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Abstract Ion selective membranes are fundamental components of biological, energy, and computing systems. The fabrication of solid-state ultrathin membranes that can separate ions of similar size and the same charge with both high selectivity and permeance remains a challenge, however. Here, we present a method, utilizing the application of a remote electric field, to fabricate a high-density of (sub)nm pores in situ. This method takes advantage of the grain boundaries in few-layer polycrystalline MoS2 to enable the synthesis of nanoporous membranes with average pore size tunable from

Abstract Objective This study aims to investigate the anatomical structure of the C6 pedicle and lateral mass in children aged 0–14 years using CT imaging, providing detailed insights into their growth and development. Methods We conducted a comprehensive measurement of C6. Measurements included width, length, and height of the pedicles, as well as the length, width, and thickness of the lateral masses, and several angular metrics. Regression analysis was performed to understand the growth trends, and statistical analyses were carried out to identify differences between age groups, genders, and sides. Results In children younger than four years, the pedicle width exceeds its height, influencing the diameter of the pedicle screws. By age two to three, the pedicle height and lateral mass thickness reaches 3.0 mm, allowing for the use of 3.0 mm diameter screws. The pedicle transverse angle remains stable. Most parameters showed no significant differences between the left and right sides. Size parameters exhibited significant larger in males than females at ages 0–1, 3–7, and 10–12 years. Regression analysis revealed that the growth trends of size parameters follow cubic or polynomial curves. Most angular metrics follow cubic fitting curves without a clear trend of change with age. Conclusion This study provides a detailed analysis of the anatomical development of the C6 pedicle and lateral masses in children, offering valuable insights for pediatric cervical spine surgeries. The findings highlight the importance of considering age-specific anatomical variations when planning posterior surgical fixation, specifically at C6. It is necessary for us to perform thin-layer CT scans on children and carefully measure various indicators before surgery.

Rare-earth elements (REEs) are critical to modern industry but difficult to separate due to their subtle and monotonic changes in physicochemical properties. MoS2-based two-dimensional (2D) materials offer novel opportunities for enhancing REE separation, exhibiting a distinct volcano-shaped transport performance distribution that peaks at Sm3+. However, the specific contributions of thermodynamic and kinetic factors to ion transport within 2D confinement remain unclear. In this study, we conducted a series of non-equilibrium all-atom molecular dynamics (MD) simulations to explore the effects of interlayer spacing and external pressure on the transport of lanthanide ions in Å-scale acetate functionalized 2D MoS2 (MoS2-COOH) channels. We examined ion entry and permeation rates, water flux, dehydration, and binding modes. The simulation results reveal that the transport trends of lanthanide ions are jointly driven by the dehydration degree and the relative-binding strengths of ions to water and to the acetate within the 2D channels. Notably, the dehydration pattern of lanthanide ions during permeation is closely linked to kinetic factors. Overall, this study provides a detailed atomistic understanding of the mechanisms underlying lanthanide ion transport under confinement. These findings point to the significant potential for tuning confinement and chemical functionalization within Å-scale channels for more efficient REE separation.

Abstract Background The study aimed to evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in distinguishing sinonasal lymphoma from sinonasal carcinoma. Methods Forty-two participants with histologically confirmed sinonasal lymphomas and fifty-two cases of sinonasal carcinoma underwent imaging with a 3.0T MRI scanner. DCE-MRI and DWI were conducted, and various parameters including type of time-intensity curve(TIC), time to peak, peak enhancement, peak contrast enhancement, washout rate, apparent diffusion coefficient (ADC), and relative ADC were measured. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were employed to assess the diagnostic capability of individual and combined indices for differentiating nasal sinus lymphoma from nasal sinus carcinoma. Results Sinonasal lymphoma predominantly exhibited type II TIC(n = 20), whereas sinonasal carcinoma predominantly exhibited type III TIC(n = 23). Significant differences were observed in all parameters except washout ratio (p

ABSTRACT: The health benefits conferred by probiotics is specific to individual probiotic strains, highlighting the importance of identifying specific strains for research and production purposes. Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047 are exceedingly valuable for commercial use with an excellent mixed-culture fermentation. To differentiate these 2 strains from other S. thermophilus and L. delbrueckii ssp. bulgaricus, a specific, sensitive, accurate, rapid, convenient, and cost-effective method is required. In this study, we conducted a pan-genome analysis of S. thermophilus and L. delbrueckii ssp. bulgaricus to identify species-specific core genes, along with strain-specific SNPs. These genes were used to develop suitable PCR primers, and the conformity of sequence length and unique SNPs was confirmed by sequencing for qualitative identification at the strain level. The results demonstrated that SNPs analysis of PCR products derived from these primers could distinguish CICC 6038 and CICC 6047 accurately and reproducibly from the other strains of S. thermophilus and L. delbrueckii ssp. bulgaricus, respectively. The strain-specific PCR method based on SNPs herein is universally applicable for probiotics identification. It offers valuable insights into identifying probiotics at the strain level that is fit-for-purpose in quality control and compliance assessment of commercial dairy products.

Abstract Background Plants can retain atmospheric particulate matter (PM) through their unique foliar microstructures, which has a profound impact on the phyllosphere microbial communities. Yet, the underlying mechanisms linking atmospheric particulate matter (PM) retention by foliar microstructures to variations in the phyllosphere microbial communities remain a mystery. In this study, we conducted a field experiment with ten Ulmus lines. A series of analytical techniques, including scanning electron microscopy, atomic force microscopy, and high-throughput amplicon sequencing, were applied to examine the relationship between foliar surface microstructures, PM retention, and phyllosphere microbial diversity of Ulmus L. Results We characterized the leaf microstructures across the ten Ulmus lines. Chun exhibited a highly undulated abaxial surface and dense stomatal distribution. Langya and Xingshan possessed dense abaxial trichomes, while Lieye, Zuiweng, and Daguo had sparsely distributed, short abaxial trichomes. Duomai, Qingyun, and Lang were characterized by sparse stomata and flat abaxial surfaces, whereas Jinye had sparsely distributed but extensive stomata. The mean leaf retention values for total suspended particulate (TSP), PM2.5, PM2.5-10, PM10-100, and PM> 100 were 135.76, 6.60, 20.10, 90.98, and 13.08 µg·cm− 2, respectively. Trichomes substantially contributed to PM2.5 retention, while larger undulations enhanced PM2.5-10 retention, as evidenced by positive correlations between PM2.5 and abaxial trichome density and between PM2.5-10 and the adaxial raw microroughness values. Phyllosphere microbial diversity patterns varied among lines, with bacteria dominated by Sediminibacterium and fungi by Mycosphaerella, Alternaria, and Cladosporium. Redundancy analysis confirmed that dense leaf trichomes facilitated the capture of PM2.5-associated fungi, while bacteria were less impacted by PM and struggled to adhere to leaf microstructures. Long and dense trichomes provided ideal microhabitats for retaining PM-borne microbes, as evidenced by positive feedback loops between PM2.5, trichome characteristics, and the relative abundances of microorganisms like Trichoderma and Aspergillus. Conclusions Based on our findings, a three-factor network profile was constructed, which provides a foundation for further exploration into how different plants retain PM through foliar microstructures, thereby impacting phyllosphere microbial communities.

