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A novel method is introduced to improve the detection performance of photoacoustic spectroscopy for trace gas detection. For effectively suppressing various types of noise, this method integrates photoacoustic spectroscopy with residual networks model which encompasses a total of 40 weighted layers. Firstly, this approach was employed to accurately retrieve methane concentrations at various levels. Secondly, the analysis of the signal-to-noise ratio (SNR) of multiple sets of photoacoustic spectroscopy signals revealed significant enhancement. The SNR was improved from 21 to 805, 52–962, 98–944, 188–933, 310–941, and 587–936 across the different concentrations, respectively, as a result of the application of the residual networks. Finally, further exploration for the measurement precision and stability of photoacoustic spectroscopy system utilizing residual networks was carried out. The measurement precision of 0.0626 ppm was obtained and the minimum detectable limit was found to be 1.47 ppb. Compared to traditional photoacoustic spectroscopy method, an approximately 46-fold improvement in detection limit and 69-fold enhancement in measurement precision were achieved, respectively. This method not only advances the measurement precision and stability of trace gas detection but also highlights the potential of deep learning algorithms in spectroscopy detection.

BackgroundChatGPT (OpenAI) has shown great potential in clinical diagnosis and could become an excellent auxiliary tool in clinical practice. This study investigates and evaluates ChatGPT in diagnostic capabilities by comparing the performance of GPT-3.5 and GPT-4.0 across model iterations. ObjectiveThis study aims to evaluate the precise diagnostic ability of GPT-3.5 and GPT-4.0 for colon cancer and its potential as an auxiliary diagnostic tool for surgeons and compare the diagnostic accuracy rates between GTP-3.5 and GPT-4.0. We precisely assess the accuracy of primary and secondary diagnoses and analyze the causes of misdiagnoses in GPT-3.5 and GPT-4.0 according to 7 categories: patient histories, symptoms, physical signs, laboratory examinations, imaging examinations, pathological examinations, and intraoperative findings. MethodsWe retrieved 316 case reports for intestinal cancer from the Chinese Medical Association Publishing House database, of which 286 cases were deemed valid after data cleansing. The cases were translated from Mandarin to English and then input into GPT-3.5 and GPT-4.0 using a simple, direct prompt to elicit primary and secondary diagnoses. We conducted a comparative study to evaluate the diagnostic accuracy of GPT-4.0 and GPT-3.5. Three senior surgeons from the General Surgery Department, specializing in Colorectal Surgery, assessed the diagnostic information at the Chinese PLA (People’s Liberation Army) General Hospital. The accuracy of primary and secondary diagnoses was scored based on predefined criteria. Additionally, we analyzed and compared the causes of misdiagnoses in both models according to 7 categories: patient histories, symptoms, physical signs, laboratory examinations, imaging examinations, pathological examinations, and intraoperative findings. ResultsOut of 286 cases, GPT-4.0 and GPT-3.5 both demonstrated high diagnostic accuracy for primary diagnoses, but the accuracy rates of GPT-4.0 were significantly higher than GPT-3.5 (mean 0.972, SD 0.137 vs mean 0.855, SD 0.335; t285=5.753; P

The dual energy X-ray transmission identification of coal gangue still faces challenges in thickness, hardening, afterglow, and fan-shaped effects, among which the parameters for 5-150 mm wide thickness coal gangue separation fluctuate significantly and the recognition rate is to be improved. Therefore, this paper proposes a multi-dimensional identification method of dual-energy X-ray transmission of coal gangue based on geometric feature constraints. This method distinguishes the thickness of coal gangue by two geometric features of the minimum circumscribed circle diameter and area of the target image, restricts the spatial distribution of X-ray transmission response characteristics, and then weakens the influence of defects from multiple dimensions. With a small amount of low-density coal and high-density gangue, the paper obtains X-ray transmission response characteristics, position characteristics, and geometric characteristics, and combine them with Relief-F feature selection to establish a strong feature combination. To test the recognition performance of multiple classifiers, medium Gaussian SVM is selected as the classification model for multi-dimensional methods. Taking strong feature combinations as input, the final decision model and classification of unknown coal gangue pixels are automatically created, and the separation parameter p-value is obtained through pixel transformation image processing method. The results show that there is a strong linear correlation between p-value and coal gangue density, and density can be used to select p-value to regulate sorting. The p-value shows a weak linear correlation with the thickness of coal gangue. Within a wide thickness range, the p-value has a small degree of dispersion and good separability, giving separation parameters a large adjustment space. The mass experimental verification results show that the p-value of the multi-dimensional method for pre discharge gangue separation parameter is 33.01%. Using this separation parameter to identify coal gangue with different densities and coal types, the overall recognition rate reaches 99.57%. The overall recognition rate of raw coal pre discharge gangue in the thickness range of 5-150 mm is 99.37%. Compared with H-L method and R-L method, the multi-dimensional method has higher recognition rate, and the p-value calculated for coal gangue with different thickness has higher accuracy and better consistency. It demonstrates the effectiveness of multi-dimensional recognition methods under geometric feature constraints and the advantages of separation parameter regulation, which is of design reference meaning for current dual energy X-ray coal gangue separation device recognition algorithms.

Abstract Parkinson’s disease is managed using levodopa; however, as Parkinson’s disease progresses, patients require increased doses of levodopa, which can cause undesirable side effects. Additionally, the oral bioavailability of levodopa decreases in Parkinson’s disease patients due to the increased metabolism of levodopa to dopamine by gut bacteria, Enterococcus faecalis, resulting in decreased neuronal uptake and dopamine formation. Parkinson’s disease patients have varying levels of these bacteria. Thus, decreasing bacterial metabolism is a promising therapeutic approach to enhance the bioavailability of levodopa in the brain. In this work, we show that Mito-ortho-HNK, formed by modification of a naturally occurring molecule, honokiol, conjugated to a triphenylphosphonium moiety, mitigates the metabolism of levodopa—alone or combined with carbidopa—to dopamine. Mito-ortho-HNK suppresses the growth of E. faecalis, decreases dopamine levels in the gut, and increases dopamine levels in the brain. Mitigating the gut bacterial metabolism of levodopa as shown here could enhance its efficacy.

Abstract Background Hypoxia is a common feature of many solid tumors and causes radiotherapy and immunotherapy resistance. Pharmacological inhibition of oxidative phosphorylation (OXPHOS) has emerged as a therapeutic strategy to reduce hypoxia. However, the OXPHOS inhibitors tested in clinical trials caused only moderate responses in hypoxia alleviation or trials were terminated due to dose-limiting toxicities. To improve the therapeutic benefit, FDA approved OXPHOS inhibitors (e.g. atovaquone) were conjugated to triphenylphosphonium (TPP+) to preferentially target cancer cell’s mitochondria. In this study, we evaluated the hypoxia reducing effects of several mitochondria-targeted OXPHOS inhibitors and compared them to non-mitochondria-targeted OXPHOS inhibitors using newly developed spheroid models for diffusion-limited hypoxia. Methods B16OVA murine melanoma cells and MC38 murine colon cancer cells expressing a HIF-Responsive Element (HRE)-induced Green Fluorescent Protein (GFP) with an oxygen-dependent degradation domain (HRE-eGFP-ODD) were generated to assess diffusion-limited hypoxia dynamics in spheroids. Spheroids were treated with IACS-010759, atovaquone, metformin, tamoxifen or with mitochondria-targeted atovaquone (Mito-ATO), PEGylated mitochondria-targeted atovaquone (Mito-PEG-ATO) or mitochondria-targeted tamoxifen (MitoTam). Hypoxia dynamics were followed and quantified over time using the IncuCyte Zoom Live Cell-Imaging system. Results Hypoxic cores developed in B16OVA.HRE and MC38.HRE spheroids within 24 h hours after seeding. Treatment with IACS-010759, metformin, atovaquone, Mito-PEG-ATO and MitoTam showed a dose-dependent reduction of hypoxia in both B16OVA.HRE and MC38.HRE spheroids. Mito-ATO only alleviated hypoxia in MC38.HRE spheroids while tamoxifen was not able to reduce hypoxia in any of the spheroid models. The mitochondria-targeted OXPHOS inhibitors demonstrated stronger anti-hypoxic effects compared to the non-mito-targeted OXPHOS inhibitors. Conclusions We successfully developed a high-throughput spheroid model in which hypoxia dynamics can be quantified over time. Using this model, we showed that the mitochondria-targeted OXPHOS inhibitors Mito-ATO, Mito-PEG-ATO and MitoTam reduce hypoxia in tumor cells in a dose-dependent manner, potentially sensitizing hypoxic tumor cells for radiotherapy.

Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied. However, it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure. Here, we aim to investigate the subsurface multi-physics of reservoir landslides under two extreme hydrologic conditions (i.e. wet and dry), particularly within sliding masses. Based on ultra-weak fiber Bragg grating (UWFBG) technology, we employ special-purpose fiber optic sensing cables that can be implanted into boreholes as “nerves of the Earth” to collect data on soil temperature, water content, pore water pressure, and strain. The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring. These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide, with a resolution of 1 m except for the pressure sensor. We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole. Results show that wet years are more likely to motivate landslide motions than dry years. The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years. The dynamic groundwater table is located at depths of 9–15 m, where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles. These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability, allowing us to correlate them to local damage events and potential global destabilization. This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes, which may form the basis for a landslide monitoring and early warning system.

Background: The intrinsic healing ability of articular cartilage is poor after injury or illness, and untreated injury could lead to cartilage degeneration and ultimately osteoarthritis. iMSCs are derived from embryonic induced pluripotent stem cells and have strong therapeutic capabilities in the repair of cartilage defects, while the mechanism of action is unclear. The aim of this study is to clarify the repair mode of iMSCs on cartilage defects in rat knee joints, elucidate the chemotactic effect of iMSCs on autologous BMSCs in rats, and provide a basis for the treatment of cartilage defects and endogenous regeneration with iMSCs. Methods: Based on the establishment of the rat cartilage defect model, the reparative effect of iMSCs on the rat cartilage defect was evaluated. The cartilage repair was evaluated by quantitative score, H&E staining, Masson staining and Safranin-O staining, and the metabolic changes of iMSCs in the joint cavity were detected in vivo. The expression of SOX9, CD29, CD90, ColⅠ, ColⅡ, PCNA, SDF-1, and CXCR4 was detected by immunohistochemistry (IHC), IF, flow cytometry, respectively. After co-culturing iMSCs with BMSCs in vitro, the expression of CXCR4/SDF-1 on the cell membrane surface of BMSCs was detected by western blotting.; The level of p-Akt and p-Erk1/2 in total protein of BMSCs were detected by western blotting. Significance: Our research results provide experimental evidence for the treatment of cartilage defects and endogenous regeneration with iMSCs; This also provides new ideas for the clinical treatment of cartilage defects using iMSCs.

Acute kidney injury (AKI) is associated with a high mortality rate. Pathologically, renal ischemia/reperfusion injury (RIRI) is one of the primary causes of AKI, and hypoxia-inducible factor (HIF)-1α may play a defensive role in RIRI. This study assessed the role of hypoxia-inducible factor 1α (HIF-1α)-mediated mitophagy in protection against RIRI in vitro and in vivo. The human tubular cell line HK-2 was used to assess hypoxia/reoxygenation (H/R)-induced mitophagy through different in vitro assays, including western blotting, immunofluorescence staining, terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL), and reactive oxygen species (ROS) measurement. Additionally, a rat RIRI model was established for evaluation by renal histopathology, renal Doppler ultrasound, and transmission electron microscopy to confirm the in vitro data. The selective HIF-1α inhibitor LW6 reduced H/R-induced mitophagy but increased H/R-induced apoptosis and ROS production. Moreover, H/R treatment enhanced expression of the FUN14 domain-containing 1 (FUNDC1) protein. Additionally, FUNDC1 overexpression reversed the effects of LW6 on the altered expression of light chain 3 (LC3) BII and voltage-dependent anion channels as well as blocked the effects of HIF-1α inhibition in cells. Pretreatment of the rat RIRI model with roxadustat, a novel oral HIF-1α inhibitor, led to decreased renal injury and apoptosis in vivo. In conclusion, the HIF-1α/FUNDC1 signaling pathway mediates H/R-promoted renal tubular cell mitophagy, whereas inhibition of this signaling pathway protects cells from mitophagy, thus aggravating apoptosis, and ROS production. Accordingly, roxadustat may protect against RIRI-related AKI.

ABSTRACTDeep learning has been extensively utilized in the assessment of building damage after disasters. However, the field of building damage segmentation faces challenges, such as misjudged regions, high network complexity, and long running times. Hence, this paper proposes a two-stage building damage assessment network called the Efficient Channel Attention and Depthwise Separable Convolutional Neural Network (ECADS-CNN). It aims to quickly detect the types of disaster damage in buildings. Deep object segmentation and deep damage classification networks were integrated into a unified building damage detection network. In this study, the efficient channel attention (ECA) module was used to enhance the performance of building semantic segmentation, and a depthwise separable (DS) module was added to the dimension upscaling process. Finally, untrained disaster dataset images were used to test the robustness of the proposed model by comparing the evaluation results of each disaster. The experiments involve testing a total of five common deep learning models, and the results indicate that the ECADS-CNN model improves the speed by 7.4% and the overall F1 score by 5.2% compared with the baseline model. The comprehensive performance is better than that of mainstream deep learning models.

Abstract Background Mecapegfilgrastim, a long‐acting granulocyte‐colony stimulating factor has been approved for reducing the incidence of infection, particularly febrile neutropenia (FN), in China. Objective We conducted a multicenter prospective observational study to examine the safety and effectiveness of mecapegfilgrastim in preventing neutropenia in gastrointestinal patients receiving the chemotherapy, including S‐1/capecitabine‐based regimens or the fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFOXIRI)/fluorouracil, leucovorin, and oxaliplatin (FOLFOX)/fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFIRINOX) regimens. Method Five hundred and sixty‐one gastrointestinal patients from 40 sites across China, between May 2019 and November 2021, were included. The administration of mecapegfilgrastim was prescribed at the discretion of local physicians. Results The most common adverse drug reactions (ADRs) of any grade for all patients was increased white blood cells (2.9%). Grade 3/4 ADRs were observed for anemia (0.2%), decreased white blood cells (0.2%), and decreased neutrophil count (0.2%). Among the 116 patients who received S‐1/capecitabine‐based chemotherapy throughout all cycles, ADRs of any grade included anemia (1.7%), myalgia (0.9%), and increased alanine aminotransferase (0.9%). No grade 3/4 ADRs were observed. In 414 cycles of patients who underwent S‐1/capecitabine‐based regimens, only one (0.2%) cycle experienced grade 4 neutropenia. In the FOLFIRINOX, FOLFOXIRI, and FOLFOX chemotherapy regimens, grade 4 neutropenia occurred in one (2.7%) of 37 cycles, four (4.7%) of 85 cycles, and two (1.2%) of 167 cycles, respectively. Conclusion In a real‐world setting, mecapegfilgrastim has proven effective in preventing severe neutropenia in gastrointestinal patients following chemotherapy. This includes commonly used moderate or high‐risk FN regimens or regimens containing S1/capecitabine, all of which have demonstrated favorable efficacy and safety profiles.

