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Objective To explore the clinicopathological impact of lncRNAs, immunotherapy, and DNA methylation in lung squamous cell carcinoma (LUSC), emphasizing their exact roles in carcinogenesis and modes of action. Background LUSC is the second most prevalent form, accounting for around 30% of non-small cell lung cancer (NSCLC). To date, molecular-targeted treatments have significantly improved overall survival in lung adenocarcinoma patients but have had little effect on LUSC therapy. As a result, there is an urgent need to discover new treatments for LUSC that are based on existing genomic methods. Methods In this review, we summarized and analyzed recent research on the biological activities and processes of lncRNA, immunotherapy, and DNA methylation in the formation of LUSC. The relevant studies were retrieved using a thorough search of Pubmed, Web of Science, Science Direct, Google Scholar, and the university’s online library, among other sources. Conclusions LncRNAs are the primary components of the mammalian transcriptome and are emerging as master regulators of a number of cellular processes, including the cell cycle, differentiation, apoptosis, and growth, and are implicated in the pathogenesis of a variety of cancers, including LUSC. Understanding their role in LUSC in detail may help develop innovative treatment methods and tactics for LUSC. Meanwhile, immunotherapy has transformed the LUSC treatment and is now considered the new standard of care. To get a better knowledge of LUSC biology, it is critical to develop superior modeling systems. Preclinical models, particularly those that resemble human illness by preserving the tumor immune environment, are essential for studying cancer progression and evaluating novel treatment targets. DNA methylation, similarly, is a component of epigenetic alterations that regulate cellular function and contribute to cancer development. By methylating the promoter regions of tumor suppressor genes, abnormal DNA methylation silences their expression. DNA methylation indicators are critical in the early detection of lung cancer, predicting therapy efficacy, and tracking treatment resistance. As such, this review seeks to explore the clinicopathological impact of lncRNAs, immunotherapy, and DNA methylation in LUSC, emphasizing their exact roles in carcinogenesis and modes of action.

Background With the increasing fragmentation of landscape induced by rapid urbanization, the construction of ecological networks is of great significance to alleviate the degradation of urban habitats and protect natural environments. However, there is considerable uncertainty when constructing ecological networks, especially the different approaches to selecting ecological sources. We used the southern Chinese city of Shenzhen as a study area to construct and optimize ecological networks using a coupling approach. Ecological source areas were extracted using morphological spatial pattern analysis (MSPA) and the landscape index method. Ecological networks were constructed using the minimal cumulative resistance (MCR) model and the gravity model. Stepping stones and ecological fault points were added in corridors to optimize the ecological network. Results Ten core areas with maximum importance patch values were extracted by the landscape index method as ecological source areas according to MSPA, after which corridors between ecological sources were constructed based on the MCR model. The constructed ecological networks were optimized using 35 stepping stones and 17 ecological fault points. The optimized ecological networks included 11 important corridors, 34 general corridors, and seven potential corridors. The results of corridor landscape-type analysis showed that a suitable ecological corridor is 60 to 200 m wide. Conclusions Overall, our results imply that ecological source areas can be identified virtually, and that ecological networks can be significantly optimized by combining MSPA and MCR models. These results provide a methodological reference for constructing ecological networks, and they will be useful for urban planning and biodiversity protection in Shenzhen and other similar regions around the world.

Nowadays, with the increasing shortage of traditional medical resources, the existing portable monitoring healthcare device is no longer satisfactory. Thus, wearable healthcare device with diagnostic capability is becoming much more desirable. However, the design of wearable healthcare device faces the challenge of limited hardware resource and high diagnostic accuracy. In this paper, an efficient hardware architecture is proposed to implement a 1-D CNN with global average pooling (GAP) specially for embedded electrocardiogram (ECG) classification. The GAP is implemented by substituting division into shifting operation without extra computing resource consumption and it can largely reduce the parameters of the network. The fully pipelined processing unit (PU) array is designed to increase computing efficiency. A sign bit based dynamic activation strategy is developed for removing redundant multiplications and resource consumption of ReLU. The proposed efficient hardware architecture is implemented on Xilinx Zynq ZC706 board and achieves an average performance of 25.7 GOP/s under 200-MHz with resource consumption of 1538 LUT, which makes resource efficiency improved by more than $3\times $ compared with non-optimized case. The averaged classification accuracy of five ECG beats classes is 99.10%. In brief, the proposed efficient hardware design is prospective for wearable healthcare device especially in ECG classification area.