Abstract Objective Immune checkpoints have emerged as promising therapeutic targets for autoimmune diseases. However, the specific roles of immune checkpoints in the pathophysiology of ankylosing spondylitis (AS) remain unclear. Methods Hip ligament samples were obtained from two patient groups: those with AS and femoral head deformity, and those with femoral head necrosis but without AS, undergoing hip arthroplasty. Label-Free Quantification (LFQ) Protein Park Analysis was used to identify the protein composition of the ligaments. Peripheral blood samples of 104 AS patients from public database were used to validate the expression of key proteins. KEGG, GO, and GSVA were employed to explore potential pathways regulated by immune checkpoints in AS progression. xCell was used to calculate cell infiltration levels, LASSO regression was applied to select key cells, and the correlation between immune checkpoints and immune cells was analyzed. Drug sensitivity analysis was conducted to identify potential therapeutic drugs targeting immune checkpoints in AS. The expression of key genes was validated through immunohistochemistry (IHC). Results HLA-DMB and HLA-DPA1 were downregulated in the ligaments of AS and this has been validated through peripheral blood datasets and IHC. Significant differences in expression were observed in CD8 + Tcm, CD8 + T cells, CD8 + Tem, osteoblasts, Th1 cells, and CD8 + naive T cells in AS. The infiltration levels of CD8 + Tcm and CD8 + naive T cells were significantly positively correlated with the expression levels of HLA-DMB and HLA-DPA1. Immune cell selection using LASSO regression showed good predictive ability for AS, with AUC values of 0.98, 0.81, and 0.75 for the three prediction models, respectively. Furthermore, this study found that HLA-DMB and HLA-DPA1 are involved in Th17 cell differentiation, and both Th17 cell differentiation and the NF-kappa B signaling pathway are activated in the AS group. Drug sensitivity analysis showed that AS patients are more sensitive to drugs such as doramapimod and GSK269962A. Conclusion Immune checkpoints and immune cells could serve as avenues for exploring diagnostic and therapeutic strategies for AS.

Abstract One-dimensional (1D) olivine iron phosphate (FePO4) is widely proposed for electrochemical lithium (Li) extraction from dilute water sources, however, significant variations in Li selectivity were observed for particles with different physical attributes. Understanding how particle features influence Li and sodium (Na) co-intercalation is crucial for system design and enhancing Li selectivity. Here, we investigate a series of FePO4 particles with various features and revealed the importance of harnessing kinetic and chemo-mechanical barrier difference between lithiation and sodiation to promote selectivity. The thermodynamic preference of FePO4 provides baseline of selectivity while the particle features are critical to induce different kinetic pathways and barriers, resulting in different Li to Na selectivity from 6.2 × 102 to 2.3 × 104. Importantly, we categorize the FePO4 particles into two groups based on their distinctly paired phase evolutions upon lithiation and sodiation, and generate quantitative correlation maps among Li preference, morphological features, and electrochemical properties. By selecting FePO4 particles with specific features, we demonstrate fast (636 mA/g) Li extraction from a high Li source (1: 100 Li to Na) with (96.6 ± 0.2)% purity, and high selectivity (2.3 × 104) from a low Li source (1: 1000 Li to Na) with (95.8 ± 0.3)% purity in a single step.

Abstract Background Deep brain stimulation (DBS) is a promising therapy for refractory Gilles de la Tourette syndrome (GTS). However, its long-term efficacy, safety, and recommended surgical age remain controversial, requiring evidence to compare different age categories. Methods This retrospective cohort study recruited 102 GTS patients who underwent DBS between October 2006 and April 2022 at two national centers. Patients were divided into two age categories: children (aged 0.05), and the children group received significantly higher improvement in GTS-QOL scores than adults (55.9% vs. 47.9%, p = 0.049). Conclusions DBS showed acceptable long-term efficacy and safety for both children and adults with GTS. Surgeries performed for patients younger than 18 years seemed to show acceptable long-term efficacy and safety and were not associated with increased risks of loss of benefit compared to patients older than 18 at the time of surgery. However, surgeries for children should also be performed cautiously to ensure their refractoriness and safety.

Abstract Machine learning algorithms investigate relationships in data to deliver useful outputs. However, past models required complete datasets as a prerequisite. In this study, rough set-based machine learning was applied using real-world incomplete datasets to generate a prediction model of biochar’s adsorption capacity based on key attributes. The predictive model consists of if–then rules classifying properties by fulfilling certain conditions. The rules generated from both complete and incomplete datasets exhibit high certainty and coverage, along with scientific coherence. Based on the complete dataset model, optimal pyrolysis conditions, biomass characteristics and adsorption conditions were identified to maximize tetracycline adsorption capacity (> 200 mg/g) by biochar. This study demonstrates the capabilities of rough set-based machine learning using incomplete practical real-world data without compromising key features. The approach can generate valid predictive models even with missing values in datasets. Overall, the preliminary results show promise for applying rough set machine learning to real-world, incomplete data for generating biomass and biochar predictive models. However, further refinement and testing are warranted before practical implementation.

Highlights Developing a universal strategy of the p–n homojunction engineering that could significantly boost electron mobility of electron transport layer (ETL) by two orders of magnitude. Proposing a new mechanism based on p–n homojunction to explain inhibited carrier loss at buried interface. Setting a new performance benchmark as high as 25.50% for planar perovskite solar cells employing TiO2 as ETLs.

