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The spread through air space (STAS) in lung cancer refers to the presence of tumor cells in the airway outside the main tumor body in the form of micropapillary cell clusters, solid cell nests, or individual cells. The researches of lung cancer in recent years discover the significant value of STAS for diagnosis, treatment, and prognosis, therefore the World Health Organization designated it as a new invasive pattern of lung adenocarcinoma in 2015. Predicting the presence of STAS by medicine imaging has been the research highlight in recent years. Though the feasibility of predicting STAS by medicine imaging has been confirmed, more in-depth researches are necessary for establishing more reliable and accurate prediction method in medicine imaging. This article reviews the research progresses of pathology, clinical significance and medicine imaging on STAS.

Abstract Generalizable and accurate stereo depth estimation is vital for 3D reconstruction, especially in surgery. Supervised learning methods obtain best performance however, limited ground truth data for surgical scenes limits generalizability. Self‐supervised methods don't need ground truth, but suffer from scale ambiguity and incorrect disparity prediction due to inconsistency of photometric loss. This work proposes a two‐phase training procedure that is generalizable and retains the high performance of supervised methods. It entails: (1) performing self‐supervised representation learning of left and right views via masked image modelling (MIM) to learn generalizable semantic stereo features (2) utilizing the MIM pre‐trained model to learn robust depth representation via supervised learning for disparity estimation on synthetic data only. To improve stereo representations learnt via MIM, perceptual loss terms are introduced, which improve the model's stereo representations learnt by explicitly encouraging the learning of higher scene‐level features. Qualitative and quantitative performance evaluation on surgical and natural scenes shows that the approach achieves sub‐millimetre accuracy and lowest errors respectively, setting a new state‐of‐the‐art. Despite not training on surgical nor natural scene data for disparity estimation.

Abstract The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 1010 cm−2 down to 3.8 × 108 cm−2 or up to 1.4 × 1011 cm−2. Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

Background: The application of nanomaterials (NMs) in the treatment of periodontitis and peri-implantitis has shown multifunctional benefits, such as antibacterial properties, immune regulation, and promotion of osteogenesis. However, a comprehensive bibliometric analysis to evaluate global scientific production in this field has not yet been conducted. Method: We searched for publications related to nanomaterials in periodontitis and peri-implantitis using the WOSCC database. The contributions from institutions, journals, countries, and authors were assessed using VOSviewer, the bibliometrix R package, and Microsoft Excel 2019. Results: We identified 2275 publications from 66 countries/regions focusing on nanomaterials in periodontitis and peri-implantitis, published between 1993 and 2023. China and the USA were the top contributors in this field, with 653 and 221 publications, respectively. Key topics include antibacterial properties, delivery systems, nanoparticles, and regeneration. The research focus has evolved from traditional treatments to advanced applications of multifunctional nanomaterials. Conclusion: Significant progress has been made in the application of NMs in periodontitis and peri-implantitis from 1993 to 2023. Future research hotspots will likely focus on multifunctional nanomaterials and those adhering to good manufacturing practices (GMP).

Background Musculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic. Methods We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed. Results A total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics. Conclusion The COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.

GeSn materials with Sn contents ranging between 0% and 11% have been successfully grown by using Ge2H6 and SnCl4 in a hot-wall UHV-CVD system at low reaction pressures of 0.2–0.4 Torr. Both fully strained and partially relaxed materials have been produced. It was found that the materials were of high crystallinity as evidenced by few threading dislocations from cross-sectional TEM. Photoluminescence studies have shown that the PL spectra shift to longer wavelengths, and the PL intensity increases exponentially as the Sn contents increase. The photoemission cutoff for a 9.5% Sn sample is beyond 2600 nm, and low temperature PL measurements have confirmed that this sample is already in the direct gap material regime. Prototype photoconductor devices were fabricated with a partially strained 5% Sn sample, showing much smaller dark currents and similar spectral coverage as compared to a fully strained 7.8% Sn device reported in the literature. The reported technical route has provided a new path toward high quality, low cost GeSn epitaxy, and will contribute to the development of group IV optoelectronics.

Background Epstein-Barr virus (EBV) is a double-stranded DNA oncogenic virus. Several types of solid tumors, such as nasopharyngeal carcinoma, EBV-associated gastric carcinoma, and lymphoepithelioma-like carcinoma of the lung, have been linked to EBV infection. Currently, several TCR-T-cell therapies for EBV-associated tumors are in clinical trials, but due to the suppressive immune microenvironment of solid tumors, the clinical application of TCR-T-cell therapy for EBV-associated solid tumors is limited. Figuring out the mechanism by which EBV participates in the formation of the tumor immunosuppressive microenvironment will help T cells or TCR-T cells break through the limitation and exert stronger antitumor potential.Methods Flow cytometry was used for analyzing macrophage differentiation phenotypes induced by EBV-infected and EBV-uninfected tumors, as well as the function of T cells co-cultured with these macrophages. Xenograft model in mice was used to explore the effects of M2 macrophages, TCR-T cells, and matrix metalloprotein 9 (MMP9) inhibitors on the growth of EBV-infected tumors.Results EBV-positive tumors exhibited an exhaustion profile of T cells, despite the presence of a large T-cell infiltration. EBV-infected tumors recruited a large number of mononuclear macrophages with CCL5 and induced CD163+M2 macrophages polarization through the secretion of CSF1 and the promotion of autocrine IL10 production by mononuclear macrophages. Massive secretion of MMP9 by this group of CD163+M2 macrophages induced by EBV infection was an important factor contributing to T-cell exhaustion and TCR-T-cell therapy resistance in EBV-positive tumors, and the use of MMP9 inhibitors improved the function of T cells cocultured with M2 macrophages. Finally, the combination of an MMP9 inhibitor with TCR-T cells targeting EBV-positive tumors significantly inhibited the growth of xenografts in mice.Conclusions MMP9 inhibitors improve TCR-T cell function suppressed by EBV-induced M2 macrophages. TCR-T-cell therapy combined with MMP9 inhibitors was an effective therapeutic strategy for EBV-positive solid tumors.

