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Abstract Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people’s dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

Abstract Background A standard surgical procedure for patients with small early-stage lung adenocarcinomas remains unknown. Hence, we aim in this study to assess the clinical utility of the consolidation-to-tumor ratio (CTR) when treating patients with small (2 cm) early stage lung cancers. Methods A retrospective cohort of 298 sublobar resection and 266 lobar resection recipients for early stage lung adenocarcinoma ≤ 2 cm was assembled from the First Affiliated Hospital of Chongqing Medical University between 2016 and 2019. To compare survival rates among the different groups, Kaplan-Meier curves were calculated, and the log-rank test was used. A multivariate Cox proportional hazard model was constructed utilizing variables that were significant in univariate analysis of survival. Results In the study, 564 patients were included, with 298 patients (52.8%) undergoing sublobar resection and 266 patients (47.2%) undergoing lobar resection. Regarding survival results, there was no significant difference in the 5-year overall survival (OS, P = 0.674) and 5-year recurrence-free survival (RFS, P = 0.253) between the two groups. Cox regression analyses showed that CTR ≥ 0.75(P 56 years (P = 0.007), and sublobar resection(P = 0.001) could predict worse survival. After examining survival results based on CTR categorization, we segmented the individuals into three categories: CTR

This work described a novel non-contact, wearable, real-time eye blink detection solution based on capacitive sensing technology. A custom-built prototype employing low-cost and low-power consumption capacitive sensors was integrated into standard glasses, with a copper tape electrode affixed to the frame. The blink of an eye induces a variation in capacitance between the electrode and the eyelid, thereby generating a distinctive capacitance-related signal. By analyzing this signal, eye blink activity can be accurately identified. The effectiveness and reliability of the proposed solution were evaluated through five distinct scenarios involving eight participants. Utilizing a user-dependent detection method with a customized predefined threshold value, an average precision of 92% and a recall of 94% were achieved. Furthermore, an efficient user-independent model based on the two-bit precision decision tree was further applied, yielding an average precision of 80% and an average recall of 81%. These results demonstrate the potential of the proposed technology for real-world applications requiring precise and unobtrusive eye blink detection.

Urban renewal has led to the proliferation of informal urban habitats, such as slums, shanty towns, and urban villages (UVs). As an important component of urban renewal, UVs influence urban spatial structure and land use patterns. Therefore, the fine extraction of UV is of great theoretical and practical significance. Existing UV classification techniques mostly employ machine learning and convolutional neural network based models, which struggle to perceive long-range global semantic information. In this article, based on high-resolution remote sensing images, we propose a multiscale mask transformer model for UV (MaskUV). It can extract both local texture features and global features. The multiscale mask transformer module with mask attention can aggregate different levels of pixel and object features, enhancing the model's recognition and generalization abilities. We extracted UV in seven cities in the Pearl River Delta (PRD) using MaskUV and analyzed the spatial pattern and accessibility of UV. Due to the scarcity of fine-grained UV detection datasets, we also provide a novel dataset (UVSet) containing 3415 pairs of 512 × 512 high-resolution UV images and labels, with a spatial resolution of 1 m. Comparative experiments with several UV extraction models demonstrate the effectiveness of MaskUV, achieving an F1 score of 84.39% and an IoU of 73.00% on UVSet. Besides, MaskUV achieves highly accurate detection results in seven cities in the PRD, with average F1 and IoU values of 84.41% and 72.44%, respectively.