Climate warming rates in the Arctic are far greater than the global average, exerting stronger impacts on permafrost degradation and thermokarst landform development. Thermokarst lakes and ponds (TLPs), which are widely distributed in the Lena Basin in the Russian Arctic, play a vital role in altering local ecosystem. However, the detailed distribution of TLPs in the Lena Basin still remains poorly known. In this study, we built the first 10 m resolution TLP dataset for the Lena Basin in the 2020 thawing season by utilizing 4902 Sentinel-2 images. A robust mapping workflow was developed and implemented in the Google Earth Engine (GEE) platform. The accuracy assessment demonstrates a satisfactory accuracy (93.63%), and our results exhibit a better consistency with real TLPs than global water body products. A total of 380,477 TLPs (~0.53% of the total surface area of the Lena Basin) were identified, showing an uneven distribution in the five sub-basins. The TLPs were found to be mainly located within plain areas, with an active layer thickness in the range of 80–100 cm. The higher ground ice content and mean annual ground temperature were favorable for TLP development. This dataset will be valuable for investigating the complex interaction between TLPs and permafrost. It will also serve as a baseline product for better incorporating thermokarst processes into permafrost-climate models.

Abstract Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Abstract Brainbow is a genetic cell-labeling technique that allows random colorization of multiple cells and real-time visualization of cell fate within a tissue, providing valuable insights into understanding complex biological processes. However, fluorescent proteins (FPs) in Brainbow have distinct excitation spectra with peak difference greater than 35 nm, which requires sequential imaging under multiple excitations and thus leads to long acquisition times. In addition, they are not easily used together with other fluorophores due to severe spectral bleed-through. Here, we report the development of a single-wavelength excitable Brainbow, UFObow, incorporating three newly developed blue-excitable FPs. We have demonstrated that UFObow enables not only tracking the growth dynamics of tumor cells in vivo but also mapping spatial distribution of immune cells within a sub-cubic centimeter tissue, revealing cell heterogeneity. This provides a powerful means to explore complex biology in a simultaneous imaging manner at a single-cell resolution in organs or in vivo.

Tree productivity is not only determined by stand structure, but also influenced by soil chemical properties, climate and topography. However, the relative importance of each indicator on larch (Larix principis-rupprechtii) productivity were uncertain. In this study, 76 pure larch forest plots were selected based on national forest inventory (NFI) data in Hebei Province, China. Structural equation model (SEM) was used to analyze the direct and indirect effects of stand structure, soil chemical properties, climate and topography on larch productivity, and to quantify the relative importance of each indicator in determining productivity. The results showed that stem volume growth (SVG) of larch was influenced by a combination of stand density, diameter at breast height (DBH), mean winter snow (PAS), annual temperature range (TD), slope, and alkali-hydrolysis nitrogen (AN). SVG tended to increase with decreasing stand density and AN content and increasing DBH. Stand density, DBH and AN were more important than PAS, TD, and slope in explaining SVG variation. The results can provide a scientific basis for adaptive management of larch forests.

The manifestation of a giant ovarian yolk sac tumor during late pregnancy is relatively rare. A yolk sac tumor is a highly malignant germ cell tumor that originates from primitive germ cells. It is characterized by yolk sac differentiation in vitro. The frequency of prenatal examinations should be appropriately increased for ovarian tumors discovered during pregnancy. Furthermore, regular follow-up ultrasound should be performed, and tumor markers should be dynamically detected. If needed, imaging examinations such as computed tomography and magnetic resonance imaging should be combined to comprehensively investigate disease progression. If the tumor diameter and tumor marker levels rapidly increase during pregnancy, the possibility of malignancy increases. Therefore, exploratory laparotomy should be immediately performed to further improve subsequent treatment modalities, early diagnosis, early treatment, and prognosis. Herein, we report the case of a 28-year-old pregnant woman whose pregnancy was terminated at 29 weeks and 5 days. She complained of lower abdominal pain for 2 days. A pelvic mass was detected for 1 week, accompanied by increased levels of tumor markers such as serum alpha-fetoprotein, cancer antigen 125, carbohydrate antigen 724, and human epididymis protein 4. Imaging revealed the presence of a pelvic mass. At 32 weeks and 3 days of pregnancy, a cesarean section was performed, with a transverse incision in the lower uterine segment. Furthermore, pelvic adhesiolysis, omentectomy, right adnexectomy, right pelvic lymph node dissection, and pelvic metastasis peritonectomy were performed. The postoperative pathological diagnosis was yolk sac tumors of the ovary (stage IIB). Postoperatively, a five-cycle chemotherapy regimen comprising bleomycin, etoposide, and cisplatin was administered. During postoperative follow-up, the patient’s general condition was noted to be good, with the newborn and pregnant women ultimately achieving good outcomes. We reviewed the relevant literature to increase clinical doctors’ understanding of ovarian malignancy during pregnancy, guide treatment selection, and facilitate early intervention for associated diseases.

Purpose: Adolescent behavior is closely linked to personality, a key predictor of physical activity. Due to inconsistent findings on how personality dimensions influence physical activity, focusing on combinations of personality traits is more valuable for theoretical and practical guidance. This study aims to examine potential categories of adolescent personality and their relationship with physical activity. Methods: Using data from the 2014–2015 China Education Panel Survey (CEPS), 9212 adolescents reported their “Big Five” personality and physical activity levels after excluding samples with missing core values. Latent profile analysis with Mplus 8.3 determined the optimal model by comparing model fits to categorize personality types. Bolck-Croon-Hagenaars (BHC) analysis was used to compared physical activity across personality profiles based on the resulting class differences and its significance. Results: Latent profile analysis identified five personality trait types among adolescents based on fit indices such as AIC, BIC, aBIC, and Entropy: Low-control conservative group (5.0 %), Balanced development group (45.1 %), Optimistic action group (40.4 %), Independent avoidant group (4.5 %), and Introverted vulnerable group 5.0 %). Significant differences in physical activity were found among these profiles (p

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyber-physical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed.

