with the conclusion of frequency analysis