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N6-methyladenosine (m6A) modifications control multifaceted RNA metabolism and are one of the most extensively distributed modifications on the human transcriptome, including non-coding RNAs (ncRNAs). Previous concepts of ncRNAs as “junk” transcriptional products have evolved to the concept that ncRNAs are functional regulatory molecules that determine specific biological processes and cell fates. The dysregulation of m6A modifications and ncRNAs have been implicated in the development of human carcinogenesis. Certain types of ncRNAs have been reported to exert regulatory effects on m6A machinery. However, a better understanding of the relationship between m6A modifications and ncRNAs in cancer is still needed. This review discusses mutual interactions between m6A modifications and ncRNAs and their impacts on the development of human cancer. We summarize the clinical significance of m6A-ncRNA networks for cancer diagnosis and treatment, and we ask challenging questions that remain unanswered in this field of research. Understanding the complex coordination between m6A modifications and ncRNAs will be useful for guiding the development of therapeutic interventions.

In the past decade, there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration. However, it remains challenging for designers to apply such optimization-based design explorations in practice. To address this issue, this paper introduces a design tool, called EvoMass, and an associated design method that facilitates design exploration for building massing typologies in performance-based design tasks. EvoMass is capable of offering architects design options reflecting performance-related building massing typologies for the design task, without necessitating advanced computational design skills. More importantly, it can provide architects with insights into the underlying performance implications, thereby enhancing early-stage performance-based design exploration. EvoMass and its associated design method overcome the limitation in the conventional typology-first-optimization-second design procedure adopted by most existing tools, and it promotes a typology-oriented design exploration method of using computational optimization in performance-based architectural design. To demonstrate the efficacy of EvoMass, case studies derived from architectural design studio tasks, incorporating daylighting, solar exposure, and subjective design intents, and the result of a user survey are presented, which highlights how EvoMass and the performance-based design optimization and exploration can enable architects to achieve a more performance-aware design.

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight-neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this paper. The proposed method consists of four main stages: image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, middle, and bottom region of lung. Finally, corrosion and expansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emission tomography-computed tomography (PET-CT) data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and accurately.

Human non–small cell lung cancer (NSCLC) is one of the most common malignancies in the modern world. Its recurrence is mainly due to its ability to invade and metastasize. However, the precise mechanism for tumor development and metastasis is still not fully understood. To shed light on the development of lung cancer, the human giant cell lung carcinoma cell lines 95D with high metastatic potential and 95C with low metastatic potential were selected in this study. The 2 cell lines originated from the same parental cell and share a similar genetic background. In the current study, we identified 3 differentially expressed proteins in 95C and 95D cell lines, namely, PAI-RBP1, C1orf142, and COTL1, by using 2-dimensional electrophoresis proteomics analysis. We found that PAI-RBP1 and C1orf142 expression levels were higher in 95D than in 95C cells, whereas COTL1 expression level was lower in 95D when compared to 95C cells. We also confirmed these results by reverse transcription–polymerase chain reaction and immunoblotting analyses. The messenger RNA and protein levels of PAI-RBP1 and C1orf142 were much higher in 95D than in 95C cells, and COTL1 expression level was lower in 95D than in 95C cells. The PAI-RBP1 expression was assessed by immunohistochemistry in 70 NSCLC and 7 normal lung tissue samples from patients. PAI-RBP1 expression level was higher in tumor tissues (positive staining in 87.1% of cases [61/70]) than in normal tissues (positive staining in 14.3% of cases [1/7]). In conclusion, by studying protein expression in NSCLC cell lines with high and low metastasis as well as in human lung cancer tissues, we have identified 3 proteins, namely, PAI-RBP1, C1orf142, and COTL1, which were differentially expressed in NSCLC cell lines with different metastatic potential. In addition, we also found that PAI-RBP1 might contribute to NSCLC development.