Abstract Background To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system). Methods Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021–June 30, 2022), SC Center (September 1, 2021–December 31, 2021), and SX Center (January 1, 2021–December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test. Results For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P 35.1%, PSN 13.3% > 5.7%, all P 74.5%, P 92.7%, all P

Abstract Background We aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC). Method We reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value. Results A total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p

Abstract The epitaxial growth of wafer-scale single-crystalline two-dimensional materials requires precise control over the crystallographic orientation and morphology of clusters formed during the initial stages of nucleation. However, there is limited knowledge about the critical nucleus and its growth mechanism for h-BN on high-symmetry surfaces of transition metals. In this study, we provide atomic insights into h-BN nucleation on Ir(111) using scanning tunneling microscopy and noncontact atomic force microscopy, corraborated by density functional theory (DFT) calculations. The atomic-resolved structural characterization reveals that the smallest h-BN cluster, exhibiting a non-1:1 stoichiometric ratio of boron and nitrogen atoms, maintains a triangular shape with zigzag-type edges. Through force spectroscopy, individual B and N atoms within the cluster, as well as the N-termination edge, are clearly identified, indicating the atomic-scale elemental sensitivity of the chemical-reactive metallic tip. While DFT calculations suggest that the NtopBfcc configuration is thermodynamically favored for larger clusters, a predominant crystalline orientation is observed for nuclei of various sizes, unequivocally attributed to the NtopBhcp registry. This orientation pinning behavior presents opportunities for large-scale growth of binary and ternary materials by impeding rotational alignment of nuclei in the early stages of nucleation.

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.

Abstract The chiral charge density wave is a many-body collective phenomenon in condensed matter that may play a role in unconventional superconductivity and topological physics. Two-dimensional chiral charge density waves provide the building blocks for the fabrication of various stacking structures and chiral homostructures, in which physical properties such as chiral currents and the anomalous Hall effect may emerge. Here, we demonstrate the phase manipulation of two-dimensional chiral charge density waves and the design of in-plane chiral homostructures in 1T-TaS2. We use chiral Raman spectroscopy to directly monitor the chirality switching of the charge density wave—revealing a temperature-mediated reversible chirality switching. We find that interlayer stacking favours homochirality configurations, which is confirmed by first-principles calculations. By exploiting the interlayer chirality-locking effect, we realise in-plane chiral homostructures in 1T-TaS2. Our results provide a versatile way to manipulate chiral collective phases by interlayer coupling in layered van der Waals semiconductors.

This study examined the impact of sodium propionate concentration (0–40 g/L) on the methanogenic archaea in an inoculum which was cultured in basal nutrient medium, exploring its mechanisms and nonlinear stress intensity. The results indicated that at low concentrations, propionate-maintained homeostasis of the anaerobic digestion (AD) system and enriched Methanosaeta. However, when the concentration exceeded 16 g/L, the stability of the AD system was disrupted. The methanogenic pathway shifted towards a predominantly hydrogenotrophic pathway, resulting in a significant increase in methane yield. Below concentrations of 28 g/L, the AD system gradually enhanced its ability to utilize propionate in an orderly manner. At concentrations of 24–28 g/L, genera (e.g., Advenella and Methanosarcina) were enriched to adapt to the high-VFA environment. This was accompanied by a significant upregulation of genes related to the methylotrophic and hydrogenotrophic pathways, effectively mitigating propionate inhibition and enhancing methanogenesis. Conversely, excess concentrations (>30 g/L) suppressed methanogenesis-related genes and led to methane production arrest despite activating specialized propionate-metabolizing bacteria such as genus Pelotomaculum schinkii. As such, an increase in the stress intensity of propionate promotes a change in the metabolic pathways of methanogens and increases methane production; however, excessive sodium propionate was not conducive to maintaining the steady state of the system.

Cas12g is an endonuclease belonging to the type V RNA-guided CRISPR-Cas family. It is known for its ability to cleave RNA substrates using a conserved endonuclease active site located in the RuvC domain. In this study, we determined the crystal structure of apo-Cas12g, the cryo-EM structure of the Cas12g-sgRNA binary complex and investigated conformational changes that occur during the transition from the apo state to the Cas12g-sgRNA binary complex. The conserved zinc finger motifs in Cas12g undergo an ordered-to-disordered transition from the apo to the sgRNA-bound state and their mutations negatively impact on target RNA cleavage. Moreover, we identified a lid motif in the RuvC domain that undergoes transformation from a helix to loop to regulate the access to the RuvC active site and subsequent cleavage of the RNA substrate. Overall, our study provides valuable insights into the mechanisms by which Cas12g recognizes sgRNA and the conformational changes it undergoes from sgRNA binding to the activation of the RNase active site, thereby laying a foundation for the potential repurposing of Cas12g as a tool for RNA-editing.