Gastric cancer is characterized by a high incidence and mortality rate, with therapeutic efficacy currently constrained by substantial limitations. Aerobic glycolysis in cancer constitutes a pivotal aspect of the reprogramming of energy metabolism in tumor cells and profoundly influences the malignant progression of cancer. CircRNAs, serving as stable endogenous RNA, have been shown to regulate downstream targets by sponging miRNAs, which in turn are involved in the regulation of multiple malignant behaviors in a variety of cancers through the CircRNA-miRNA axis, suggesting that CircRNAs could be used as potential therapeutic targets for cancer. In recent years, it has been shown that some CircRNAs can be involved in the regulation of GC glycolysis, therefore, this paper summarizes the notable roles of some important CircRNAs in the regulation of GC glycolysis in recent years, which may be useful for our understanding of GC progression and the development of new therapeutic strategies.

Large electromechanical equipment typically operates under low-speed, heavy-duty conditions, significantly increasing the likelihood of bearing failures. The reduced speed diminishes the frequency of fault impacts per unit time, rendering them prone to being obscured within intricate vibration signals. To address this challenge, a novel fault weak feature extraction method is proposed for low-speed, heavy-duty operations. Initially, Improved Variational Modal Decomposition (IVMD) is employed to efficiently isolate the primary signal components from diverse signals and reconstruct them. Subsequently, a Stacking Autoencoder (SAE) is leveraged to extract higher-order features from each reconstructed signal spectrum, markedly enhancing the discriminative power between distinct signals. Lastly, Random Forest (RF) is utilized to establish a precise mapping between these higher-order features and fault category labels, enabling effective diagnosis of bearing health status. This integrated method effectively tackles the difficulties of weak fault feature extraction and classification under low-speed, heavy-duty conditions. Experimental data validates the method’s accuracy, achieving a remarkable 97% success rate.

The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system.

Short-term traffic flow prediction is crucial for intelligent transport systems and mitigating traffic congestion. Therefore, precise prediction of real-time traffic conditions is becoming more important. Currently, the existing prediction models lack the ability to effectively extract spatio-temporal characteristics and fail to adequately account for the impact of non-linear noise. To address these issues, the study proposes a hybrid short-term traffic flow prediction model based on spatio-temporal characteristics. First, the method decomposes the initial spatio-temporal traffic sequence data into multiple modal components using the complementary ensemble empirical modal decomposition method. Then, spatio-temporal characteristics are extracted from the decomposed spatio-temporal components using a deep residual network. The predicted values of each factor are combined to obtain the final predicted values. To validate the model, traffic flow data that is collected at point 4909A on the M25 motorway in London is used. The results indicate that the proposed model outperforms other models in terms of accuracy metrics such as root mean square error, mean absolute percentage error, mean absolute error, mean squared error, and coefficient of determination. Therefore, the model has high accuracy and practicality and exhibits great potential for short-term traffic flow prediction.

An automatic annotation method of coal gangue image data based on X-ray acquisition is proposed. Firstly, the manually screened coal and gangue are sent to the X-ray acquisition device for image sampling. Secondly, according to the characteristics of the pure background of coal and gangue images, a gray threshold is used to detect. Further, the mask is used to purify the detected foreground objects, which obtain a single object image. At the same time, the manually screened coal/gangue is sampled and the category information of the currently detected objects is obtained at the same time, and the monomer sample database is established. Finally, based on the monomer sample library established, the monomers randomly were selected in the sample library and combined to generate sample images with classification and location. That is, the automatic annotation of coal gangue data is completed. The results show that the automatic annotation method of coal gangue image data through digital image processing can effectively annotate the collected coal gangue image data, with a high sample generation rate and annotation accuracy of 99%.

Among tasks related to intelligent interpretation of remote sensing data, scene classification mainly focuses on the holistic information of the entire scene. Compared with pixel-level or object-based tasks, it involves a richer semantic context, making it more challenging. With the rapid advancement of deep learning, convolutional neural networks (CNNs) have found widespread applications across various domains, and some work has introduced them into scene classification tasks. However, traditional convolution operations involve sliding small convolutional kernels across an image, primarily focusing on local details within a small receptive field. To achieve better modeling of the entire image, the smaller receptive field limits the ability of convolution operation to capture features over a broader range. To this end, we introduce large kernel CNNs into the scene classification task to expand the receptive field of the mode, which allows us to capture comprehensive nonlocal information while still acquiring rich local details. However, in addition to encoding spatial association, the effective information within the feature maps is also strongly channel related. Therefore, to fully model this channel dependency, a novel channel separation and mixing module has been designed to realize feature correlation in the channel dimension. The combination of them forms a large kernel separable mixed ConvNet, enabling the model to capture effective dependencies of feature maps in both spatial and channel dimensions, thus achieving enhanced feature expression. Extensive experiments conducted on three datasets have also validated the effectiveness of the proposed method.

The Torrent frogs of the genus Amolops are widely distributed in Nepal and northern India eastwards to southern China and southwards to Malaysia. The genus currently contains 84 species. Previous studies indicated underestimated species diversity in the genus. In the context, a new species occurring from the mountains in the northwestern Guizhou Province, China is found and described based on morphological comparisons and molecular phylogenetic analyses, Amolops dafangensis sp. nov. Phylogenetic analyses based on DNA sequences of the mitochondrial 16S rRNA and COI genes supported the new species as an independent lineage. The uncorrected genetic distances between the 16S rRNA and COI genes in the new species and its closest congener were 0.7% and 2.6%, respectively, which are higher than or at the same level as those among many pairs of congeners. Morphologically, the new species can be distinguished from its congeners by a combination of the following characters: body size moderate (SVL 43.2–46.8 mm in males); head length larger than head width slightly; tympanum distinct, oval; vocal sacs absent; vomerine teeth present; dorsolateral folds weak formed by series of glands; nuptial pads present on the base of finger I; heels overlapping when thighs are positioned at right angles to the body; tibiotarsal articulation reaching the level far beyond the tip of the snout when leg stretched forward.

As a characteristic representative of the new generation of vehicles, autonomous driving is expected to improve people's driving experience. The study on traffic accident of autonomous vehicles (AVs) helps provide suggestions for autonomous driving safety from multiple disciplines and perspectives, and provide support for formulating traffic accident treatment schemes. Knowledge mapping, as a cutting-edge research method in bibliometrics, scientifically and objectively displays the relevant research status using visual means. This paper uses CiteSpace 6.1.r3 to analyze 5068 related literature on the Web of Science database from 1991 to 2022 and finds out major thematic clusters, important documents and representative journals according to citation frequency. The results show that research on traffic accidents involving AVs focuses on accident preventing technologies, including how to avoid collisions, track lane-position, and enhance vehicle-to-everything (V2X) communication. This paper extracts the mean research topics and key points involved in the field and illustrates journals related to AVs and traffic accidents, which provides guidance for subsequent researchers to carry out in-depth research and contribute their papers. Popular journals are in disciplines of mathematics, systems, computer, economics, and social science. This paper also suggests scholars to consider the aspects of scene reconstruction, cause analysis, and injury of vulnerable road users, so as to investigate traffic accidents and put forward effective treatment schemes to reduce AV accidents, and ultimately improve road safety.