Abstract Background Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty (TJA). Although some risk factors of PJI were well studied, the association between trauma and PJI remains unknown in revision patients. Materials and methods Between 2015 and 2018, a total of 71 patients with trauma history before revisions (trauma cohort) were propensity score matched (PSM) at a ratio of 1 to 5 with a control cohort of revision patients without a history of trauma. Then, the cumulative incidence rate of PJI within 3 years after operation between the two groups was compared. The secondary endpoints were aseptic revisions within 3 postoperative years, complications up to 30 postoperative days, and readmission up to 90 days. During a minimal 3-year follow-up, the survival was comparatively analyzed between the trauma cohort and the control cohort. Results The cumulative incidence of PJI was 40.85% in patients with trauma history against 27.04% in the controls (P = 0.02). Correspondingly, the cumulative incidence of aseptic re-revisions was 12.68% in patients with trauma history compared with 5.07% in the control cohort (P = 0.028). Cox regression revealed that trauma history was a risk factor of PJI (HR, 1.533 [95%CI, (1.019,2.306)]; P = 0.04) and aseptic re-revisions (HR, 3.285 [95%CI, (1.790,6.028)]; P

Industrial control systems are facing ever-increasing security challenges due to the large-scale access of heterogeneous devices in the open Internet environment. Existing anomaly detection methods are mainly based on the priori knowledge of industrial control protocols (ICPs) whose protocol specifications, communication mechanism, and data format are already known. However, when these knowledge are blank, namely, unknown ICPs, existing methods become powerless to detect the anomaly data. To tackle this challenge, we propose a multigranularity feature automatic marking-based deep learning method to classify unknown ICPs for anomaly detection. First, to obtain the feature sequences without priori knowledge assisting, we propose a multigranularity feature extraction algorithm to extract both byte and half-byte information by fully utilizing the intensive key information in the header field of the application layer. Then, to label the feature sequences for deep learning, we propose a feature automatic marking algorithm that utilizes the inconsistency feature sequences to dynamically update the feature sequence set. With the labeled feature sequences, we employ deep learning with 1-D convolutional neural network and gated recurrent unit to classify the unknown ICPs and realize anomaly detection. Extensive experiments on two public datasets show that both the accuracy and precision of the proposed method reach above 98.4%, which is better than the three benchmark methods.

With the wide applications of industrial wireless network technologies, the industrial control system (ICS) is evolving from wired and centralized to wireless and distributed, during which eavesdropping and attacking become serious problems. To guarantee the security of wireless and distributed ICS, this article establishes an end-edge collaborative lightweight secure federated learning (LSFL) architecture and proposes an LSFL anomaly detection strategy. Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. Then, to reduce the wireless communication cost for parameter exchange and edge federal learning, we propose a dynamic parameter pruning algorithm by evaluating the contribution of each parameter based on the information entropy gain. Furthermore, to ensure the parameter security during wireless transmission in the open radio environment, we propose an adaptive key generation algorithm for parameter encryption. Finally, the proposed strategy is experimentally validated on representative datasets, including Smart Meter, NSL-KDD, and UNSW-NB15. Experimental results demonstrate that the proposed strategy achieves 99% accuracy on different datasets, where at least 89.6% wireless communication cost is reduced and tampering/injecting attacks are defended.

The Naoli River Basin (NRB), a pivotal agricultural production area in China, is poised to undergo substantial impacts on water resources due to projected climate and land use/cover (LULC) changes. Despite its significance in the context of China's expanding farmland construction in the NRB, there exists limited research on the potential repercussions of future shifts in runoff, soil water content (SWC), and evapotranspiration (ET) on crop productivity and water availability (both in terms of quantity and timing). This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models (GCMs) across three scenarios to drive the well-calibrated distributed hydrological model, ESSI-3. The objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term (2026–2050), middle-term (2051–2075), and far-term (2076–2100) future in comparison to the baseline period of 1990–2014. Results consistently indicate an increase trend in annual average ET, runoff, and SWC in the NRB across all three future periods under the three SSP scenarios. LULC changes emerge as the primary driver influencing regional hydrological processes in the near future. Notably, under high-emission scenarios, monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the baseline. This shift is attributed to the anticipated warming of winter and spring, leading to a transition in peak snowmelt from April to March. Concurrently, the expansion of cropland intensifies crop evapotranspiration demand, potentially exacerbating water stress during the early stages of crop growth in April. The findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling processes. Early planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing water-related challenges in the NRB and neighboring mid-high latitude regions.