The spring-in behavior of woven carbon fiber/polycarbonate thermoplastic composite is investigated by both the experiment and simulation ways. These thermoplastic composite plates are thermoformed into V-shaped structures, and the final bending angle is measured after demolding. In the finite element simulation to describe the thermoforming and spring-in behavior of the composite, the discrete approach is adopted to simulate the behavior of woven carbon fabric and combined with a resin model. The results from both the experiment and the simulation indicate the spring-in behavior of this thermoplastic composite. For [(±45)]6 blanks, the spring-in angle is around 2.60° for a 60-degree V-shape and around 1.72° for a 90-degree V-shape. For [(0/90)]6 blanks, the spring-in angle is around 2.38° and 1.19° for 60-degree and 90-degree V-shapes, respectively. The [(±45)]6 blank always has a slightly higher spring-in angle than the [(0/90)]6 blank, even though the difference is small. The finite element results show 6% to 12% differences with the experimental values if the spring-in angle is used, and the error is less than 0.6% when the bending angle is compared. By using the present finite element analysis, the results indicate that the stacking sequence could have an evident effect on the spring-in angle, and symmetric laminates have less spring-in effect as compared to their unsymmetric counterparts.

Aims We conducted a multi-sectoral comprehensive intervention for elderly Alzheimer Disease patients and their caregivers to improve their quality of life. Design Randomized controlled trial. Methods From June to June in 2019, 150 Alzheimer Disease patients were randomly selected from a nursing home in Zhejiang province of China, they were randomly divided into the intervention group and the control group. And they were given comprehensive and routine interventions respectively for six months. We used mixed models in our analyses to see how outcomes changed over time and how they were affected by the intervention, which could also solve the problems of missing values and data correlation. Results After six months, compared with the control group, the communication ability, housework handling ability, self-care ability and life quality of the patients in the intervention group were improved, and the quality of life and caring ability of their caregivers were also significantly improved. Conclusion The multi-sectoral collaborative care model proposed in this study integrated resources from several departments, effectively improved the quality of life of patients and their caregivers, and provided a way of care services for patients with Alzheimer Disease. Impact Multi-sectoral comprehensive intervention would improve the life quality of elderly patients with Alzheimer Disease and their caregivers.

Background: At present, there are a few research studies on the stability of the supporting structure of bored piles with steel supports for deep foundation pits, and it is even less common to use this support method for deep foundation pits in sandy pebble stratum. This paper studied the stability of the deep foundation pit support of the Southwest Jiaotong University Station in Chengdu, China, using the stability analysis theory of bored piles with steel supports for deep foundation pits. Objective: The study aims to show the stability of steel supports for deep foundation pits. Methods: The overall stability, anti-overturning stability and uplift stability of the foundation pit are important components of the stability analysis of bored piles with steel supports for deep foundation pits. Aiming at the designed method and parameters of the bored piles with steel support for the deep foundation pit, in-depth theoretical analysis, and calculation of the overall stability, antioverturning stability, and uplift stability of the deep foundation pit are conducted, respectively. Results: The results show that the stability of the deep foundation pit using bored piles with steel support meets the requirements, indicating that the design of this support form is safe and reasonable, thus ensuring the smooth progress of the project. Conclusion: The stability analysis of deep foundation pits in sandy pebble stratum provides ideas for the optimization of deep foundation pits and a reference for the stability analysis of deep foundation pits in sandy pebble stratum and other types of the stratum.

An adaptive machine-learning object-localization system was proposed in this study and applied to an automated carrier robot equipped with monocular vision, and the problem of object-distance localization in vision was improved using machine learning. When executing a project involving small robots, the calculation of the target object by the object-distance algorithm depends on the system-defined parameters, such as camera height, field of view, and inclination angle. When the carrier encounters an inclination that causes changes in the camera’s external parameters, the parameters defined in the system differ from those under real conditions, causing severe errors in calculation results. This system combined a triaxial sensor and machine-learning regression to identify the functional relationship between the output signal of the accelerometer and the actual carrier attitude. The regression network model was trained by recording several sets of signal features and carrier angles as the data set. During object-distance calculation, the adaptive machine-learning object-localization system predicted the current carrier angle by using the output of the accelerometer and the trained regression network model to update the algorithm’s angle parameters. The experimental results indicated that the system possessed stability and accuracy in calculating the distance of the target object when the carrier angle changed.

Background: Ovarian carcinoma (OC) is one of the most common malignancies of the female reproductive organs, with a low survival rate primarily due to the lack of effective methods for early diagnosis and prognosis. Objective: In this article, our motivation is to explore the lncRNA-related network mechanisms involved in the pathogenesis of OC. Methods: Public lncRNAs and mRNA expression datasets for OC were collected from the Gene Expression Omnibus (GEO) database. By integrated bioinformatics analysis, we constructed a UCA1-miRNA-mRNA network. We studied lncRNA-related molecular modulation mechanism in ovarian cancer cells based on MTT assay, dual luciferase reporter gene assays, quantitative realtime PCR, and western blotting. Results: UCA1 was higher in ovarian tumor tissues and cells than normal tissues and cells. It was demonstrated in this study that knockdown of UCA1 inhibited ovarian cancer cell viability, which a miR-99b-3p inhibitor could reverse in vitro. Further, UCA1 was shown to regulate the expression of SRPK1 by directly binding to miR-99b-3p. Conclusions: These results suggest that UCA1 functions as an oncogene in ovarian cancer. Inhibition of UCA1/miR-99b-3p/SRPK1 axis may become a novel target for treating ovarian cancer.

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