Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed

Postnatal onset of retinal degeneration by loss of embryonic Ezh2 repression of Six1

Some adult-onset disorders may be linked to dysregulated embryonic development, yet the mechanisms underlying this association remain poorly understood. Congenital retinal degenerative diseases are blinding disorders characterized by postnatal degeneration of photoreceptors, and affect nearly 2 million individuals worldwide, but ∼50% do not have a known mutation, implicating contributions of epigenetic factors. We found that embryonic deletion of the histone methyltransferase (HMT) Ezh2 from all retinal progenitors resulted in progressive photoreceptor degeneration throughout postnatal life, via derepression of fetal expression of Six1 and its targets. Forced expression of Six1 in the postnatal retina was sufficient to induce photoreceptor degeneration. Ezh2, although enriched in the embryonic retina, was not present in the mature retina; these data reveal an Ezh2-mediated feed-forward pathway that is required for maintaining photoreceptor homeostasis in the adult and suggest novel targets for retinal degeneration therapy

Integrated Profiling of MicroRNAs and mRNAs: MicroRNAs Located on Xq27.3 Associate with Clear Cell Renal Cell Carcinoma

Background: With the advent of second-generation sequencing, the expression of gene transcripts can be digitally measured with high accuracy. The purpose of this study was to systematically profile the expression of both mRNA and miRNA genes in clear cell renal cell carcinoma (ccRCC) using massively parallel sequencing technology. Methodology: The expression of mRNAs and miRNAs were analyzed in tumor tissues and matched normal adjacent tissues obtained from 10 ccRCC patients without distant metastases. In a prevalence screen, some of the most interesting results were validated in a large cohort of ccRCC patients. Principal Findings: A total of 404 miRNAs and 9,799 mRNAs were detected to be differentially expressed in the 10 ccRCC patients. We also identified 56 novel miRNA candidates in at least two samples. In addition to confirming that canonical cancer genes and miRNAs (including VEGFA, DUSP9 and ERBB4; miR-210, miR-184 and miR-206) play pivotal roles in ccRCC development, promising novel candidates (such as PNCK and miR-122) without previous annotation in ccRCC carcinogenesis were also discovered in this study. Pathways controlling cell fates (e. g., cell cycle and apoptosis pathways) and cell communication (e. g., focal adhesion and ECM-receptor interaction) were found to be significantly more likely to be disrupted in ccRCC. Additionally, the results of the prevalence screen revealed that the expression of a miRNA gene cluster located on Xq27.3 was consistently downregulated in at least 76.7% of similar to 50 ccRCC patients. Conclusions: Our study provided a two-dimensional map of the mRNA and miRNA expression profiles of ccRCC using deep sequencing technology. Our results indicate that the phenotypic status of ccRCC is characterized by a loss of normal renal function, downregulation of metabolic genes, and upregulation of many signal transduction genes in key pathways. Furthermore, it can be concluded that downregulation of miRNA genes clustered on Xq27.3 is associated with ccRCC

Pentaquark states in a diquark–triquark model

The diquark–triquark model is used to explain charmonium-pentaquark states, i.e., Pc(4380) and Pc(4450), which were observed recently by the LHCb Collaboration. For the first time, we investigate the properties of the color attractive configuration of a triquark and we define a nonlocal light cone distribution amplitude for pentaquark states, where both diquark and triquark are not pointlike, but they have nonzero size. We establish an effective diquark–triquark Hamiltonian based on spin–orbital interaction. According to the Hamiltonian, we show that the minimum mass splitting between 52+ and 32− is around 100 MeV, which may naturally solve the challenging problem of small mass splitting between Pc(4450) and Pc(4380). This helps to understand the peculiarities of Pc(4380) with a broad decay width whereas Pc(4450) has a narrow decay width. Based on the diquark–triquark model, we predict more pentaquark states, which will hopefully be measured in future experiments

Landslide Extraction Using Mask R-CNN with Background-Enhancement Method

The application of deep learning methods has brought improvements to the accuracy and automation of landslide extractions based on remote sensing images because deep learning techniques have independent feature learning and powerful computing ability. However, in application, the quality of training samples often fails the requirement for training deep networks, causing insufficient feature learning. Furthermore, some background objects (e.g., river, bare land, building) share similar shapes, colors, and textures with landslides. They can be confusing to automatic tasks, contributing false and missed extractions. To solve the above problems, a background-enhancement method was proposed to enrich the complexity of samples. Models can learn the differences between landslides and background objects more efficiently through background-enhanced samples, then reduce false extractions on background objects. Considering that the environments of disaster areas play dominant roles in the formation of landslides, landslide-inducing attributes (DEM, slope, distance from river) were used as supplements, providing additional information for landslide extraction models to further improve the accuracy of extraction results. The proposed methods were applied to extract landslides that occurred in Ludian county, Yunnan Province, in August 2014. Comparative experiments were conducted using a mask R-CNN model. The experiment using both background-enhanced samples and landslide-inducing information showed a satisfying result with an F1 score of 89.08%. Compared with the F1 score from the experiment using only satellite images as input data, it was significantly improved by 22.38%, underscoring the applicability and effectiveness of our background-enhancement method

Underground Coal Mine Methane Displacement by Injecting Low-pressure Gas into the Meta-anthracite Seam: Laboratory and Field Tests

Because of the strong adsorption capacity of meta-anthracite, the gas content of a meta-anthracite seam can be as high as 10 m3/t, with a gas pressure lower than 0.74 MPa; this results in low efficiency of gas extraction in underground mines. To enhance low-pressure methane extraction efficiency in meta-anthracite seams, a new approach – methane displacement by gas injection – has been developed, investigated in the laboratory, and then applied in the field in the Fuyanshan coal mine. Laboratory results show that when the gas content of the coal seam is high, methane displacement by nitrogen injection is difficult. The volume of methane displaced is directly related to the pressure difference between the coal seam gas pressure and the injection gas pressure. If the total gas pressure is greater than 0.5 MPa after nitrogen injection, then the methane displacement efficiency will be greatly enhanced. It is also confirmed that the displacement efficiency can be improved by injecting inert gas to change the partial pressure of the methane. Field test data show quite good methane displacement efficiency

Influence of InP/ZnS Quantum Dots on Thermodynamic Properties and Morphology of the DPPC/DPPG Monolayers at Different Temperatures

In this work, the effects of InP/ZnS quantum dots modified with amino or carboxyl group on the characteristic parameters in phase behavior, elastic modulus, relaxation time of the DPPC/DPPG mixed monolayers are studied by the Langmuir technology at the temperature of 37, 40 and 45 °C. Additionally, the information on the morphology and height of monolayers are obtained by the Langmuir–Bloggett technique and atomic force microscope technique. The results suggest that the modification of the groups can reduce the compressibility of monolayers at a higher temperature, and the most significant effect is the role of the amino group. At a high temperature of 45 °C, the penetration ability of InP/ZnS-NH2 quantum dots in the LC phase of the mixed monolayer is stronger. At 37 °C and 40 °C, there is no clear difference between the penetration ability of InP/ZnS-NH2 quantum dots and InP/ZnS-COOH quantum dots. The InP/ZnS-NH2 quantum dots can prolong the recombination of monolayers at 45 °C and accelerate it at 37 °C and 40 °C either in the LE phase or in the LC phase. However, the InP/ZnS-COOH quantum dots can accelerate it in the LE phase at all temperatures involved but only prolong it at 45 °C in the LC phase. This work provides support for understanding the effects of InP/ZnS nanoparticles on the structure and properties of cell membranes, which is useful for understanding the behavior about the ingestion of nanoparticles by cells and the cause of toxicity