Background S100a8/9 (S100 calcium binding protein a8/9) belongs to the S100 family and has gained a lot of interest as a critical regulator of inflammatory response. Our previous study found that S100a8/9 homolog promoted aortic valve sclerosis in mice with chronic kidney disease. However, the role of S100a8/9 in pressure overload‐induced cardiac hypertrophy remains unclear. The present study was to explore the role of S100a8/9 in cardiac hypertrophy. Methods and Results Cardiomyocyte‐specific S100a9 loss or gain of function was achieved using an adeno‐associated virus system, and the model of cardiac hypertrophy was established by aortic banding‐induced pressure overload. The results indicate that S100a8/9 expression was increased in response to pressure overload. S100a9 deficiency alleviated pressure overload‐induced hypertrophic response, whereas S100a9 overexpression accelerated cardiac hypertrophy. S100a9‐overexpressed mice showed increased FGF23 (fibroblast growth factor 23) expression in the hearts after exposure to pressure overload, which activated calcineurin/NFAT (nuclear factor of activated T cells) signaling in cardiac myocytes and thus promoted hypertrophic response. A specific antibody that blocks FGFR4 (FGF receptor 4) largely abolished the prohypertrophic response of S100a9 in mice. Conclusions In conclusion, S100a8/9 promoted the development of cardiac hypertrophy in mice. Targeting S100a8/9 may be a promising therapeutic approach to treat cardiac hypertrophy.

Abstract Objectives The proper procedure for inferior pulmonary ligament (IPL) during upper lobectomy remains a topic of debate. To address this matter, we carried out a trial comparing the clinical outcomes of IPL preservation versus IPL dissection during thoracoscopic upper lobectomy (TUL). Methods Patients undergoing thoracoscopic left/right upper lobectomy (TLUL/TRUL) were assigned to either the dissection group (Group D) or the preservation group (Group P). Our primary objective was to quantify and compare the alterations in postoperative residual bronchial angle and lung volume changes between the two groups. Our secondary objective encompassed the assessment of various other intraoperative and postoperative outcomes. Results Following adherence to the inclusion and exclusion criteria, we enrolled 100 patients (41 left and 59 right) in Group P and 108 patients (41 left and 67 right) in Group D for the study. Our findings revealed that in TLUL, Group P was able to reduce the degree of postoperative residual bronchial angle change (P 0.05) with regard to alterations in lung volume or the occurrence of postoperative complications—except for the duration of postoperative hospital stay (P

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.

Background: The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers. Methods: The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells. Results: A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells. Conclusion: The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

A virtual synchronous generator (VSG) strategy can introduce the rotational inertia and damping characteristics of the synchronous generator to the static inverter, e.g., PV, wind generation, and ESS, which are used to enhance the system frequency support characteristics of the micro-grid. However, under various operations of the VSG, the inertia and damping support capabilities are different, and the conventional VSG strategy with a fixed control coefficient sacrifices a certain degree of dynamic regulation performance with less robustness. This research proposes an improved VSG strategy with adaptive inertia and damping coefficients to increase the flexibility of VSG. To this end, a mathematical model is first established to analyze the impact of various parameters on the characteristics of VSG, and then the root trajectory is used to explore the impacts of various rotational inertia and damping coefficients on the stability of the VSG system. Second, an improved control strategy with adaptive inertia and damping coefficients is proposed based on the characteristics of the second-order system and the system frequency deviations. Finally, a simulation system with the VSG is constructed based on MATLAB/Simulink. The effectiveness of the proposed control strategy is verified by comparing the simulation results to the conventional control strategy with the fixed control coefficients under over-frequency and under-frequency disturbances.

Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.

Abstract Background B7-H3 is an important immune checkpoint molecule that plays a negative role in immune regulation. This study was aimed to explore B7-H3 expression in HIV-infected patients and its clinical significance. Methods To explore the expression and clinical significance of B7-H3 in HIV-infected patients, we investigated the B7-H3 expression pattern and the correlation of B7-H3 expression with clinical parameters of HIV-infected patients with different levels of CD4+ T cells. To assess the role of B7-H3 in regulating the function of T cells in HIV infection, we performed a proliferation assay and T cell function test in vitro. Results B7-H3 expression in HIV-infected patients was significantly higher than that in healthy controls. mB7-H3 expression on CD4+CD25high T cells and CD14+ monocytes increased with disease progression. mB7-H3 expression on CD4+CD25high T cells and monocytes was negatively correlated with lymphocyte count, CD4+T cell count, and positively correlated with HIV viral load in HIV-infected patients. when the number of CD4+ T cells in HIV-infected patients was ≥ 200/µL, sB7-H3 and mB7-H3 expression levels on CD4+CD25high T cells and monocytes were negatively correlated with lymphocyte count, CD4+T cell count. sB7-H3 and mB7-H3 expression on monocytes were positively correlated with HIV viral load. B7-H3 inhibited the proliferation of lymphocytes and the secretion of IFN-γ in vitro, especially the ability of CD8+ T cells to secrete IFN-γ. Conclusions B7-H3 played an important negative regulatory role in anti-HIV infection immunity. It could be used as a potential biomarker for the progression of HIV infection and a novel target for the treatment of HIV infection.