We demonstrate a compact, partially end-pumping Innoslab laser based on a micro-cylindrical lens array homogenizer. A dimension of 12 × 0.4 mm2 flat-top pumping line with a Gaussian intensity distribution across the line was simulated by the ray tracing technique. The rate equations considering the asymmetric transverse spatial distributions are theoretically developed. The simulation results are in good agreement with the experimental results. Preliminary data shows that for a pump power of 260 W, a maximum pulse energy of 15.7 mJ was obtained with a pulse width of 8.5 ns at a repetition frequency of 1 kHz. The beam quality M2 factors in the unstable and stable directions were 1.732 and 1.485, respectively. The technology has been successfully applied to temperature and humidity profiling lidar and ozone lidar and has been productized, yielding direct economic value.

Diabetic wounds present significant burdens to both patients and the healthcare system due to their prolonged inflammatory phase and adverse microenvironment. Traditional Chinese medicine (TCM), particularly Scutellaria baicalensis extract (SE), has shown promise in wound healing. Herein, sesbania gum (SG) was oxidized and formed hydrogel with carboxymethyl chitosan (CMCS) through the imine bond. Then, SE was loaded into the hydrogel as a wound dressing (CMCS−OSG@SE hydrogel). In vitro experiments demonstrated the mechanical properties and ROS scavenging efficiency of the hydrogel, as well as the release of SE and its biocompatibility. In an vivo study, diabetic mice with S. aureus infection were used, and the CMCS−-OSG@SE hydrogel dressing accelerated wound healing by promoting epidermal regeneration and collagen deposition. This composite polysaccharide hydrogel loaded with SE shows great potential for diabetic wound treatment.

The Dina 2 gas field in the Kuqa Depression of the Tarim Basin is one of China’s most critical oil and gas exploration areas. Natural fractures have played an important role in the low-permeability reservoirs in the Tarim area. Tectonic fractures are dominant in such reservoirs. In fact, the factors influencing tectonic fracture development have always been the source of important issues in tight reservoirs. Cores, thin sections, and borehole image logs were used to analyze the types, basic characteristics, and factors influencing tectonic fractures in the tight sandstone reservoirs of the Dina 2 gas field in the Tarim Basin. The results showed that the tectonic fractures are dominated by high-angle and upright shearing fractures, and they mainly show ENE–WSW strikes. The thin sections suggest that 60% of the fractures are fully filled with minerals, 20% are unfilled, and 20% are partially filled. The analysis also shows that lithology, faults, and in situ stress are the main factors controlling the development of the tectonic structures. Furthermore, the correlation between the unimpeded flow from a single well and the apertures of the tectonic fractures indicates that tectonic fractures play an important role in the production of hydrocarbons.

The mining of deep underground coal seams induces the movement, failure, and collapse of the overlying rock–soil body, and the development of this damaging effect on the surface causes ground fissures and ground subsidence on the surface. To ensure safety throughout the life cycle of the mine, fully distributed, real-time, and continuous sensing and early warning is essential. However, due to mining being a dynamic process with time and space, the overburden movement and collapse induced by mining activities often have a time lag effect. Therefore, how to find a new way to resolve the issue of the existing discontinuous monitoring technology of overburden deformation, obtain the spatiotemporal continuous information of the overlying strata above the coal seam in real time and accurately, and clarify the whole process of deformation in the compression–tensile strain transition zone of overburden has become a key breakthrough in the investigation of overburden deformation mechanism and mining subsidence. On this basis, firstly, the advantages and disadvantages of in situ observation technology of mine rock–soil body were compared and analyzed from the five levels of survey, remote sensing, testing, exploration, and monitoring, and a deformation and failure perception technology based on spatiotemporal continuity was proposed. Secondly, the evolution characteristics and deformation failure mechanism of the compression–tensile strain transition zone of overburden were summarized from three aspects: the typical mode of deformation and collapse of overlying rock–soil body, the key controlling factors of deformation and failure in the overburden compression–tensile strain transition zone, and the stability evaluation of overburden based on reliability theory. Finally, the spatiotemporal continuous perception technology of overburden deformation based on DFOS is introduced in detail, and an integrated coal seam mining overburden safety guarantee system is proposed. The results of the research can provide an important evaluation basis for the design of mining intensity, emergency decisions, and disposal of risks, and they can also give important guidance for the assessment of ground geological and ecological restoration and management caused by underground coal mining.

In this review, we explore how short-chain fatty acids (SCFAs) produced by the gut microbiome affect Parkinson's disease (PD) through their modulatory interactions with alpha-synuclein, neuroinflammation, and oxidative stress mediated by reactive oxygen and nitrogen species (ROS/RNS). In particular, SCFAs—such as acetate, propionate, and butyrate—are involved in gut–brain communication and can modulate alpha-synuclein aggregation, a hallmark of PD. The gut microbiome of patients with PD has lower levels of SCFAs than healthy individuals. Probiotics may be a potential strategy to restore SCFAs and alleviate PD symptoms, but the underlying mechanisms are not fully understood. Also in this review, we discuss how alpha-synuclein, present in the guts and brains of patients with PD, may induce neuroinflammation and oxidative stress via ROS/RNS. Alpha-synuclein is considered an early biomarker for PD and may link the gut–brain axis to the disease pathogenesis. Therefore, elucidating the role of SCFAs in the gut microbiome and their impact on alpha-synuclein-induced neuroinflammation in microglia and on ROS/RNS is crucial in PD pathogenesis and treatment.

Underground coal seam mining causes the deformation, failure, and collapse of the overlying rock mass of the coal seam. Suppose the monitoring and early warning of overburden deformation and failure during mining or the emergency response are inappropriate. Mining disasters such as roof falls, water inrush, gas outbursts, and rock bursts will often occur in the working face. To ensure the intrinsic safety of the mining face, it is of great significance to implement full real-time dynamic sensing and whole process intelligent warning of the overlying strata deformation caused by coal mining. A large number of field monitoring test results show that the deformation and failure of the overlying rock and soil mass of the coal seam are discontinuous and nonlinear. Therefore, how to break through the bottleneck of spatio-temporal discontinuous observation of overlying rock deformation and obtain real-time and accurate multi-field data of overlying rock and soil mass of the coal seam has become a critical link in the mechanism research for mining overlying rock deformation and failure. Based on the distributed fiber optic sensing technology, this paper systematically studies the deformation field, temperature field, seepage field, and geoelectric field of the overlying rock and soil mass during the coal mining process, introduces the mechanism, observation technology, research status, and development trend of each physical field in detail, and analyzes the research direction of mine engineering microseismic based on DAS technology, to provide theoretical support for the realization of “transparent” exploration of mine underground space. A safety guarantee system for coal seam overburden during mining integrating sensing, transmission, processing, early warning, and decision-making emergency is established through constructing a fiber optic sensing neural network combined with surface and downhole. The system consists of five modules: real-time sensing, reliable transmission, intelligent processing, instant early warning, and emergency decision-making, which can realize the intelligent sensing of the whole process of overburden deformation and failure during mining as well as the correlation analysis and evaluation of multi-field real-time data. The research results can also provide a reference for the monitoring and evaluation of the geological environment within the mining area and the restoration and treatment.

