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Cancer stem cells (CSC) can be identified by modifications in their genomic DNA. Here, we report a concept of precisely shrinking an organic semiconductor surface-enhanced Raman scattering (SERS) probe to quantum size, for investigating the epigenetic profile of CSC. The probe is used for tag-free genomic DNA detection, an approach towards the advancement of single-molecule DNA detection. The sensor detected structural, molecular and gene expression aberrations of genomic DNA in femtomolar concentration simultaneously in a single test. In addition to pointing out the divergences in genomic DNA of cancerous and non-cancerous cells, the quantum scale organic semiconductor was able to trace the expression of two genes which are frequently used as CSC markers. The quantum scale organic semiconductor holds the potential to be a new tool for label-free, ultra-sensitive multiplexed genomic analysis. The low detection sensitivity of organic semiconductors has limited their use in biomedical surface-enhanced Raman scattering applications. Here, the authors use quantum scale organic semiconductors and show detection of genomic DNA methylation as well as gene expression.

Surface-enhanced Raman scattering (SERS)-based cancer diagnostics is an important analytical tool in early detection of cancer. Current work in SERS focuses on plasmonic nanomaterials that suffer from coagulation, selectivity, and adverse biocompatibility when used in vitro, limiting this research to stand-alone biomolecule sensing. Here we introduce a label-free, biocompatible, ZnO-based, 3D semiconductor quantum probe as a pathway for in vitro diagnosis of cancer. By reducing size of the probes to quantum scale, we observed a unique phenomenon of exponential increase in the SERS enhancement up to ~10 6 at nanomolar concentration. The quantum probes are decorated on a nano-dendrite platform functionalized for cell adhesion, proliferation, and label-free application. The quantum probes demonstrate discrimination of cancerous and non-cancerous cells along with biomolecular sensing of DNA, RNA, proteins and lipids in vitro. The limit of detection is up to a single-cell-level detection. Surface enhanced Raman scattering is a bio-analytical tool and the development and optimisation of probes is an active area of investigation. Here, the authors report on the development and testing of biocompatible semiconductor zinc oxide quantum probes on a platform for cell adhesion and analysis.

The rapid development of science and technology has led to an increasing number of high-tech enterprises offering new products through successive generations of product upgrades. This trend presents a new challenge for the sustainable operations of enterprises. Based on the Norton-Bass model, this study begins by constructing a multi-generation product diffusion model within a single enterprise in the context of a monopoly under the quality upgrade scenario. Subsequently, a supply model is established based on this foundation, and these two models are seamlessly integrated using product sales volume as an interface, culminating in a comprehensive sales-supply system. This study analyzes the effects of new-product pricing, quality levels, initial stock, and production capacity on the performance of this system. The system dynamics (SD) method was used to simulate and solve the system in the decentralized and centralized decision-making modes, and the two decision-making modes were compared and analyzed. The research reveals several key findings. i) Comprehensive decision optimization yields enhanced profitability through joint optimization calculation of the multi-generation product diffusion system and the supply adjustment system. ii) consumer price sensitivity significantly affects product quality upgrades and profits. A negative correlation exists between consumer price sensitivity and both factors. The upgrades of product quality should be carefully traded off with consideration of pricing and quality costs. iii) Maximizing profits by maintaining a certain order level of backlog or stock shortage is beneficial for overall enterprise profitability. Additionally, optimal production capacity has been identified as a crucial element in efficient operational inventory management. This study expands the multi-generation product diffusion operational theory and provides valuable theoretical support and decision-making foundations for the sustainable management of enterprises.

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.

Advancing the development of centers for disease control and prevention (CDCs) has become a priority within global public health governance. However, public health governance capacity varies significantly among CDCs across different countries and regions, grassroots CDCs face particular disadvantages. Establishing stable, efficient collaborative development mechanisms among CDCs across diverse regions to maximize overall effectiveness and ensure sustainable development represents a critical public health science issue. This study aims to provide scientific references and a theoretical foundation for the coordinated development of grassroots CDCs within the Chengdu-Chongqing Economic Circle (CCEC) and the construction of public health systems. A questionnaire for collaborative development needs indicators in grassroots CDCs, comprising 4 primary needs and 13 secondary needs, was developed through literature review, the Delphi expert consultation method, and the Kano model. Analysis focused on questionnaires collected from eight grassroots CDCs within the CCEC. The importance of needs was ranked using the better-worse coefficient and satisfaction sensitivity analysis. Analysis of the 110 valid questionnaires showed that for the must-be attribute, satisfaction sensitivity ranked as follows: performance compensation (0.883)> talent exchange and scientific research and innovation cooperation (0.824)> public health emergency rescue mechanism (emergency material reserve and cross-regional material mobilization; 0.817)> cross-regional case monitoring, investigation, and tracking (0.775). Regarding the one-dimensional attribute, the satisfaction sensitivity ranking was joint risk assessment and emergency command (0.937)> business archive co-construction and sharing mechanism (emergency response plan, and technical scheme) (0.909)> regional co-construction and sharing between the university and the local area (0.832). For the attractive attribute, the satisfaction sensitivity ranking was regional monitoring and early-warning information management system (0.922)> community chronic disease prevention and service (0.804)> coordinated transfer and diversion diagnosis and treatment of patient with infectious diseases within the region (0.734). However, the collaborative release and interaction mechanism of social integrated media information, public health collaborative governance entities, and the construction of a cross-regional expert database constitute indifferent attributes. This study provides preliminary scientific evidence for the precise allocation of public health resources and the establishment of localized collaborative development mechanisms. Simultaneously, the research methodology and analytical framework offer new theoretical references for similar studies in other regions globally.