Abstract Aims Periprosthetic joint infection (PJI) is one of the most serious complications after total joint arthroplasty (TJA) but the characterization of the periprosthetic environment microbiome after TJA remains unknown. Here, we performed a prospective study based on metagenomic next-generation sequencing to explore the periprosthetic microbiota in patients with suspected PJI. Methods We recruited 28 patients with culture-positive PJI, 14 patients with culture-negative PJI, and 35 patients without PJI, which was followed by joint aspiration, untargeted metagenomic next-generation sequencing (mNGS), and bioinformatics analysis. Our results showed that the periprosthetic environment microbiome was significantly different between the PJI group and the non-PJI group. Then, we built a “typing system” for the periprosthetic microbiota based on the RandomForest Model. After that, the ‘typing system’ was verified externally. Results We found the periprosthetic microbiota can be classified into four types generally: “Staphylococcus type,” “Pseudomonas type,” “Escherichia type,” and “Cutibacterium type.” Importantly, these four types of microbiotas had different clinical signatures, and the patients with the former two microbiota types showed obvious inflammatory responses compared to the latter ones. Based on the 2014 Musculoskeletal Infection Society (MSIS) criteria, clinical PJI was more likely to be confirmed when the former two types were encountered. In addition, the Staphylococcus spp. with compositional changes were correlated with C-reactive protein levels, the erythrocyte sedimentation rate, and the synovial fluid white blood cell count and granulocyte percentage. Conclusions Our study shed light on the characterization of the periprosthetic environment microbiome in patients after TJA. Based on the RandomForest model, we established a basic “typing system” for the microbiota in the periprosthetic environment. This work can provide a reference for future studies about the characterization of periprosthetic microbiota in periprosthetic joint infection patients.

Abstract Background Periprosthetic joint infection is a serious complication after total joint arthroplasty. Despite that alpha-defensin was used as diagnostic test in the 2018 ICM (international consensus meeting) criteria, its position in the PJI diagnostic pipeline was controversial. Therefore, we performed a retrospective pilot study to identify whether synovial fluid alpha-defensin test was necessary when corresponding synovial fluid analysis (WBC count, PMN% and LE tests) was performed. Methods Between May 2015 and October 2018, a total of 90 suspected PJI patients who underwent revisions after TJA were included in this study. Based on the 2018 ICM criteria, the interobserver agreements between preoperative diagnostic results and postoperative diagnostic results and the interobserver reliability between preoperative diagnostic results and postoperative diagnostic results with or without synovial fluid alpha-defensin tests were calculated. After that, the ROC analysis, and the direct cost-effectiveness of adding alpha-defensin was performed. Results There were 48,16 and 26 patients in the PJI group, inconclusive group and non-PJI group, respectively. Adding the alpha-defensin tests into 2018 ICM criteria can’t change the preoperative diagnostic results, postoperative diagnostic results, and the concordance between preoperative and postoperative diagnostic results. Moreover, the Risk–benefit Ratio is over 90 per changed decision and the direct cost-effectiveness of alpha-defensin was more than $8370($93*90) per case. Conclusions Alpha-defensin assay exhibit high sensitivity and specificity for PJI detection as a standalone test based on the 2018 ICM criteria. However, the additional order of Alpha-defensin can’t offer additional evidence for PJI diagnosis when corresponding synovial fluid analysis was performed (synovial fluid WBC count, PMN% and LE strip tests). Evidence level Level II, Diagnostic study.

Abstract Temperature and biodiversity changes occur in concert, but their joint effects on ecological stability of natural food webs are unknown. Here, we assess these relationships in 19 planktonic food webs. We estimate stability as structural stability (using the volume contraction rate) and temporal stability (using the temporal variation of species abundances). Warmer temperatures were associated with lower structural and temporal stability, while biodiversity had no consistent effects on either stability property. While species richness was associated with lower structural stability and higher temporal stability, Simpson diversity was associated with higher temporal stability. The responses of structural stability were linked to disproportionate contributions from two trophic groups (predators and consumers), while the responses of temporal stability were linked both to synchrony of all species within the food web and distinctive contributions from three trophic groups (predators, consumers, and producers). Our results suggest that, in natural ecosystems, warmer temperatures can erode ecosystem stability, while biodiversity changes may not have consistent effects.

Abstract Climate change is expected to shift the boreal biome northward through expansion at the northern and contraction at the southern boundary respectively. However, biome-scale evidence of such a shift is rare. Here, we used remotely-sensed tree cover data to quantify temporal changes across the North American boreal biome from 2000 to 2019. We reveal a strong north-south asymmetry in tree cover change, coupled with a range shrinkage of tree cover distributions. We found no evidence for tree cover expansion in the northern biome, while tree cover increased markedly in the core of the biome range. By contrast, tree cover declined along the southern biome boundary, where losses were related largely to wildfires and timber logging. We show that these contrasting trends are structural indicators for a possible onset of a biome contraction which may lead to long-term carbon declines.