With the rapid expansion of high-speed rail (HSR), the HSR station areas are often the key development areas of the cities and the gateways to display the cities’ image. However, the problem of lack of vitality in these areas has emerged. Limited studies have quantified the vitality of HSR station areas and examined the factors influencing it. The purpose of this study is to assess the impact of various factors on the vitality of HSR station areas. To accomplish this objective, we propose a method for measuring the vitality of HSR station areas using Baidu’s real-time user density data. We demonstrate the method through the case study of 91 HSR station areas in the Chengdu-Chongqing urban agglomeration in China. We construct structural equation models using a Bayesian approach to test the effects of intercity accessibility, intracity accessibility, surrounding area density, and local socio-economic development on the vitality of HSR station areas. The results show that (1) Intracity accessibility, surrounding area density, and local socioeconomic development have significant positive effects on the vitality density of HSR station areas. Intercity accessibility has a negative effect on the vitality density of HSR station areas. (2) Surrounding area density positively influences the stability of weekday and weekend vitality in HSR station areas. Local socio-economic development negatively impacts the stability of weekend vitality in HSR station areas. (3) High-vitality HSR station areas are mostly located in Chengdu and Chongqing, both megacities. Station areas with low vitality are primarily located in small cities. This study’s findings can be used to guide the planning and decision-making of HSR station areas aimed at enhancing their vitality.

Sex and thyroid hormones are critical for male reproductive health. However, the associations between haloacetic acid (HAA) exposure – a known endocrine disruptor – and sex and thyroid hormones in humans remains unclear. We thus recruited 502 male participants seeking fertility evaluation from a reproductive center. We measured concentrations of sex and thyroid hormones in a single blood sample and dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA) in repeated urine samples. Multivariable linear regression models were constructed to evaluate the associations between HAA concentrations and hormone measurements. After adjusting for potential confounders and urinary creatinine concentrations, urinary concentrations of TCAA were inversely associated with serum levels of sex hormone-binding globulin (SHBG), testosterone (T), T/luteinizing hormone ratio (T/LH), and thyroid stimulating hormone (TSH) (all P for trend

Abstract Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism’s presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Abstract Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km2. It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China.

Abstract Introduction Electrical injuries rarely result in fractures, such as long bone fractures and spinal fractures. A few articles have reported osteoporosis vertebral compression fractures (OVCFs) caused by electrical injuries. Here, we present a rare case of 37-year-old male suffering from the 9th thoracic (T9) and 5th lumbar (L5) OVCFs after receiving a electric shock. Case presentation A 37-year-old Han male experienced an electric shock (480 V direct current) at the working time and felt immediately serious back pain. He did not fall and lose consciousness. X-ray and magnetic resonance imaging showed acute OVCFs, as well as dual-energy X-ray absorptiometry indicated osteoporosis. Normal laboratory tests can avoid secondary osteoporosis resulting from metabolic diseases and tumors. Finally, he was diagnosed with acute discontinuous OVCFs (T9 and L5). The patient denied having a history of back pain, whereas, he had a history of smoking, alcohol abuse, and congenital heart disease (tetralogy of Fallot) were associated with osteoporosis. Considering no local kyphosis and

In recent years, the movement analysis is commonly used to track the risk of injury and strengthen the efficiency of athlete performance. However, most of these devices are costly, found mainly in experimental settings, which analyze a few samples of each movement. In this paper, a new ambulatory movement analysis system with wearable sensors for the precise measurement of all athleteꞌs movements in an actual training scenario is introduced. Initially, an adaptive method categorizes a broad variety of training behaviors by the Differential Finite element Transformation method (DFET) along with a Random Forest Classification (DFET- RF) method. Secondly, the measurement of the absolute identities of the wearable sensor devices placed on the knee bone and pelvic bone is performed with a discrete gradient descent (DGD) algorithm, which calculates a range of motion-extension between the knee and hip angle. Finally, the edge computing is used to process data in real-time and reduce the latency of the system. The next version of wearable technology will know the person's identity, individually - not just physically and actively in a much more significant way; a wearable device that tells the world about the identity of the person and the connected devices. The knee flexion is greater at the terminal swing period (85%) and hip flexion (68%). The development of future device capabilities is based on verification. Once a wearable can validate the wearer's identity, several other things about their activities can be regulated. Such angles are automatically extracted for each movement during jogging at the acceleration of the sacrum effect. Besides, standard data has developed and is used to decide whether the movement methodology for a person varied from the standard data to classify potential instances due to injury. This is done by a gradient-shift recording technique for the joint-related angle details. For precise and automated assessment of athletic movements, effective activity measurement in various uncontrolled conditions for both injuries processing performance progress, the suggested system has been discussed in this paper.

Abstract Background The alteration of the prognostic nutritional index (PNI) or the utilization of distinct anesthesia strategies has been linked to the prognosis of various cancer types, but the existing evidence is limited and inconclusive, particularly for colorectal cancer (CRC). Our objective was to evaluate the association between PNI change and progression free survival (PFS) and overall survival (OS) in patients treated with CRC surgery after propofol-based or sevoflurane-based anesthesia. Methods We conducted a retrospective analysis of 414 patients with CRC who underwent surgical resection. Among them, 165 patients received propofol-based total intravenous anesthesia (TIVA-P), while 249 patients received sevoflurane-based inhalation anesthesia (IA-S). The PNI change (ΔPNI) was calculated by subtracting the pre-surgery PNI from the post-surgery PNI, and patients were categorized into high (≥ -2.25) and low (

It is necessary to consider the robustness in the tracking problem, which can effectively suppress the external disturbance to ensure the tracking performance. Different from previous tracking control methods, considering the robustness, completely unknown nonlinear system dynamics and constrained controller, we propose a data-based echo state network (ESN) approximated algorithm for a class of robust tracking problems. First, the robust tracking control problem (RTCP) is transformed into the optimal control problem of the according nominal system by designing a elaborate value function. To obtain the optimal control policy, we have to solve a Hamilton-Jacobi-Bellman equation (HJBE) about the augmented nominal system. It is well-known that modelling the accurate dynamics for the practical engineering applications is usually difficult, so the model-free integral reinforcement learning (IRL) algorithm is used to learn the optimal control policy and performance function simultaneously by only using systems data. In this IRL algorithm, a reservoir computing based ESN is used to approximate the performance function and control input. Contrast to other neural networks, ESN need not consider the choice of activation function, which can greatly reduce the difficulty and effort of neural network structure design. The output weights of the ESNs are iteratively updated towards the optimal ones by using least square algorithm and the pre-collected off-line system data. Then, using the converged output weights and ESNs, the tracking control input can be derived without knowing any system dynamic information. Finally, we demonstrate that the given system can be controlled to track the desired trajectory well under the proposed method by using two simulation examples.