Natural Killer (NK) cells, a subset of innate immune cells, undergo cancer-specific changes during tumor progression. Therefore, tracking NK cell activity in circulation has potential for cancer diagnosis. Identification of tumor associated NK cells remains a challenge as most of the cancer antigens are unknown. Here, we introduce tumor-associated circulating NK cell profiling (CNKP) as a stand-alone cancer diagnostic modality with a liquid biopsy. Metabolic profiles of NK cell activation as a result of tumor interaction are detected with a SERS functionalized OncoImmune probe platform. We show that the cancer stem cell-associated NK cell is of value in cancer diagnosis. Through machine learning, the features of NK cell activity in patient blood could identify cancer from non-cancer using 5uL of peripheral blood with 100% accuracy and localization of cancer with 93% accuracy. These results show the feasibility of minimally invasive cancer diagnostics using circulating NK cells. NK cells can be affected by tumour cells and this difference could be utilised as a cancer diagnostic. Here the authors use a nickel based plasmonic spectroscopy system to measure metabolic differences in NK cells that have been exposed to cancer cells as a method of cancer detection.

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.

Phylogenomic data enriched with informative loci have significantly improved phylogenetic resolution and facilitated the elucidation of evolutionary mechanisms underlying phylogenetic discordance. Species of the East Asian lineage (EAL) within the Allium subgenus Anguinum are widespread in the Himalaya–Hengduan Mountains (HHMs), which exhibit long-standing taxonomic ambiguity and phylogenetic discordance, calling for investigation. In this study, we collected 102 samples, including 45 transcriptomes and 57 plastid genomes, covering multiple populations of all currently recognized taxa within the EAL and relatives. A total of 2,186 low-copy nuclear genes (LCGs) and 163 plastid sequences (including 111 genes and 52 intergenic regions) were employed for phylogenetic analyses. Our results revealed that the EAL is a monophyletic taxon but exhibits a polytomous phylogeny, it further divides into four sublineages in the LCG-based tree and two sublineages in the plastid-based tree, which display distinct geographical distribution patterns. Samples of A. ovalifolium var. leuconeurum , A. ovalifolium var. cordifolium and A. funckiifolium from the northwest Sichuan Basin and Qinling-Daba Mountains clustered within the A. ovalifolium samples of these regions, while A. nanodes is entirely embedded within the HHMs populations of A. prattii and A. ovalifolium . Extensive phylogenetic conflicts were detected within EAL, and the ancestral area reconstruction indicates that the EAL originated in the Hengduan Mountains (HDMs). Morphological and phylogenetic evidence confirmed the varietal status of A. ovalifolium var. leuconeurum and A. ovalifolium var. cordifolium , while also proposing the reclassification of A. funckiifolium as A. ovalifolium var. funckiifolium . The observed polytomous phylogeny within EAL is likely attributed to rapid radiations triggered by geological events and climatic fluctuations during the late Pliocene and Pleistocene, coupled with recurrent isolation–contact dynamics, which resulted in the retention of ancestral polymorphisms and historical gene flow. Widespread phylogenetic discordance in the EAL is mainly due to incomplete lineage sorting (ILS), with hybridization also playing key roles. This study not only reveals the underlying causes of taxonomic controversies within the EAL but also provides critical insights into the unique phylogenetic patterns and evolutionary mechanisms shaping plant lineages in the HHMs biodiversity hotspot.

Due to their low viscosity, high mobility, and high element contents, supercritical fluids are important agents in the cycling of elements. However, the chemical composition of supercritical fluids in natural rocks is poorly understood. Here, we investigate well-preserved primary multiphase fluid inclusions (MFIs) from an ultrahigh-pressure (UHP) metamorphic vein of the Bixiling eclogite in Dabieshan, China, thus providing direct evidence for the components of supercritical fluid occurring in a natural system. Via the 3D modeling of MFIs by Raman scanning, we quantitatively determined the major composition of the fluid trapped in the MFIs. Combined with the peak-metamorphic pressure–temperature conditions and the cooccurrence of coesite, rutile, and garnet, we suggest that the trapped fluids in the MFIs represent supercritical fluids in a deep subduction zone. The strong mobility of the supercritical fluids with respect to carbon and sulfur suggests that such fluids have profound effects on global carbon and sulfur cycling.