Objective Periprosthetic joint infection (PJI) caused by non‐tubercular mycobacteria (NTM) is uncommon but catastrophic. However, conclusive clinical data on PJI caused by NTM are lacking. In this case series and systematic review, the clinical manifestations, diagnosis, and management of NTM PJI are summarized and analyzed. Methods From 2012 to 2020, we retrospectively analyzed consecutive PJI cases caused by NTM in our institution. A literature review was also conducted from January 2000 to December 2021, utilizing the PubMed, MEDLINE, Cochrane Library, and EMBASE databases to identify all reported NTM‐induced PJI cases. The clinical characteristics, demographics, pathogen identification, treatment protocols, and prognosis of NTM PJI were summarized and analyzed. Results In this retrospective analysis, seven patients infected with NTM following total joint arthroplasty at our institution were included, including six cases of PJI caused by NTM and one case of septic arthritis (SA) caused by NTM. There were six men and one woman, and their average age was 62.3 years. The average interval between TJA and PJI onset was 4 months. The preoperative serological markers, including the mean ESR (51 mm/h), CRP (4.0 mg/dL), fibrinogen (5.7 g/L), and D‐dimer (1.1 g/L), were increased. Six patients underwent staged revision surgery, and one patient with SA received antibiotic‐loaded bone cement beads to treat the infection. After an average of 33 months of observation following surgical intervention, none of the patients showed any symptoms of infection recurrence. From 2000 to 2021, 68 patients with NTM PJI were found in 39 studies in the published literature. Reinfections occurred within 1 year after arthroplasty in more than half (53.2%) of the patients. M. fortuitum and M. abscesses were the most prevalent rapidly growing mycobacteria (RGM) in all PJI patients, whereas Mycobacterium avium intracellulare (MAC) was the most prevalent slowly growing mycobacterium (SGM). The corresponding antibiotics were amikacin and ethambutol. The rate of culture‐negative without specific clinical symptoms was as high as 36.4% (12/33), while 45% (18/40) utilized additional diagnostic techniques such as NGS. A final clinical follow‐up record was available for 59 patients (86.7%; mean follow‐up period, 29 months), and 10.1% of patients failed to respond to treatment. Conclusion Orthopaedic surgeons should consider NTM in patients with negative routine cultures who are at risk for Mycobacterium infection. Treatment options rely on the accurate result of microbiologic identification and drug sensitivity testing, and to achieve this, it may be necessary to send multiple culture specimens, extend the culture time, and change the culture medium. Every effort should be made to identify NTM and its various subtypes through modern diagnostic tools if necessary.

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7–4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from −0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from −1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0–3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm.

Abstract Background The learning curve of single-port thoracoscopic lobectomy (SPTL) in lung cancer has been widely studied. However, the efficiency of different experience levels of thoracic surgeons in mastering the learning curve is unknown. Hence, we discuss this issue in depth by using several perioperative parameters and oncological outcomes. Methods A total of 120 consecutive cases of SPTL performed by a senior (STS group) and junior (JTS group) thoracic surgeons were retrospectively analyzed. Operation time, estimated blood loss, and duration of postoperative hospital stay were recorded for cumulative summation (CUSUM) learning curve analysis, while the 5-year survival rate was used for oncological evaluation. Results The CUSUM learning curve of the STS group was y = 0.000106x3 − 0.019x2 + 0.852x − 0.036, with a high R-value of 0.9517. When the number of cases exceeded 33, the slope changed from positive to negative. The CUSUM learning curve of the JTS group was y = 0.000266x3 − 0.04x2 + 1.429 × –0.335, with a high R-value of 0.9644. When the number of cases exceeded 25, the slope changed from positive to negative. The learning curve was divided into two phases (phases 1 and 2). The slope of the JTS group in phase 1 was greater than that of the STS group in phase 1 (p 0.05). In addition, the 5-year survival rate was not significantly different between the two groups (p = 0.72). Conclusion This is the first study to analyze the learning curve of thoracic surgeons with different experience levels in mastering SPTL. Moreover, it is also the first study to include multiple perioperative parameters and overall survival to study how quickly surgeons master the SPTL technique. The junior thoracic surgeon was found to have a shorter learning curve for SPTL.

Abstract Periprosthetic joint infection (PJI) is the most devastating complication following total joint arthroplasty (TJA) and is posing a global healthcare challenge as the demand for TJA mounts. Two-stage exchange arthroplasty with the placement of antibiotic-loaded spacers has been shown to be efficacious against chronic PJI. This study aimed to review the key concepts, types, and outcome evaluations of articulating spacers in the two-stage exchange for PJI. Previous studies indicated that articulating spacers have been widely used due to better functional improvement and a comparable infection control rate relative to static spacers. Several types of articulating spacers are reportedly available, including hand-made spacers, spacers fashioned from molds, commercially preformed spacers, spacers with additional metal or polyethylene elements, new or autoclaved prosthesis, custom-made articulating spacers, and 3D printing-assisted spacers. However, limited evidence suggested no significant difference in clinical outcomes among the different subtypes of articulating spacers. Surgeons should be familiar with different treatment strategies when using various spacers to know which is the most appropriate.