ABSTRACT: This study aimed to investigate the symbiosis between Streptococcus thermophilus CICC 6038 and Lactobacillus delbrueckii ssp. bulgaricus CICC 6047. In addition, the effect of their different inoculum ratios was determined, and comparison experiments of fermentation characteristics and storage stability of milk fermented by their monocultures and cocultures at optimal inoculum ratio were performed. We found the time to obtain pH 4.6 and ΔpH during storage varied among 6 inoculum ratios (1:1, 2:1, 10:1, 19:1, 50:1, 100:1). By the statistical model to evaluate the optimal ratio, the ratio of 19:1 was selected, which exhibited high acidification rate and low postacidification with pH values remaining between 4.2 and 4.4 after a 50-d storage. Among the 3 groups included in our analyses (i.e., the monocultures of S. thermophilus CICC 6038 [St] and Lb. bulgaricus CICC 6047 [Lb] and their cocultures [St+Lb] at 19:1), the coculture group showed higher acidification activity, improved rheological properties, richer typical volatile compounds, more desirable sensor quality after the fermentation process than the other 2 groups. However, the continuous accumulation of acetic acid during storage showed that acetic acid was more highly correlated with postacidification than d-lactic acid for the Lb group and St+Lb group. Our study emphasized the importance of selecting an appropriate bacterial consortium at the optimal inoculum ratio to achieve favorable fermentation performance and enhanced postacidification stability during storage.

Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.Methods The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility.Results For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214–0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort).Conclusion We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

ABSTRACTThe circumpolar arctic tundra, located at Earth’s highest latitudes, is extremely sensitive to climate warming. Studies on arctic greening, based on satellite data and field measurements, show discrepancies due to differences in spatial resolution across datasets (e.g., Landsat 30-m, MODIS 250-m, and AVHRR GIMMS 8 km). Research on scale effects has been limited, mostly focusing on small areas rather than the entire 7.11 million km² arctic tundra. Our study addresses this by mapping scale effects across the entire tundra using Normalized Difference Vegetation Index (NDVI) measurements. Findings reveal: (1) Landsat data provides detailed spatial trends, identifying 18.7% of the area as significantly greening, whereas GIMMS data detects more browning due to spectral mixing; (2) GIMMS underestimates the greening to browning ratio at 2.2:1, compared to Landsat and MODIS ratios of 14.1:1 and 15.1:1, respectively; (3) Over 93% agreement exists between Landsat and MODIS or GIMMS trends, with discrepancies in limited areas. This highlights the importance of high-resolution data and field studies for accurately understanding vegetation trends across the arctic tundra.

ABSTRACTPermafrost degradation has strong and long-lasting effects on anthropogenic land-use activities, contradicting the goal of sustainable development in polar and high-elevation regions. The artificial surface (AS) plays a central role in determining human-environment relationships in permafrost environments. Despite recent progress in monitoring land surfaces, attempts to map permafrost AS with satellite remote sensing have been limited. In this study, we propose an operational framework for fine-resolution mapping and assessment of permafrost AS across the entire Northern Hemisphere landmass. The proposed framework was designed to take advantage of prior knowledge obtained from existing global-scale land-cover products. As a result, a 10 m resolution permafrost AS map for 2016–2017 was created using a locally adaptive classification strategy. We found that the created map exhibited an overall accuracy of 91.7 ± 2.1% with minimum accuracies > 70%. We estimated that the total area of permafrost AS in the Northern Hemisphere was approximately 9,000 km2, most notably in the Russian Arctic. Future projections indicate that there will be over one-seventh of the permafrost AS area at high geohazard risk by the end of the twenty-first century. Our study provides new perspectives on the ‘permafrost-human-climate’ nexus, which can advance our understanding of the terrestrial system.

Nuclear batteries, a novel energy device in microelectromechanical systems (MEMS), have garnered significant attention from academia and industry due to their promising application prospects. They possess high energy density and reliable operation without human intervention and offer unique advantages in the case of long-term stable power supply. Among these, thermal conversion nuclear batteries (RTGs) represent the most mature technology and the earliest application, while betavoltaic nuclear batteries have entered commercialization. Challenges in betavoltaic nuclear batteries research include energy wastage due to the self-absorption effect of radioactive sources, low conversion efficiency, and significant radiation damage to transducer devices. These issues are attributable not only to the inherent properties of the radioactive source but also to the material and structural design of transducers. A 3D interface structure design scheme based on the wide bandgap semiconductor material GaN and the radioactive isotope 63Ni nuclear microbatteries is proposed. In the scheme, Geant4 and COMSOL Multiphysics were used to simulate the GaN-based betavoltaic nuclear battery of 63Ni source, and the PN junction 3D interface structure of the transducer was designed and optimized. The effects of the surface area, number of micropillars, thickness, and doping concentration of each region on the battery performance were analyzed. Results indicate that with P- and N- region thicknesses and doping concentrations at 0.1, 9.9 µm, 1 × 1018, and 1 × 1014 cm−3, respectively, the nuclear battery can achieve a conversion efficiency of 7.57%, a short-circuit current density of 0.3959 µA/cm2, an open-circuit voltage of 2.3074 V, and maximum output power of 0.7795 µW/cm2. In addition, discussion regarding the surface area and quantity of P-layer micropillars confirms the hypothesis that these variables are positively correlated with the output performance of the transducer.

Abstract Myocardial infarction (MI) is a leading cause of death worldwide. Few drugs hold the ability to depress cardiac electrical and structural remodeling simultaneously after MI, which is crucial for the treatment of MI. The aim of this study is to investigate an effective therapy to improve both electrical and structural remodeling of the heart caused by MI. Here, an “ion cocktail therapy” is proposed to simultaneously reverse cardiac structural and electrical remodeling post‐MI in rats and minipigs by applying a unique combination of silicate, strontium (Sr) and copper (Cu) ions due to their specific regulatory effects on the behavior of the key cells involved in MI including angiogenesis of endothelial cells, M2 polarization of macrophages and apoptosis of cardiomyocyte. The results demonstrate that ion cocktail treatment attenuates structural remodeling post‐MI by ameliorating infarct size, promoting angiogenesis in both peri‐infarct and infarct areas. Meantime, to some extent, ion cocktail treatment reverses the deteriorative electrical remodeling by reducing the incidence rate of early/delayed afterdepolarizations and minimizing the heterogeneity of cardiac electrophysiology. This ion cocktail therapy reveals a new strategy to effectively treat MI with great clinical translation potential due to the high effectiveness and safety of the ion cocktail combination.