Abstract In the process of double-shield TBM excavation, it is difficult to directly observe and test the characteristics of the surrounding rock. In this paper, the screening test of the different type tailings in the wet state was carried out to obtain the gradation curve and curve evaluation index. Combining with the excavation parameters and the surrounding rock characteristics of the tailings, a comprehensive analysis was carried out to establish the evaluation system among with the tailings gradation characteristics, lithology characteristics and excavation parameters. The results showed that: Sparsely fissured rock: the tailings are extensional fractures, the tailings gradation curve is inverse “S” type and gentle, and the evaluation index value: (1.50 > lg(Cu) > 1.35), (1.90 > C c > 1.10). Broken surrounding rock: the curve is “L” type and steep, the content of coarse particles is much more than that of fine particles and (1.10 > lg(Cu) > 1.00), (2.60 > C c > 2.40). Fractured rock: the curve is “Step” type, the tailings particles lack the middle particle size, the minerals are mostly weathered, (2.15 > lg(Cu )> 1.95), (0.09 > C c > 0.07). The research results have good applicability to the surrounding rock stability evaluation of the example tunnel, which verifies the feasibility of the method.

A 65-year-old male complaining of low back pain was noted to have diffuse, homogenous bilateral lung uptake on Tc-99m methylene diphosphate (Tc99m-MDP) bone scintigraphy. The patient had no prior history of pulmonary disease with no apparent respiratory symptoms at time of imaging, but did endorse a long history of lupus nephritis and end-stage renal disease on hemodialysis. Review of prior chest CT and chest X-ray imaging over the last 5 years revealed diffuse ground-glass opacities and extensive parenchymal calcifications, consistent with metastatic pulmonary calcification. These radiological findings were further corroborated by laboratory studies, which demonstrated longstanding secondary hyperparathyroidism with a most recent work-up including an iPTH level of 1251 pg/mL. The differential diagnosis of bilateral, diffuse Tc99m-MDP uptake on bone scintigraphy includes tracer contamination, pulmonary etiologies such as pleural effusion or mesothelioma, metabolic diseases such as metastatic pulmonary calcification, and genetic diseases including pulmonary alveolar microlithiasis. In the setting of longstanding renal dysfunction and chronic hypercalcemia as in this patient, such radiological findings are a classic presentation of metastatic pulmonary calcification.

The optical properties of an off-axis integrated cavity system are influenced by both structural deformation and thermal deformation. In this paper, the finite element simulation and analysis software COMSOL multiphysics was used to numerically simulate the optical system. By coupling geometric optics, solid mechanics, and solid heat transfer and conducting parametric temperature scanning, a multiphysics simulation of the off-axis integrated cavity optical sensing system was achieved. The effects of different temperature conditions on the stress field, displacement field, and optical mirrors were analyzed, and changes in optical properties were assessed using ray trajectories and point diagrams. Additionally, optical simulation software was used to simulate and optimize the experimental optical path, obtaining the distribution of light spots on the detector surface. This provides a theoretical basis for the subsequent optimization of the off-axis integrated cavity optical system.

Landslide is a typical geological disaster distributed in most countries worldwide. Due to long-term natural weathering and human engineering disturbances, the instability of landslides is prone to occur. Once monitoring and disposal methods are implemented inappropriately, they can lead to landslide hazards, seriously threatening the safety of people’s lives and property. For a long time, extensive research on landslide monitoring has been conducted from various countries, providing crucial technical support for reducing the incidence and severity of landslide hazards. However, considering the complex geological conditions of actual landslides and the direct impact of internal and external factors such as rainfall, storms, and earthquakes, the early warning accuracy of landslide hazards is still relatively low. Therefore, based on advanced research achievements, it is significant to carry out research on the current status and development trends of landslide monitoring technology. Based on the Web of Science core database, this study quantitatively analyzes the advanced research achievements in global landslide monitoring in the past decade using bibliometric analysis. A systematic analysis of landslide monitoring technology development is conducted according to each study’s publication time, keywords, and countries. On this basis, a multi-dimensional monitoring system for landslides was proposed, which utilizes the complementary advantages of multi-dimensional monitoring technology to achieve all-round, high-precision, and real-time monitoring of landslides. Finally, taking the Xinpu landslide in the Three Gorges Region of China as an example, a multi-source and multi-field-monitoring experiment was conducted. The application of landslide multi-field-monitoring technology provides an essential reference for monitoring, early warning, as well as the scientific prevention and control of landslide hazard.

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.

As a new technology for harvesting distributed energy, the triboelectric nanogenerator (TENG) has been widely used in harvesting wind energy. However, the wind-driven TENG (WD-TENG) faces the problems of high frictional resistance and low mechanical energy conversion efficiency. Here, based on optimizing the structure of the wind turbine, a rotational double-electrode-layer WD-TENG (DEL-WD-TENG) is developed. When the rotational speed is less than 400 round per minute (rpm), the dielectric triboelectric layer rubs with the inner electrode layer under its gravity; when the rotational speed is higher than 400 rpm, the dielectric triboelectric layer rubs with the outer electrode layer under the centrifugal force. The double-electrode-layer structure avoids the energy loss caused by other forces except gravity, centrifugal, and electrostatic adsorption, which improves the mechanical energy conversion efficiency and prolongs the working life of the DEL-WD-TENG. The conversion efficiency from mechanical energy to electricity of the DEL-WD-TENG can reach 10.3%. After 7 million cycles, the transferred charge of the DEL-WD-TENG is reduced by about 5.0%, and the mass loss of dielectric triboelectric layer is only 5.6%. The DEL-WD-TENG with low frictional resistance and high energy conversion efficiency has important application prospects in wind energy harvesting and self-powered sensing systems.

Abstract Background NRG1 fusions are rare oncogenic drivers in solid tumors, and the incidence of NRG1 fusions in non-small cell lung cancer (NSCLC) was 0.26%. It is essential to explore potential therapeutic strategies and efficacy predictors for NRG1 fusion-positive cancers. Case presentation We report an advanced lung adenocarcinoma patient harboring a novel NPTN-NRG1 fusion identified by RNA-based next-generation sequencing (NGS), which was not detected by DNA-based NGS at initial diagnosis. Transcriptomics data of the tissue biopsy showed NRG1α isoform accounted for 30% of total NRG1 reads, and NRG1β isoform was undetectable. The patient received afatinib as fourth-line treatment and received a progression-free survival (PFS) of 14 months. Conclusions This report supports afatinib can provide potential benefit for NRG1 fusion patients, and RNA-based NGS is an accurate and cost-effective strategy for fusion detection and isoform identification.