As high temperature and heat wave have become great threats to human survival, social stability, and ecological safety, it is of great significance to master the spatial and temporal dynamic changes of temperature to prevent high temperature and heat wave risks. The meteorological station can provide accurate near-ground temperature, but only within a specific space and time. In order to meet the needs of large-scale research, spatial interpolation methods were widely used to obtain spatially continuous temperature maps. However, these methods often ignore the influence of external factors on temperature, such as land cover, height, etc., and neglect to supplement temporal-wise information. To deal with these issues, a joint spatio-temporal method is proposed to obtain dense temperature mapping from multisource remote sensing data, which combining a geographically weighted regression model and a polynomial fitting model. Besides, a heat wave risk model is also built based on the dense temperature maps and population data, in order to evaluate the heat wave risk of different areas. Accuracy evaluations and experiments have verified the effectiveness of the proposed methods. Case study on the four cities of Zhejiang Province, China have demonstrated that areas with higher degree of urbanization are often accompanied by higher heat wave risks, such as the northern part of the study area. The study also found that the heat wave risks have presented a centralized distribution and spatial autocorrelation characteristics in the study area.

ObjectiveTo investigate the diagnostic value of dermoscopy in defining the tumor margin of cutaneous squamous cell carcinoma (cSCC) for the appropriate surgical margin.MethodsA total of 90 cSCC patients were enrolled in the study. All patients were recruited into two groups: those who preserved intact macroscopic features of neoplasms without or after incisional biopsy and those with uncertain residual tumors after excisional biopsy. A dermoscopy-defined surgical margin of 8mm outward was used according to the tumor boundaries observed with the naked eye and dermoscopy. All excised tumor specimens were divided into serial sections according to the four “3, 6, 9, 12” directions at every 4-mm interval from the dermoscopy-detected tumor margin. Pathological examination was performed at 0 mm, 4 mm, and 8 mm margins to confirm tumor remnants.ResultsRetrospective analysis of dermatoscopic results showed inconsistent clinical and dermatoscopic borders in 43 of 90 cases (47.8%). The ability of dermoscopy to detect tumor borders showed no statistical difference between the two groups (p > 0.05). In the unbiopsy or incisional biopsy group, 66.6% of the tumors were resected with a 4-mm margin and 98.3% with an 8-mm margin, with significant differences (p = 0.047). For patients with inconspicuous clinical evidence of residual tumor after excisional biopsy, the tumor clearance rate was 53.3% at 0 mm, 93.3% at 4 mm, and 100.0% at 8 mm. Statistically significant differences were noted between 0 mm and 4 mm (p = 0.017), as well as between 0 mm and 8 mm (p = 0.043) but did not differ between 4 mm and 8 mm (p > 0.05).ConclusionsDermoscopy defined the tumor margin of cSCC better than visual inspection alone. Direct dermoscopic-guided surgery with at least 8-mm expansion was recommended for high-risk cSCC. Dermoscopy also assisted in identifying surgical margins at the healing biopsy site, making 8 mm still the recommended expansion range.

Derris trifoliate, one of the most notorious invaders of mangroves in South China, seriously threatens the growth of mangroves and the stability of local ecosystem. In order to effectively control the spread of Derris trifoliate and stabilize the mangrove ecosystem, it is necessary to identify the distribution and simulate the occurrence of Derris trifoliate in mangrove forests. While previous methods for invasive plant mapping based on satellite images were limited by low temporal and spatial resolution, the newly born unmanned aerial vehicle (UAV) images have provided a fast, fine-grained, and low-cost alternative for rapid invasive plant monitoring. However, extracting Derris trifoliate in mangrove forests based on UAV images is still a challenge. For one thing, it is difficult to collect enough data for model training, since the Derris trifoliate is hard to be distinguished in mangroves. For another thing, few existing methods can meet the requirements of high computing efficiency and low memory consumption of UAVs. Therefore, we proposed two lightweight deep learning networks based on DenseNet and VGG, namely, the lightweight DenseNet (LDN) and the lightweight VGG (LVG), and investigated the capability of LDN and LVG in identifying Derris trifoliate from mangrove forests with small amounts of data. This study has verified the effect of the lightweight deep learning algorithms for the accurate detection of mangrove invasive plants Derris trifoliate from UAV images.

Nonagriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intraclass differences of cropland changes in high-resolution images. In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, in this article, we propose a CNN-transformer network with multiscale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland CD. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based MSCA is designed to encode and aggregate context information. Finally, a multibranch prediction head with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of CD dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset, which contains 600 pairs of 512 × 512 bi-temporal images with the spatial resolution of 0.5–2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic CD dataset, and of 71.29% on CLCD.

Built-up area change detection (CD) plays an important role in city management, which always uses very high spatial resolution (VHR) remote sensing data to extract refined spatial information. Recently, many CD models based on deep learning with VHR data have been proposed. However, due to the complex background information and natural landscape changes, VHR with optical RGB features is hard to extract changes exactly. To this end, we tend to explore the abundant channel information of multispectral and SAR data as a supplement to the refined spatial features of VHR images. We propose a new deep learning framework called multisource CD UNet++ (MSCDUNet), integrating multispectral, SAR, and VHR data for built-up area CD. First, we label and reform two new built-up area CD datasets containing multispectral, SAR, and VHR data: multisource built-up change (MSBC) and multisource OSCD (MSOSCD) datasets. Second, a feature selection method based on random forest is introduced to choose effective features from multispectral and SAR images. Finally, a multilevel heterogeneous feature fusion module is embedded in MSCDUNet to combine multifeatures for CD. Experiments are conducted on both the MSOSCD and the MSBC datasets. Compared to other CD methods based on VHR images, our proposal achieves the highest accuracy on both datasets and proves the effectiveness of multispectral, SAR, and VHR data fusion for CD. The dataset in the article will be available for download from the following link.1

BackgroundOnychopapilloma is generally recognized as a benign tumor of the nail bed and distal matrix. However, the origin of onychopapilloma has not been explained yet.ObjectiveTo clarify the origin of onychopapilloma, we detected the expression patterns of hair-related keratins and epithelial keratins, which are expressed specifically in the nail unit.Materials and methodsThe clinical and histopathologic features of 11 patients with onychopapilloma were analyzed, and the expression patterns of hair-related and epithelial keratins were detected.ResultsHistologically, all subjects showed acanthosis, papillomatosis and matrix metaplasia within the nail bed. Immunohistochemically, the expression pattern of keratins in our standard nail unit was consistent with previous reports. “Nail matrix-related keratins” HK31, HK34, HK85, and HK86 were only expressed in the nail matrix, and “Nail bed-related keratins” HK75 and K6/K16 were only expressed in the nail bed. However, in onychopapilloma, whether adjacent to the matrix or in the distal nail bed, all cases were positive for nail bed-related keratins and HK31 but negative for other nail matrix-related keratins.ConclusionOur study suggests that onychopapilloma may originate from the nail bed rather than the nail matrix. Furthermore, the expression of nail bed-related keratins and HK31 could be used as diagnostic markers of onychopapilloma.

Background: In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. Methods: The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN–vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. Results: Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p < 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). Conclusions: In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022.

Hydrothermal synthesis was used to synthesize SnCNx photocatalysts with different SnO2 content. The SnCN4 photocatalyst has a well photocatalytic degradation efficiency for pollutants under visible light irradiation. Rising photoactivity may be related to the synergistic effect between SnO2 and g-C3N4, which leads to an increase in visible light absorption. The interface interaction between the two components promotes effective charge transfer, thereby increasing visible light absorption. The ·O2¯ and ·OH are the main reaction species in the degradation reaction and we also discussed the possible mechanism of this process.