The interaction between water and graphene is important for understanding the thermodynamic and kinetic properties of water on hydrophobic surfaces. In this study, we constructed a high-dimensional potential energy surface (PES) for the water-graphene system using the many-body expansion scheme and neural network fitting. By analyzing the landscape of the PES, we found that the water molecule exhibits a weak physisorption behavior with a binding energy of about − 1000 cm−1 and a very low diffusion barrier. Furthermore, extensive molecular dynamics were performed to investigate the adsorption and diffusion dynamics of a single water on a graphene surface at temperatures ranging from 50 to 300 K. Potential-of-mean-forces were computed from the trajectories, providing a comprehensive and accurate description of the water-graphene interaction kinetics.

Abstract Objective Radiation therapy (RT) may increase the risk of second cancer. This study aimed to determine the association between exposure to radiotherapy for the treatment of thoracic cancer (TC) and subsequent secondary lung cancer (SLC). Materials and Methods The Surveillance, Epidemiology, and End Results (SEER) database (from 1975 to 2015) was queried for TC. Univariate Cox regression analyses and multiple primary standardized incidence ratios (SIRs) were used to assess the risk of SLC. Subgroup analyses of patients stratified by latency time since TC diagnosis, age at TC diagnosis, and calendar year of TC diagnosis stage were also performed. Overall survival and SLC‐related death were compared among the RT and no radiation therapy (NRT) groups by using Kaplan–Meier analysis and competitive risk analysis. Results In a total of 329 129 observations, 147 847 of whom had been treated with RT. And 6799 patients developed SLC. Receiving radiotherapy was related to a higher risk of developing SLC for TC patients (adjusted HR, 1.25; 95% CI, 1.19–1.32; P

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with multiple etiological factors. Immune disorder contributes to SLE development and is an important clinical manifestation of SLE patients. Immune dysfunction is characterized by abnormal of B cells, T cells, monocyte-macrophages and dendritic cells (DCs), in both quantity and quality. Adenosine is a critical factor for human immune homeostasis, which acts as an immunosuppressive signal and can prevent the hyperactivity of human immune system. Adenosine levels are significant decreased in serum from SLE patients. Adenosine level is regulated by the CD39, CD73 and adenosine deaminase (ADA). CD39/CD73/ADA catalyzed the cascade enzymatic reaction, which contained the adenosine generation and degradation. Adenosine affects the function of various immune cells via bind to the adenosine receptors, which are expressed on the cell surface. This review aims to export the changes of immune cells and adenosine signal pathway in SLE, as well as the effect of adenosine signal pathway in SLE development.

Abstract Stand spatial structure plays a key role in forest management, and in particular the random structural unit, comprising random tree and its neighbors, largely determines forest stability and its productivity. However, how the spatial structure of the random structural unit affects productivity remains unclear. The study focused on four larch forest types from Hebei and Shanxi provinces, China: 35‐year‐old larch (Larix principis‐rupprechtii) plantations (35LP), 39‐year‐old mixed larch–birch (Betula platyphylla) forests (39LB), 58‐year‐old natural larch forests (58LN), and 73‐year‐old mixed larch–birch–spruce (Picea asperata) forests (73LBS). The forest spatial structure index (FSSI) was employed to comprehensively evaluate the stand spatial structure. Additionally, the uniform angle index was used to discern whether the stand structure units were uniform, random, or clumped. A regression model was used to elucidate the effects of species mingling, diameter dominance, and crowding on the productivity of random trees. Results showed that the FSSI varied among the stand types, ranking as 35LP

Abstract Mineralization is a long-lasting method commonly used by biological materials to selectively strengthen in response to site specific mechanical stress. Achieving a similar form of toughening in synthetic polymer composites remains challenging. In previous work, we developed methods to promote chemical reactions via the piezoelectrochemical effect with mechanical responses of inorganic, ZnO nanoparticles. Herein, we report a distinct example of a mechanically-mediated reaction in which the spherical ZnO nanoparticles react themselves leading to the formation of microrods composed of a Zn/S mineral inside an organogel. The microrods can be used to selectively create mineral deposits within the material resulting in the strengthening of the overall resulting composite.

Abstract Background Gout pain seriously affects the quality of patients' life. There is still no effective treatment. The inflammatory response is the main mechanism of gout. Here, we found that ozone can reduce the inflammatory reaction in the joints of gouty mice and relieve gout pain, and we further explore its protective mechanism. Methods MSU was used to establish the gouty mice model. Nociception was assessed by Von Frey hairs. Cell signaling assays were performed by western blotting and immunohistochemistry. The mouse leukemia cells of monocyte macrophage line RAW264.7 were cultured to investigate the effects of ozone administration on macrophage. Results Ozone reduced inflammation, relieved gout pain and improved the paw mean intensity and duty cycle of the gouty mice. Ozone increased the phosphorylation of AMP-activated protein kinase (AMPK), induced suppressor of cytokine signaling 3 (SOCS3) expression and inhibited metallopeptidase 9 (MMP9) expression. In vivo, ozone activated AMPK to induce Gas6 release, and upregulated MerTK/SOCS3 signaling pathway to reduce inflammation in mouse macrophage line RAW264.7. Inhibitors of AMPK and MerTK, respectively abolished the analgesic and anti-inflammatory effects of ozone in vivo and in vitro. Gas6 knockout cancelled the protectively effects of ozone on gout pain and the paw mean intensity and duty cycle of gouty mice. Additionally, the level of Gas6 and protein S in plasma of patients with hyperuricemia was significantly higher than that of healthy contrast group. Conclusion Ozone reduces inflammation and alleviates gout pain by activating AMPK to up-regulate Gas6/MerTK/SOCS3 signaling pathway.