Abstract The scarcity of wettability, insufficient active sites, and low surface area of graphite felt (GF) have long been suppressing the performance of vanadium redox flow batteries (VRFBs). Herein, an ultra‐homogeneous multiple‐dimensioned defect, including nano‐scale etching and atomic‐scale N, O co‐doping, was used to modify GF by the molten salt system. NH4Cl and KClO3 were added simultaneously to the system to obtain porous N/O co‐doped electrode (GF/ON), where KClO3 was used to ultra‐homogeneously etch, and O‐functionalize electrode, and NH4Cl was used as N dopant, respectively. GF/ON presents better electrochemical catalysis for VO2+/VO2+ and V3+/V2+ reactions than only O‐functionalized electrodes (GF/O) and GF. The enhanced electrochemical properties are attributed to an increase in active sites, surface area, and wettability, as well as the synergistic effect of N and O, which is also supported by the density functional theory calculations. Further, the cell using GF/ON shows higher discharge capacity, energy efficiency, and stability for cycling performance than the pristine cell at 140 mA cm−2 for 200 cycles. Moreover, the energy efficiency of the modified cell is increased by 9.7% from 55.2% for the pristine cell at 260 mA cm−2. Such an ultra‐homogeneous etching with N and O co‐doping through “boiling” molten salt medium provides an effective and practical application potential way to prepare superior electrodes for VRFB.

Type-2 cannabinoid receptor (CB2R) is a member of the G protein-coupled receptor superfamily with various biological activities in pain regulation, anti-inflammation, anti-fibrosis, and bone metabolism regulation. CB2R modulators have been demonstrated to have significant regulatory effects on bone metabolism with no significant psychoactive adverse effects. In this article, we present a comprehensive review of the molecular mechanism of CB2R in regulating bone metabolism and summarize virtual screening and experimental screening methods for CB2R modulators from natural and synthetic products. Knowing that some botanical compounds are CB2R modulators involved in the regulation of bone metabolism, we also review the botanical and synthetic compounds currently known to be potential CB2R and summarize their regulatory effects on bone metabolism based on the results of animal, osteoblasts (OBs) and osteoclasts (OCs) experimental studies presently available, hoping that the findings and conclusions in this review could provide a scientific basis for the discovery of new CB2R regulators and the development of new anti-osteoporosis drugs.

Abstract N-acetylcysteine (NAC) has been used as an antioxidant drug in tumor cells and preclinical mice tumor xenografts, and it improves adaptive immunotherapy in melanoma. NAC is not readily bioavailable and is used in high concentrations. The effects of NAC have been attributed to its antioxidant and redox signaling role in mitochondria. New thiol-containing molecules targeted to mitochondria are needed. Here, mitochondria-targeted NAC with a 10-carbon alkyl side chain attached to a triphenylphosphonium group (Mito10-NAC) that is functionally similar to NAC was synthesized and studied. Mito10-NAC has a free sulfhydryl group and is more hydrophobic than NAC. Mito10-NAC is nearly 2000-fold more effective than NAC in inhibiting several cancer cells, including pancreatic cancer cells. Methylation of NAC and Mito10-NAC also inhibited cancer cell proliferation. Mito10-NAC inhibits mitochondrial complex I-induced respiration and, in combination with monocarboxylate transporter 1 inhibitor, synergistically decreased pancreatic cancer cell proliferation. Results suggest that the antiproliferative effects of NAC and Mito10-NAC are unlikely to be related to their antioxidant mechanism (i.e., scavenging of reactive oxygen species) or to the sulfhydryl group-dependent redox modulatory effects.

Pr-doped metal oxide polycrystalline transparent ceramics are highly desirable for photothermal window systems served in extreme environments; however, obtaining efficient photoluminescence (PL) together with high transparency in these ceramics is still posing serious challenges, which undoubtedly limits their applications. Here, Pr-doped Y2Zr2O7 (YZO) transparent ceramics, as an illustrative example, are prepared by a solid-state reaction and vacuum sintering method. Owing to the elimination of defect clusters [PrY4+−O2−−PrY4+] and [PrY4+−e′] without the introduction of impurities and additional defects, the fabricated YZO:Pr ceramics exhibit high transparency (74%) and efficient PL (39-fold enhanced) after air annealing plus vacuum re-annealing treatment. Moreover, upon 295/450 nm excitation, the emission bands (blue, green, red, and dark red) from YZO:Pr ceramics present different temperature-dependent properties due to the thermal-quenching channel generated by the intervalence charge transfer (IVCT) state between Pr3+ and Zr4+ ions. Furthermore, a self-calibrated temperature feedback window with the same fluorescence intensity ratio (FIR) model (I613/I503, where I represents the intensity) under different excitation light sources (295 and 450 nm) is designed. The developed photothermal window operated in a wide temperature range (303–663 K) shows relatively high sensitivities (absolute sensitivity (Sa) and relative sensitivity (Sr) reach 0.008 K−1 at 663 K and 0.47% K−1 at 363 K, respectively), high repeatability (> 98%), and low temperature uncertainty (δT < 3.2 K). This work presents a paradigm for achieving enhanced PL along with elevated transparency of lanthanide (Ln)-doped ceramics through vacuum re-annealing treatment engineering and demonstrates their promising potential for photothermal window systems.

As a kind of clean energy which creates little carbon dioxide, natural gas will play a key role in the process of achieving “Peak Carbon Dioxide Emission” and “Carbon Neutrality”. The Long-range Energy Alternatives Planning System (LEAP) model was improved by using new parameters including comprehensive energy efficiency and terminal effective energy consumption. The Back Propagation (BP) Neural Network—LEAP model was proposed to predict key data such as total primary energy consumption, energy mix, carbon emissions from energy consumption, and natural gas consumption in China. Moreover, natural gas production in China was forecasted by the production composition method. Finally, based on the forecast results of natural gas supply and demand, suggestions were put forward on the development of China's natural gas industry under the background of “Dual Carbon Targets”. The research results indicate that under the background of carbon peak and carbon neutrality, China's primary energy consumption will peak (59.4×108 tce) around 2035, carbon emissions from energy consumption will peak (103.4×108 t) by 2025, and natural gas consumption will peak (6100×108 m3) around 2040, of which the largest increase will be contributed by the power sector and industrial sector. China's peak natural gas production is about (2800–3400)×108 m3, including (2100–2300)×108 m3 conventional gas (including tight gas), (600–1050)×108 m3 shale gas, and (150–220)×108 m3 coalbed methane. Under the background of carbon peak and carbon neutrality, the natural gas consumption and production of China will further increase, showing a great potential of the natural gas industry.

The filling efficiency during the hot embossing process at micro scale is essential for micro-component replication. The presence of the unfilled area is often due to the inadequate behavior law applied to the embossed materials. This research consists of the identification of viscoplastic law (two-layer viscoplastic model) of polymers and the optimization of processing parameters. Mechanical tests have been performed for two polymers at 20 °C and 30 °C above their glass transition temperature. The viscoplastic parameters are characterized based on stress–strain curves from the compression tests. The influences of imposed displacement, temperature, and friction on mold filling are investigated. The processing parameters are optimized to achieving the complete filling of micro cavities. The replication of a micro-structured cavity has been effectuated using this process and the experimental observations validate the results in the simulation, which confirms the efficiency of the proposed numerical approach.

Objective: The aims of this study were to evaluate the correlation between chemokine (C-X-C) ligand 7 (CXCL7) expression and glycolysis and to explore the prognostic significance of CXCL7 in colorectal cancer (CRC). Methods: The expression of CXCL7 and lactate dehydrogenase A (LDH-A) was measured by immunohistochemistry in tissue from 158 CRC patients. Patients were divided into high expression and low expression groups based on receiver operating characteristic curves and a cut-off value. The correlation between CXCL7 and LDH-A expression was evaluated. The overall survival (OS) times of CRC patients were explored. The risk factors related to prognosis were assessed. Results: Significantly higher expression of CXCL7 and LDH-A was detected in CRC tissue than in non-CRC tissue, and was associated with N stage and tumor-node-metastasis (TNM) stage. CXCL7 expression was strongly correlated with LDH-A expression in CRC tissue. High expression of CXCL7 was validated as an independent risk factor for OS. Conclusion: Increased expression of CXCL7 was positively correlated with LDH-A expression and was an independent risk factor for CRC prognosis.