Abstract Background The risk of death in sepsis patients with acute respiratory distress syndrome (ARDS) was as high as 20–50%. Few studies focused on the risk identification of ARDS among sepsis patients. This study aimed to develop and validate a nomogram to predict the ARDS risk in sepsis patients based on the Medical Information Mart for Intensive Care IV database. Methods A total of 16,523 sepsis patients were included and randomly divided into the training and testing sets with a ratio of 7:3 in this retrospective cohort study. The outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. Univariate and multivariate logistic regression analyses were used in the training set to identify the factors that were associated with ARDS risk, which were adopted to establish the nomogram. The receiver operating characteristic and calibration curves were used to assess the predictive performance of nomogram. Results Totally 2422 (20.66%) sepsis patients occurred ARDS, with the median follow-up time of 8.47 (5.20, 16.20) days. The results found that body mass index, respiratory rate, urine output, partial pressure of carbon dioxide, blood urea nitrogen, vasopressin, continuous renal replacement therapy, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors. The area under the curve of developed model were 0.811 (95% CI 0.802–0.820) in the training set and 0.812 (95% CI 0.798–0.826) in the testing set. The calibration curve showed a good concordance between the predicted and observed ARDS among sepsis patients. Conclusion We developed a model incorporating thirteen clinical features to predict the ARDS risk in patients with sepsis. The model showed a good predictive ability by internal validation.

Abstract Background Low back pain (LBP) from hip and spinal disorders has been one of the main reasons for visiting physicians in patients with developmental dysplasia of the hip (DDH). It is essential to identify the LBP improvement among all grades of DDH patients treated with total hip arthroplasty (THA) at 5-year follow-up. Methods The study included 407 hips of 306 patients (38 males, 268 females) who underwent THA between July 2007 and December 2016. There were 65 hips in Crowe I, 61 hips in Crowe II, 69 hips in Crowe III, and 212 hips in Crowe IV. One hundred and fourteen hips received subtrochanteric shortening. Patients included 101 bilateral THA (BTHA) and 205 unilateral THA (UTHA). The evaluation was performed through Back Pain Function Scale (BPFS), Harris hip score, Visual Analogue Scale (VAS), operative data and radiographic examinations. Results The BPFS in patients of unilateral Crowe III and IV relieved significantly more (p

Abstract Cardiac aging is evident by a reduction in function which subsequently contributes to heart failure. The metabolic microenvironment has been identified as a hallmark of malignancy, but recent studies have shed light on its role in cardiovascular diseases (CVDs). Various metabolic pathways in cardiomyocytes and noncardiomyocytes determine cellular senescence in the aging heart. Metabolic alteration is a common process throughout cardiac degeneration. Importantly, the involvement of cellular senescence in cardiac injuries, including heart failure and myocardial ischemia and infarction, has been reported. However, metabolic complexity among human aging hearts hinders the development of strategies that targets metabolic susceptibility. Advances over the past decade have linked cellular senescence and function with their metabolic reprogramming pathway in cardiac aging, including autophagy, oxidative stress, epigenetic modifications, chronic inflammation, and myocyte systolic phenotype regulation. In addition, metabolic status is involved in crucial aspects of myocardial biology, from fibrosis to hypertrophy and chronic inflammation. However, further elucidation of the metabolism involvement in cardiac degeneration is still needed. Thus, deciphering the mechanisms underlying how metabolic reprogramming impacts cardiac aging is thought to contribute to the novel interventions to protect or even restore cardiac function in aging hearts. Here, we summarize emerging concepts about metabolic landscapes of cardiac aging, with specific focuses on why metabolic profile alters during cardiac degeneration and how we could utilize the current knowledge to improve the management of cardiac aging.

The ecological conservation red line (ECRL) framework plays an increasingly important role in biodiversity conservation practices in China. Following the implementation of ECRL, it is essential to systematically assess the effectiveness of this national policy in mitigating the negative impacts of human disturbances on habitat quality at the landscape and regional levels. Combined with remote sensing and ground survey data, the Integrated Valuation of Ecosystem Services and Trade-off (InVEST) habitat quality model was used to evaluate the impact of six major anthropogenic activities, including built-up land, roads, mining land, crop land, water pollution, and grazing, on habitat degradation in two grassland river corridors (Kherlen River corridor and Wuerxun River corridor) in Northeastern China. Then, the effectiveness of different management scenarios was assessed based on the ECRL framework. The results indicated that over 80% of the habitat degradation was attributed to grazing activities, followed by water pollution. Built-up land, roads, mining land, and crop land played minor roles in causing habitat degradation. Comparing the implementation of ECRL management scenario with no ECRL management scenario, habitat degradation in the Kherlen River corridor reduced by 17.86%, and habitat quality improved by 11.11%. Similarly, in the Wuerxun River corridor, compared with the priority ECRL management scenario, habitat degradation reduced by 69.23%, and habitat quality improved by 2.38% in the implementation of ECRL management scenario. Moreover, an optimized ECRL management scenario, including grazing prohibition, grassland enclosure, and ecological restoration of river habitats, further improved the habitat quality of both river corridors, resulting in a 12.70% improvement for the Kherlen River corridor and a 3.57% improvement for the Wuerxun River corridor. Our work provides a quantitative framework for optimizing conservation and restoration measures for river corridors based on the ECRL.