Salinity is an essential factor of lake water environments and aquatic systems. It is also sensitive to climatic changes and human activities based on concentration variations of solved minerals. However, there are few consecutively temporal studies on lake salinity variations on the Tibetan Plateau because the harsh environmental conditions make it difficult to carry out in-situ observations for several lakes. In this study, we constructed a remote sensing retrieval model for lake salinity based on 87 in-situ lake investigations; moreover, interannual lake salinity and associated variations from 152 lakes larger than 50 km2 were analyzed on the Tibetan Plateau. A significant decreasing trend in lake salinity was observed between 2000 and 2019 (p

Abstract Accurate and large-scale assessment of volumetric water content (VWC) plays a critical role in mining waste monitoring to mitigate potential geotechnical and environmental risks. In recent years, time-lapse electrical resistivity tomography (TL-ERT) has emerged as a promising monitoring approach that can be used in combination with traditional invasive and point-measurements techniques to estimate VWC in mine tailings. Moreover, the bulk electrical conductivity (EC) imaged using TL-ERT can be converted into VWC in the field using petrophysical relationships calibrated in the laboratory. This study is the first to assess the scale effect on the accuracy of ERT-predicted VWC in tailings. Simultaneous and co-located monitoring of bulk EC and VWC are carried out in tailings at five different scales, in the laboratory and in the field. The hydrogeophysical datasets are used to calibrate a petrophysical model used to predict VWC from TL-ERT data. Overall, the accuracy of ERT-predicted VWC is $$\pm 0.03~\textrm{m}^3/\textrm{m}^3$$ ± 0.03 m 3 / m 3 , and the petrophysical models determined at sample-scale in the laboratory remain valid at larger scales. Notably, the impact of temperature and pore water EC evolution plays a major role in VWC predictions at the field scale (tenfold reduction of accuracy) and, therefore, must be properly taken into account during the TL-ERT data processing using complementary hydrogeological sensors. Based on these results, we suggest that future studies using TL-ERT to predict VWC in mine tailings could use sample-scale laboratory apparatus similar to the electrical resistivity Tempe cell presented here to calibrate petrophysical models and carefully upscale them to field applications.

Abstract Objective To explore the DPP4 expression changes and functions in ovarian cancer (OV), as well as the regulation mechanism for DDP4. Methods GEPIA2, GSE18520, GSE26712 and UALCAN were used to analyze differences in DPP4 expression between OV tumors and control tissues. Serum DPP4 levels were measured by ELISA. The prognostic values of DPP4 were evaluated using a Kaplan–Meier (KM) plotter. Small interfering RNA was used for DPP4 knockdown in OVCAR-3 and SKOV-3 cells. CCK-8 and scratch healing assays were used to determine the cells’ proliferation and migration abilities. Flow cytometry (FCM) was used to detect the cell cycle and apoptosis. A dual-luciferase assay was designed to confirm the regulatory effect of miR-29a-3p on DPP4. Results The expressions of DPP4 mRNA and protein were decreased in OV tumor tissues. Serum DPP4 levels decreased in OV patients. KM plotter analysis showed correlation between high DPP4 expression and a poor prognosis in OV patients. By targeting knockdown of DPP4, we found that OVCAR-3 and SKOV-3 cells’ proliferation was inhibited, while cell’s migration ability was significantly promoted. FCM analysis showed that DPP4 knockdown induced a decrease in the S phase. Furthermore, DPP4 was shown to be downregulated by miR-29a-3p and TGFβ1 in OVCAR-3 cells, and miR-29a-3p expression was upregulated by TGFβ1. The effects of miR-29a-3p and TGFβ1 on OVCAR-3 cells’ biological behaviors were consistent with DPP4 knockdown. Conclusion DPP4 was downregulated in OV patients. DPP4 knockdown significantly inhibited OVCAR-3 and SKOV-3 cell proliferation and promoted cell migration. DDP4 can be downregulated by TGFβ1 through the upregulation of miR-29a-3p in OV cells.

Abstract To produce a three-dimensional micro/nanocarbon structure, a manufacturing design technique for sub-10 nm carbon fiber arrays on three-dimensional carbon micropillars has been developed; the method involves initiating electrostatic jetting, forming submicron-to-nanoscale PAN-based fibers, and maximizing the shrinkage from polyacrylonitrile (PAN)-based fibers to carbon fibers. Nanoforming and nanodepositing methods for polyacrylonitrile-based jet fibers as precursors of carbon fibers are proposed for the processing design of electrostatic jet initiation and for the forming design of submicron-to-nanoscale PAN-based fibers by establishing and analyzing mathematical models that include the diameter and tensile stress values of jet fibers and the electric field intensity values on the surfaces of carbon micropillars. In accordance with these methods, an array of jet fibers with diameters of ~80 nm is experimentally formed based on the thinning of the electrospinning fluid on top of a dispensing needle, the poking of drum into an electrospinning droplet, and the controlling of the needle–drum distance. When converting thin PAN-based jet fibers to carbon fibers, a pyrolysis method consisting of the suspension of jet nanofibers between carbon micropillars, the bond between the fibers and the surface of the carbon micropillar, and the control of micropillar spacing, stabilization temperature, and carbonation rate is presented to maximize the shrinkage from PAN-based fibers to carbon fibers and to form sub-10 nm carbon fiber arrays between three-dimensional carbon micropillars. The manufacturing design of a three-dimensional micro/nanocarbon structure can produce thin PAN-based jet nanofibers and nanofiber arrays aligned on micropillar surfaces, obtain shrinkage levels reaching 96% and incorporate sub-10 nm carbon fibers into three-dimensional carbon micropillars; these actions provide new research opportunities for correlated three-dimensional micro/nanocarbon structures that have not previously been technically possible.