Abstract The research presented in this paper introduces a novel environmental energy‐harvesting technology that harnesses electricity from the evaporation of water using porous structural materials. Specifically, a strategy employing paper‐based hydroelectric generators (p‐HEGs) is proposed to capture the energy produced during water evaporation and convert it into usable electricity. The p‐HEGs offer several advantages, including simplicity in fabrication, low cost, and reusability. To evaluate their effectiveness, the water evaporation‐induced electrical output performance of four different p‐HEGs are compared. Among the variants tested, the p‐HEG combining wood pulp and polyester fiber exhibits the best output performance. At room temperature, this particular p‐HEG generates a short‐circuit current and open‐circuit voltage of ≈0.4 µA and 0.3 V, respectively, thereby demonstrating excellent electrical stability. Furthermore, the electrical current and voltage generated by the p‐HEG through water evaporation are able to power an LED light, both individually and in series and parallel connections. This study delves into the potential of electricity harvesting from water evaporation and establishes it as a viable method for renewable energy applications.

Ulcerative colitis (UC) has been confirmed as a disease with a high incidence and low cure rate worldwide. In severe cases, UC can develop into colon cancer. Modern research has confirmed that berberine (BBR) can treat UC by inhibiting the expressions of inflammatory factors. However, the contribution of gut microbiota and flora metabolites in treating UC with BBR remains unclear. In this study, the ameliorative effects of BBR on gut microbiota dysbiosis and flora metabolites were investigated in a dextran sodium sulfate (DSS)-induced UC rodent model. We found that BBR significantly improved the pathological phenotype, attenuated intestinal barrier disruption, and mitigated colonic inflammation in DSS mice. By 16 S rDNA sequencing, BBR alleviated gut microbiota dysbiosis in UC mice. Moreover, the gut microbiota depletion experiment confirmed that the therapeutic effect of BBR was inextricably correlated with the gut microbiota. Besides, the flora metabolites (e.g., short-chain fatty acids, bile acids, and 5-hydroxytryptamine) were studied using HPLC-MS. The results suggested that BBR ameliorated the bile acid imbalance induced by DSS in the liver and gut. Furthermore, BBR treatment repaired gut barrier damage. The above results revealed that BBR alleviated DSS-induced UC in mice by restoring the disturbed gut microbiota, elevating unconjugated and secondary bile acids in the gastrointestinal tract, and activating the FXR and TGR5 signal pathway. This study provides novel insights into the mechanism of BBR in treating UC.

In this paper, an optimal sliding mode control method for trajectory tracking of discrete-time systems based on linear quadratic regulator is proposed to improve the trajectory tracking control accuracy and robustness of upper limb rehabilitation robot under the condition of highly nonlinearities, external disturbances, and unmodeled dynamics. Firstly, considering the uncertainty of the mass and moment of inertia of the connecting rod arm of the upper limb rehabilitation robot and the uncertainty of external interference, the dynamic model of the upper limb rehabilitation robot is established by using the Euler Lagrange method, and the linear time-varying state equation of the rehabilitation robot system under the influence of both nonlinear and uncertain factors is derived. Secondly, directing at the chattering problem in sliding mode control, a sliding mode control method based on a new discrete time reaching law is designed to reduce the amplitude of chattering in the control input signal of the upper limb rehabilitation robot system and improve the tracking speed. Furthermore, combined with linear quadratic optimal control, the optimal discrete integral sliding mode control law (LQRSMC) is finally obtained. Meanwhile, for the sake of reducing the influence of the uncertain signal on the system, a robust control law is adopted to estimate and compensate the uncertain interference. The stability of the upper limb rehabilitation robot system is verified by the sliding mode approach condition of the discrete system. Finally, the genetic algorithm is used to further optimize the weighted value, and MATLAB/Simulink is used to simulate the state trajectory of the upper limb rehabilitation robot under various weighted values. The control strategy can not only effectively weaken the trajectory tracking oscillation problem of the upper limb rehabilitation robot, but also overcome the external disturbance and modeling uncertainty, while ensuring the robustness of the rehabilitation robot system.

As a new type of fiber optic sensing technology for long-distance, distributed and real-time monitoring, distributed acoustic sensing (DAS) has been a wide concern since its initiation. Compared with the traditional seismic wave sensing system (node array, geophone, seismometer), DAS has the advantages of high spatial sampling density, wide measurement range, and strong environmental adaptability (anti-electromagnetic interference, corrosion resistance, waterproof).Recently, researchers have carried out a large number of exploratory experiments around DAS technology and made important progress in the field of earth exploration.This paper reviews the latest research on the application of DAS technology for earth exploration. Starting from theworking concept of DAS, the basic concept of DAS is elaborated, and the performance of DAS and seismometer is fully compared.And we introduce the applications of DAS in oil and gas exploration, seismic observation, and structural imagingby reviewing several representative experiments. Finally, this paper summarizes the current bottleneck of the earth exploration technological system based on DAS and analyzes the development trends in the future.

Abstract Squamous cell carcinomas are the most common head and neck malignancies. Significant progress has been made in standard therapeutic methods combining surgery, radiation, and chemotherapy. Nevertheless, the 5-year survival rate remains at 40–50%. Immune checkpoint inhibitors (ICIs) are a new strategy for treating head and neck squamous cell carcinomas (HNSCCs). Still, the overall response and effective rates are poor, as HNSCCs are ‘cold’ tumors with an immunosuppressive tumor microenvironment (TME), limiting ICI’s beneficial effects. In this case, transforming the tumor suppression microenvironment before using ICIs could be helpful. Oncolytic viruses (OVs) can transform cold tumors into hot tumors, improving the situation. Talimogene laherparepvec (T-VEC), oncolytic immunotherapy authorized for advanced melanoma, also showed good safety and antitumor activity in treating head and neck cancer and pancreatic cancer. In combination with pembrolizumab, T-Vec may have more anticancer efficacy than either drug alone. Therefore, understanding the mechanisms underpinning OVs and their potential synergism with ICIs could benefit patients with HNSCC.

In order to solve the problem that the channel state information (CSI) feedback algorithm based on deep learning in massive MIMO systems at present had too many parameters to be trained and could not be explained well, two CSI feedback algorithms based on depth expansion were proposed.The first one was approximate message delivery (AMP) algorithm based on learnable parameters.The learnable parameters in deep learning were used to replace the threshold value of the threshold function in the AMP algorithm and the parameters of the Onsage correction term.The nonlinear ability of threshold function in dealing with non-strict sparse data was enhanced.The other was the AMP algorithm based on convolutional network, which replaced the threshold function module with the convolutional residual learning module, and used the module to remove the Gaussian random noise generated by each iteration of the AMP algorithm.Simulation results show that the proposed two algorithms have better CSI feedback performance than AMP algorithm, and the AMP algorithm based on convolutional network has better CSI reconstruction performance than the representative method based on deep learning.