Electrical impedance spectroscopy (EIS) is widely recognized as a powerful tool in biomedical research. For example, it allows detection and monitoring of diseases, measuring of cell density in bioreactors, and characterizing the permeability of tight junctions in barrier-forming tissue models. However, with single-channel measurement systems, only integral information is obtained without spatial resolution. Here we present a low-cost multichannel impedance measurement set-up capable of mapping cell distributions in a fluidic environment by using a microelectrode array (MEA) realized in 4-level printed circuit board (PCB) technology including layers for shielding, interconnections, and microelectrodes. The array of 8 × 8 gold microelectrode pairs was connected to home-built electric circuitry consisting of commercial components such as programmable multiplexers and an analog front-end module which allows the acquisition and processing of electrical impedances. For a proof-of-concept, the MEA was wetted in a 3D printed reservoir into which yeast cells were locally injected. Impedance maps were recorded at 200 kHz which correlate well with the optical images showing the yeast cell distribution in the reservoir. Blurring from parasitic currents slightly disturbing the impedance maps could be eliminated by deconvolution using an experimentally determined point spread function. The MEA of the impedance camera can in future be further miniaturized and integrated into cell cultivation and perfusion systems such as organ on chip devices to augment or even replace light microscopic monitoring of cell monolayer confluence and integrity during the cultivation in incubation chambers.

Finding reliable miRNA markers and revealing their potential mechanisms will play an important role in the diagnosis and treatment of NSCLC. Most existing computational methods for identifying miRNA biomarkers only consider the expression variation of miRNAs or rely heavily on training sets. These deficiencies lead to high false-positive rates. The independent regulatory model is an important complement to traditional models of co-regulation and is more impervious to the dataset. In addition, previous studies of miRNA mechanisms in the development of non-small cell lung cancer (NSCLC) have mostly focused on the post-transcriptional level and did not distinguish between NSCLC subtypes. For the above problems, we improved mainly in two areas: miRNA identification based on both the NOG network and biological functions of miRNA target genes; and the construction of a 4-node directed competitive regulatory network to illustrate the mechanisms. NSCLC was classified as lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) in this work. One miRNA biomarker of LUAD (miR-708-5p) and four of LUSC (miR-183-5p, miR-140-5p, miR-766-5p, and miR-766-3p) were obtained. They were validated using literature and external datasets. The ceRNA-hub-FFL involving transcription factors (TFs), microRNAs (miRNAs), mRNAs, and long non-coding RNAs (lncRNAs) was constructed. There were multiple interactions among these components within the net at the transcriptional, post-transcriptional, and protein levels. New regulations were revealed by the network. Meanwhile, the network revealed the reasons for the previous conflicting conclusions on the roles of CD44, ACTB, and ITGB1 in NSCLC, and demonstrated the necessity of typing studies on NSCLC. The novel miRNA markers screening method and the 4-node directed competitive ceRNA-hub-FFL network constructed in this work can provide new ideas for screening tumor markers and understanding tumor development mechanisms in depth.

Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

Coal mining involves numerous challenges and safety risks owing to the complex engineering properties of coal bodies, which include discontinuities, heterogeneity, and anisotropy. In this paper, the strain energy during the coal deformation process is redivided in combination with cyclic loading and unloading tests to determine the energy evolution law and discuss the rockburst tendency characteristics. The results show that the elastic strain energy, and particularly the base-material strain energy, consistently dominates during the energy adjustment process, which is an important indicator of rockburst tendency. The elastic energy index and rebound deformation index also show that moderate plastic deformation (e.g., crack expansion and local penetration) can reduce the rockburst tendency level and prevent rockburst accidents. On the basis of the obtained results, precracking and pressure relief measures of blasting are adopted on site in advance of the working face, and good safety and economic benefits are achieved. These findings, thus, provide an important engineering reference for mines under similar conditions.