Abstract Background Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges. Method A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information. Results In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%. Conclusions Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

The widespread environmental contamination resulting from the misuse of tetracycline antibiotics (TCs) has garnered significant attention and study by scholars. Photocatalytic technology is one of the environmentally friendly advanced oxidation processes (AOPs) that can effectively solve the problem of residue of TCs in the water environment. This study involved the synthesis of the heterogeneous magnetic photocatalytic material of CoFe2O4/NaBiO3 via the solvothermal method, and it was characterized using different characterization techniques. Then, the photocatalytic system under visible light (Vis) was coupled with peroxymonosulfate (PMS) to explore the performance and mechanism of degradation of tetracycline hydrochloride (TCH) in the wastewater. The characterization results revealed that CoFe2O4/NaBiO3 effectively alleviated the agglomeration phenomenon of CoFe2O4 particles, increased the specific surface area, effectively narrowed the band gap, expanded the visible light absorption spectrum, and inhibited recombination of photogenerated electron–hole pairs. In the Vis+CoFe2O4/NaBiO3+PMS system, CoFe2O4/NaBiO3 effectively activated PMS to produce hydroxyl radicals (·OH) and sulfate radicals (SO4−). Under the conditions of a TCH concentration of 10 mg/L−1, a catalyst concentration of 1 g/L−1 and a PMS concentration of 100 mg/L−1, the degradation efficiency of TCH reached 94% after 100 min illumination. The degradation of TCH was enhanced with the increase in the CoFe2O4/NaBiO3 and PMS dosage. The solution pH and organic matter had a significant impact on TCH degradation. Notably, the TCH degradation efficiency decreased inversely with increasing values of these parameters. The quenching experiments indicated that the free radicals contributing to the Vis+CoFe2O4/NaBiO3+PMS system were ·OH followed by SO4−, hole (h+), and the superoxide radical (O2−). The main mechanism of PMS was based on the cycle of Co3+ and Co2+, as well as Fe3+ and Fe2+. The cyclic tests and characterization by XRD and FT-IR revealed that CoFe2O4/NaBiO3 had good degradation stability. The experimental findings can serve as a reference for the complete removal of antibiotics from wastewater.

Ligusticum Chuanxiong, a perennial herb of considerable medicinal value commonly known as Chuanxiong, holds pivotal importance in sliced form for ensuring quality and regulating markets through geographical origin identification. This study introduces an integrated approach utilizing Near-Infrared Spectroscopy (NIRS) and Convolutional Neural Networks (CNNs) to establish an efficient method for rapidly determining the geographical origin of Chuanxiong slices. A dataset comprising 300 samples from 6 distinct origins was analyzed using a 1D-CNN model. In this study, we initially established a traditional classification model. By utilizing the Spectrum Outlier feature in TQ-Analyst 9 software to exclude outliers, we have enhanced the performance of the model. After evaluating various spectral preprocessing techniques, we selected Savitzky–Golay filtering combined with Multiplicative Scatter Correction (S-G + MSC) to process the raw spectral data. This approach significantly improved the predictive accuracy of the model. After 2000 iterations of training, the CNN model achieved a prediction accuracy of 92.22%, marking a 12.09% improvement over traditional methods. The application of the Class Activation Mapping algorithm not only visualized the feature extraction process but also enhanced the traditional model’s classification accuracy by an additional 7.41% when integrated with features extracted from the CNN model. This research provides a powerful tool for the quality control of Chuanxiong slices and presents a novel perspective on the quality inspection of other agricultural products.

A metal-free and Selectfluor-mediated selective oxidation reaction of benzo[d]isothiazol-3(2H)-ones in aqueous media is presented. This novel strategy provides a facile, green, and efficient approach to access important benzo[d]isothiazol-3(2H)-one-1-oxides with excellent yields and high tolerance to various functional groups. Furthermore, the purification of benzoisothiazol-3-one-1-oxides does not rely on column chromatography. Moreover, the preparation of saccharine derivatives has been achieved through sequential, double oxidation reactions in a one-pot aqueous media.

The rapid development of unmanned aerial vehicle (UAV) technology has contributed to the increasing sophistication of UAV-based object-detection systems, which are now extensively utilized in civilian and military sectors. However, object detection from UAV images has numerous challenges, including significant variations in the object size, changing spatial configurations, and cluttered backgrounds with multiple interfering elements. To address these challenges, we propose SOD-YOLO, an innovative model based on the YOLOv8 model, to detect small objects in UAV images. The model integrates the receptive field convolutional block attention module (RFCBAM) in the backbone network to perform downsampling, improving feature extraction efficiency and mitigating the spatial information sparsity caused by downsampling. Additionally, we developed a novel neck architecture called the balanced spatial and semantic information fusion pyramid network (BSSI-FPN) designed for multi-scale feature fusion. The BSSI-FPN effectively balances spatial and semantic information across feature maps using three primary strategies: fully utilizing large-scale features, increasing the frequency of multi-scale feature fusion, and implementing dynamic upsampling. The experimental results on the VisDrone2019 dataset demonstrate that SOD-YOLO-s improves the mAP50 indicator by 3% compared to YOLOv8s while reducing the number of parameters and computational complexity by 84.2% and 30%, respectively. Compared to YOLOv8l, SOD-YOLO-l improves the mAP50 indicator by 7.7% and reduces the number of parameters by 59.6%. Compared to other existing methods, SODA-YOLO-l achieves the highest detection accuracy, demonstrating the superiority of the proposed method.

To qualify as competent sorbents for airborne contaminants such as iodine vapor, permanent porosity and chemical stability are key criteria for the selection of candidate metal-organic frameworks (MOFs). To ensure these characteristics, in the present study, an unsymmetrical bifunctional ligand incorporating both carboxylic acid and hydroxamic acid groups was employed for MOF [Zn(CBHA)](DMF) [SUM-13; CPHA = 4-carboxyphenylhydroxamate, DMF = N,N-dimethylformamide] design and synthesis. Though coupled with Zn2+, which does not typically yield kinetically robust MOFs with hard acids, the SUM-13 featuring differentiated coordination modes of chelating, bridging and monodentate bonding exhibited exceptional chemical stability and permanent porosity, with a Brunauer–Emmett–Teller (BET) surface area of 296.9 m2/g and a total pore volume of 0.1196 cm3/g. Additionally, with porosity and open metal sites at the five-coordinate Zn2+ centers, SUM-13 was demonstrated to be an eligible iodine adsorbent, reaching a maximum uptake of 796 mg/g. These findings underscore the validity and potential of the design strategy in constructing stable metal–organic frameworks.

The fractional-order grey prediction model is widely recognized for its performance in time series prediction tasks with small sample characteristics. However, its parameter-estimation method, namely the least squares method, limits the predictive performance of the model and requires time to address the ill-conditioning of the system. To address these issues, this paper proposes a novel parameter-acquisition method treating structural parameters as hyperparameters, obtained through the marine predators optimization algorithm. The experimental analysis on three datasets validate the effectiveness of the method proposed in this paper.

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model’s performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients’ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.