Understanding the spatiotemporal evolution of overburden deformation during coal mining is still a challenge in engineering practice due to the limitation of monitoring techniques. Taking the Yangliu Coal Mine as an example, a similarity model test was designed and conducted to investigate the deformation and failure mechanism of overlying rocks in this study. Distributed fiber optic sensing (DFOS), high-density electrical resistivity tomography (HD-ERT) and close-range photogrammetry (CRP) technologies were used in the test for comprehensive analyses. The combined use of the three methods facilitates the investigation of the spatiotemporal evolution characteristics of overburden deformation, showing that the mining-induced deformation of overburden strata was a dynamic evolution process. This process was accompanied by the formation, propagation, closure and redevelopment of separation cracks. Moreover, the key rock stratum with high strength and high-quality lithology played a crucial role in the whole process of overburden deformation. There were generally three failure modes of overburden rock layers, including bending and tension, overall shearing, and shearing and sliding. Shear failure often leads to overburden falling off in blocks, which poses a serious threat to mining safety. Therefore, real-time and accurate monitoring of overburden deformation is of great significance for the safe mining of underground coal seams.

Abstract Introduction Previous studies have inconsistent associations between changes in sleep duration and cognitive function and have not separated interindividual effects from intraindividual effects. This study aimed to examine the bidirectional associations between subjective night sleep duration and cognitive function while differing intraindividual and interindividual effects. Methods A national representative sample was obtained from China Health and Retirement Longitudinal Study during 2011–2018. Night sleep duration and potential confounders were assessed and collected by questionnaires. Cognition was assessed in three categories (orientation, executive function, and memory), and z scores were used for global cognitive performance. The random intercept cross-lagged panel model was used to examine the temporal associations during 2011–2018. Results Across 9404 participants over 45, at interindividual level, moderate (β= -0.014) and long sleepers (β= -0.06) had positive association between sleep duration and cognitive decline after adjusted covariates, but short sleepers had negative associations between sleep duration and cognitive decline (β = 0.87). At intraindividual level, prolonged sleep duration predicted better cognition for short (β= -0.021 at wave2-3; β= -0.04 at wave3-4) and moderate (β= -0.017 at wave3-4) sleepers. Conclusion For short sleepers, longer subjective nocturnal sleep duration predicted better cognitive performance; but moderate and long sleepers showed opposite results—short and moderate sleepers with prolonged subjective nocturnal sleep duration would have later cognitive decline. Our findings tentatively suggested that an increased subjective night sleep duration and subjective long sleep duration could be regarded as useful tools for identifying middle- and old adults at higher risks of progressing to cognitive decline.

Preferential public transport policies provide an important social welfare support for travel by the elderly. However, the travel problems faced by the elderly, such as traffic congestion during peak hours, have not attracted enough attention from transportation-related departments. This study proposes a passenger flow prediction model for the elderly taking public transport and validates it using bus smart card data. The study incorporates short time series clustering (STSC) to integrate the elements of the heterogeneity of bus trips taken by the elderly, and accurately identifies the needs of elderly passengers by analysing passenger flow spatiotemporal characteristics. According to the needs and characteristics of passenger flow, a short time series clustering Seasonal Autoregressive Integrated Moving Average (STSC-SARIMA) model was constructed to predict passenger flow. The analysis of spatiotemporal travel characteristics identified three peak periods for the elderly to travel every day. The number of people traveling in the morning peak was significantly larger compared to other periods. At the same time, compared with bus lines running through central urban areas, multi-community, and densely populated areas, the passenger flow of bus lines in other areas dropped significantly. The study model was applied to Lhasa, China. The prediction results verify that the model has high prediction accuracy and applicability. In addition to the initial application, this predictive model provides new directions for bus passenger flow forecasting to support better public transport policy-making and improve elderly mobility.

The bacterial diversity in Populus euphratica stem storage liquid samples grown in Shaya County and Yuli County of the Tarim River Basin was investigated. A culture-dependent (dilution spread plate method) and culture-independent method (PCR-RFLP technique) were used to identify the endophytic bacteria community structure and composition in P. euphratica in Tarim River Basin. Sixty-six bacterial strains were isolated from P. euphratica stem storage liquid samples on three agar media. The 16S rDNA gene was amplified and sequenced using bacterial universal primers. Phylogenetic analysis showed that the 66 strains belonged to three phyla (Firmicutes, Actinomycetes, and Gamma-Proteobacteria) and included 16 genera and 29 species. Among them, Pseudomonas (27.27%) and Bacillus (19.69%) were the dominant isolates. CGM-17 was a potentially new species of Pantoea. Restriction fragment length polymorphism of 16S rDNA gene amplified by polymerase chain reaction (PCR-RFLP) revealed 48 operational taxonomic units (OTUs). Phylogenetic analysis indicated that the 48 OTUs belonged to Firmicutes, Actinobacteria, Proteobacteria (α-, β-, γ-subgroup), Bacteroidetes, and Verrucomicrobia. Gamma-Proteobacteria was the dominant group, similarly to the culture-dependent method, accounting for 53% of the entire bacterial clone library. Our results indicate that P. euphratica endophytic bacteria diversity in the Tarim River Basin was rich, and the resources of endophytic bacteria were high. They provide valuable reference data and species resources for screening indigenous and functional strains of endophytic bacteria in P. euphratica.

Fine-scale population map plays an essential role in numerous fields, including resource allocation, urban planning, disaster prevention and response. Point of Interest (POI) data is widely used for population spatialization, but the types of POI are ignored. Since different types of POI data have different impacts on population distribution, this paper used typed POI data and other multi-source data to map population distributions at fine scales. At the township level, three random forest models were used to generate the population maps of 150 m, 300 m, and 500 m in 2020, enabling the downscaling of county-level population distribution to the grid level. The main influencing factors of population distribution were extracted and analyzed based on the feature importance output from the model. Zhengzhou city was used as a case for study. The experiments show the results of population spatialization for all three scales in this study have better fitting accuracy than that of the GPWv4 and LandScan datasets. The coefficient of determination (R2) is 0.8333 for 150 m gridded population, 0.8295 for 300 m, and 0.8224 for 500 m; POI types related to residence information have greater contributions to population spatialization than other features; typed POI data are more conducive to population spatialization.

BackgroundUntreated maternal postpartum depression (PPD) has consequences for children's physical growth, but no published study has evaluated changes in this effect over time. Here we therefore aimed to evaluate the dynamic effects of PPD on the physical growth of children in a prospective birth cohort.MethodsBetween 2015 and 2019, 960 mother-child pairs in Changsha, China were followed up when the child was aged 1–48 months. Data were obtained through household surveys. The mothers' depressive symptoms were measured using the Edinburgh Postpartum Depression Scale (EPDS) at 1 month postpartum. Linear mixed models were used to examine the changes in the association of PPD and EPDS scores with physical growth in six different age groups of children between 1 and 48 months.ResultsA total of 604 mother-child pairs completed the follow-up, and 3.3% of mothers reported PPD. No associations were found between PPD and weight or height growth at any age. While EPDS scores were associated with weight gain (β = −0.014, 95% CI (−0.025, −0.002), P = 0.024) and height growth (β = −0.044, 95% CI (−0.084, −0.004), P = 0.030) rates at 1–3 months, no associations were found in older children.LimitationsThe number of mothers who reported PPD was relatively small, and the measurement of PPD was not continuously taken.ConclusionsAfter adjustments for confounders, no dynamic association was found between PPD and children's weight and height growth. EPDS scores, in contrast, did negatively affect children's weight and height growth at age 1–3 months, but this effect was not long-lasting.