Coal is typically considered a special engineering rock mass because of its low strength, high internal fracture development, good permeability, and random distribution of microparticles and fractures. The results of cyclic loading and unloading tests indicate that the strain energy during the coal deformation process can be divided into three parts: plastic strain energy; fracture strain energy; and base-material strain energy. The energy composition ratio differs depending on coal strength. Lower proportions of fracture strain energy are associated with higher elastic energy indexes, and there is a negative correlation between fracture strain energy and other coal burst tendency indexes. The results were applied on the 4206 isolated island working face of coal mine A in Yan’an, Shanxi, China, yielding good benefits. The findings presented here provide a theoretical basis for understanding the principle of coal seam bursting and guidance for reducing burst risks.

In order to further explore the deformation and failure essence of the deep coal body, based on the characteristics of surrounding rock stress adjustment before and after solid coal roadway excavation, an experiment of unloading confining pressure and loading axial pressure of the coal body was designed and conducted in this study. Based on test results, the failure mechanics and energy characteristics of the coal body were analyzed through experiments. Rapid unloading is considered a key factor contributing to lateral deformation and expansion failure, which exacerbates the deterioration of coal body and reduces the deformation energy storage capacity of coal. On the other hand, the larger loading rate tends to shorten the accumulation time of microcracks and cause damage to the coal body, resulting in strengthening the coal body and improving energy storage. Under the circumstance that the coal body is destroyed, the conversion rates of the internal deformation energy and dissipated energy are more significantly affected by unloading rate. The increasing unloading rate and rapid decreases in the conversion rate of deformation energy make the coal body more vulnerable to damage. Under the same stress conditions, the excavation unloading is more likely to deform, destroy, or even throw the coal than the experiment unloading. In order to reduce or avoid the occurrence of deep roadway excavation accidents, the understanding of the excavation unloading including possible influencing factors and the monitoring of the surrounding rock stress and energy during the excavation disturbance should be strengthened. It can be used as the basis for studying the mechanism of deformation and failure of coal and rock and dynamic disasters in deep mines, as well as the prediction, early warning, prevention, and control of related dynamic disasters.

Methyl 2 -[ [ 1- (5- fluoropentyl) indole - 3- carbonyl] amino] -3, 3- dimethyl - butanoate (5F-MDMB-PICA) is a new synthetic cannabinoid characterized by valinate or tert-leucinate moieties. In recent years, 5F-MDMB-PICA has been abused in the form of “spice-like” herbal incenses or electronic cigarette oil. A UHPLC-MS/MS method was developed to detect 5F-MDMB-PICA and its metabolites in human hair. Approximately 20 mg of hair was weighed and pulverized with methanol below 4°C. After ultrasonication, centrifugation and filtration, 200 μL of supernatant was placed into an autosampler vial and analyzed on a Waters Acquity UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm particle size) using an acetonitrile-20 mmol/L ammonium acetate (0.1% formic acid, 5% acetonitrile) gradient with a run time of 8 min. The limit of detection (LOD) ranged from 0.5 to 5 pg/mg, and the lower limit of quantitation (LLOQ) ranged from 1 to 5 pg/mg. The method was shown to be linear over a concentration range of 1–200 pg/mg. The linear correlation (R2) of the calibration curves for all analytes was >0.999. The accuracy varied from 95.4 to 107.4%, while the intra- and inter-day precision RSD values were 0.7–10.6% and 1.7–12.2%, respectively. Recoveries were within the range of 61.1–93.3%, and matrix effects were in the range of 19.1–102.6%. The validated method was successfully applied to the identification and quantification of 5F-MDMB-PICA and its metabolites in hair from authentic forensic cases.

Radialenes have distinct structural, electronic and chemical properties from other hydrocarbons, but their synthesis remains a challenge. Here, the authors report a copper catalyzed one-step synthetic protocol of [4]radialene via the cyclotetramerization of phenylacetylene molecules upon thermal activation.

Graphene nanoribbons consist of carbon atoms arranged in a hexagonal lattice. Despite non-hexagonal rings generally being more unstable, the authors demonstrate the successful synthesis of graphene-like nanoribbons with periodically embedded four- and eight-membered carbon rings, with tailored electronic properties.

Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.

The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.