BackgroundTwo-stage exchange with placement of antibiotic cement spacer (ACS) is the gold standard for the treatment of chronic periprosthetic joint infection (PJI), but it could cause a high prevalence of acute kidney injury (AKI). However, the results of the current evidence on this topic are too mixed to effectively guide clinical practice.MethodsWe retrospectively identified 340 chronic PJI patients who underwent the first-stage exchange with placement of ACS. The Kidney Disease Improving Global Outcomes guideline was used to define postoperative AKI. Multivariate logistic analysis was performed to determine the potential factors associated with AKI. Furthermore, a systematic review and meta-analysis on this topic were conducted to summarize the knowledge in the current literature further.ResultsIn our cohort, the incidence of AKI following first-stage exchange was 12.1%. Older age (per 10 years, OR= 1.509) and preoperative hypoalbuminemia (OR= 3.593) were independent predictors for postoperative AKI. Eight AKI patients progressed to chronic kidney disease after 90 days. A meta-analysis including a total of 2525 PJI patients showed the incidence of AKI was 16.6%, and AKI requiring acute dialysis was 1.4%. Besides, host characteristics, poor baseline liver function, factors contributing to acute renal blood flow injury, and the use of nephrotoxic drugs may be associated with the development of AKI. However, only a few studies supported an association between antibiotic dose and AKI.ConclusionAKI occurs in approximately one out of every six PJI patients undergoing first-stage exchange. The pathogenesis of AKI is multifactorial, with hypoalbuminemia could be an overlooked associated factor. Although the need for acute dialysis is uncommon, the fact that some AKI patients will develop CKD still needs to be taken into consideration.

Abstract In the dual inverter topology of open‐winding permanent magnet synchronous motor system, due to the symmetry of the topology, switch devices have high similarity in working characteristics, which undoubtedly increases the difficulty in locating faulty devices. In this paper, by analysing the difference of inverter output characteristics before and after switch devices open‐circuit fault, the relationship between the inverter output voltage error and fault location is found, and a fault diagnosis strategy is proposed. In this strategy, a construction method of fault phase characteristic current and the estimation equation of inverter output‐voltage error are designed. Then, the real‐time diagnosis of single device fault and two devices with the same characteristics fault at the same time is realized. Since the strategy proposed in this paper does not need additional voltage sensors, it has the same applicability as the traditional current method. In addition, the diagnosis method based on voltage information reduces the diagnosis time. Besides, this method is independent of the PWM strategy and is not sensitive to the phenomena of dead time and delayed insulated gate bipolar transistors switching. Finally, experimental results demonstrate the effectiveness of the proposed fault diagnosis strategy.

Abstract Background Tuberculosis infection is a major complication of silicosis, but there is no study on whether silicosis can affect the sensitivity of QuantiFERON-TB Gold In-Tube (QFT-GIT) assays. This study will analyze the relationship between silicosis and QFT-GIT, determine the main factor of the QFT-GIT sensitivity decrease in silicosis and explore the methods to increase the sensitivity. Methods Silicosis patients with positive tubercle bacillus cultures were collected. The QFT-GIT, flow cytometry and blocking antibodies were used. Results The sensitivity of QFT-GIT in silicosis patients (58.46%) was significantly decreased and the expression of PD-1 on T cells and CD56+NK cells in pulmonary tuberculosis combined with silicosis were higher than normal tuberculosis patients and silicosis only patients. Further analysis found that the ratio of PD-1+CD4+T and IFN-γwere negatively correlated and blockaded the PD-1 pathway with antibodies can restore the sensitivity of QFT-GIT in silicosis. Conclusions This is the first study to analyze the relationship between immune exhaustion and QFT-GIT in silicosis and found that the sensitivity of QFT-GIT was decreased by the expression of PD-1 on lymphocytes. Antibody blocking experiments increased the expression of IFN-γ and provided a new method to improve the sensitivity of QFT in silicosis. The study also found that silicosis can increase PD-1 expression. As PD-1 functions in infectious diseases, it will promote immune exhaustion in silicosis and lead to tuberculosis from latent to active infection. The study provided theoretical evidence for the diagnosis and immunotherapy of silicosis complications, and it has great value in clinical diagnostics and treatment.

Abstract Background Maternal mortality has always been a major medical concern. Recently, the successful application of extracorporeal membrane oxygenation (ECMO) technology in the rescue of near-death patients has been reported. Case presentation This study retrospectively analyzed 5 cases of critically ill pregnant women/parturients treated with ECMO for respiratory and circulatory failure in the Wuxi People’s Hospital from 2018 to 2020. The mean age of the 5 cases was 30.2 years. Among them, Cases 1 and 5 were treated with Venoarterial (VA) ECMO. Case 1 was diagnosed with congenital heart disease, atrial septal defect, and severe pulmonary hypertension. VA ECMO was applied before cesarean section and was successfully removed after double lung transplantation, but the patient died 10 months after delivery from lung infection. While Case 5 was diagnosed with systemic lupus erythematosus, lupus nephritis, thrombotic vascular disease, HELLP syndrome, and cerebral hemorrhage. VA ECMO was applied 39 days after cesarean section, and the patient died 40 days after delivery due to multiple organ failure. Cases 3 and 4 were treated with Venovenous (VV) ECMO. Case 3 was diagnosed with refractory postpartum hemorrhage, and Case 4 was diagnosed with postpartum hypoglycemic coma, aspiration pneumonia, and shock. They were treated with VV ECMO after delivery, and all survived after successful evacuation. Another Case (Case 2) was diagnosed with postpartum pelvic infection, sepsis and septic shock, and was treated with VA ECMO at 15 days after delivery. The patient changed to VV ECMO at 30 days after delivery due to significant improvement in heart function and poor lung function, but eventually died of multiple organ failure. For the 5 cases, the mean duration of ECMO was 8.7 days, the mean duration of intensive care was 22.0 days, and the mean length of hospital stay was 57.6 days. As a result, 3 patients gradually returned to normal with significant improvement in ventilation and oxygenation after ECMO treatment. Conclusions ECMO technology can be used to treat some of the critical obstetric patients with respiratory and circulatory failure that is ineffective to conventional treatment, but it has no therapeutic effect on the primary disease.

Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

A bias current in the power system will cause saturation of the measuring current transformer (CT), leading to an increase in measurement error. Therefore, in this paper, we first conducted measurements of the direct current component in a 10 kV distribution system. Subsequently, a reverse extraction method for the CT distorted current under direct current bias conditions based on Random Forest Classification (RFC) and Long Short-Term Memory (LSTM) was proposed. This method involves two stages for the reverse extraction of CT distorted currents under direct current bias conditions. In the offline stage, data samples were generated by changing the operating environment of the CT. The RFC classification algorithm was used to divide the saturation levels of the CT, and for each sub-class, Particle Swarm Optimization–Long Short-Term Memory Network (PSO-LSTM) models were trained to establish the mapping relationship between the secondary distorted current and the primary current fundamental component. In the online stage, the saturated data segments were extracted from the secondary current waveform using wavelet transform, and these segments were input into the offline model for current reverse extraction. The simulation results show that the proposed method exhibited strong robustness under various CT conditions, and achieved high reconstruction accuracy for the primary current.

Real-time performance and reliability are two critical indicators in cyber–physical production systems (CPPS). To meet strict requirements in terms of these indicators, it is necessary to solve complex job-shop scheduling problems (JSPs) and reserve considerable redundant resources for unexpected jobs before production. However, traditional job-shop methods are difficult to apply under dynamic conditions due to the uncertain time cost of transmission and computation. Edge computing offers an efficient solution to this issue. By deploying edge servers around the equipment, smart factories can achieve localized decisions based on computational intelligence (CI) methods offloaded from the cloud. Most works on edge computing have studied task offloading and dispatching scheduling based on CI. However, few of the existing methods can be used for behavior-level control due to the corresponding requirements for ultralow latency (10 ms) and ultrahigh reliability (99.9999% in wireless transmission), especially when unexpected computing jobs arise. Therefore, this paper proposes a dynamic resource prediction scheduling (DRPS) method based on CI to achieve real-time localized behavior-level control. The proposed DRPS method primarily focuses on the schedulability of unexpected computing jobs, and its core ideas are (1) to predict job arrival times based on a backpropagation neural network and (2) to perform real-time migration in the form of human–computer interaction based on the results of resource analysis. An experimental comparison with existing schemes shows that our DRPS method improves the acceptance ratio by 25.9% compared to the earliest deadline first scheme.

Shrubs, encompassing important ecosystem engineers in dryland environments, have experienced mass mortality due to long-term droughts across the northwestern Israeli Negev. This massive die-back, which has particularly affected the predominant shrub species Noaea mucronata, predominates edaphically homogeneous (low geodiversity) hillslopes, and is minor in edaphically heterogeneous (high geodiversity) hillslopes. While the homogeneous hillslopes encompass a thick and non-stony soil layer, the heterogeneous hillslopes are composed of a thin, stony layer that overlies weathered, chalky bedrock. The objective of this geophysical study was to assess the subsurface and bedrock characteristics, and its effect on shrub durability vs mass mortality in a dryland ecosystem. A combination of 2D and 3D Ground Penetrating Radar (GPR) imaging and Frequency Domain Electromagnetics (FDEM) was applied. For the GPR procedure, the 2D and 3D reflection imaging was dominated by different dielectric permittivities (dielectric constants) of layers and changes in soil-rock-water content. For the FDEM procedure, the soil-rock electrical properties, alongside the concentration of dissolved salts, determined the measured electrical conductivity (EC). The results show substantial differences in EC values between the homogeneous and heterogeneous hillslopes, which are attributed to differences in the soil-rock interface, soil-water content, and salts concentration. Overall, the much greater EC values of the soil-rock interface in the heterogeneous hillslopes are expected to adversely affect shrub vitality. However, the greater shrub vitality in these hillslopes is likely attributed to the fractured and weathered chalky underground, which retains moisture in pockets or micro-aquicludes that increase water availability for the shrubs. Under these better habitat conditions of the heterogeneous hillslopes, the high EC values do not limit shrub survival or growth. At the same time, the absence of such water pockets in the thick soil layer of the homogeneous hillslopes imposes severe stress under long-term drought conditions, resulting in shrub die-off. This study illustrates the vital role of subsurface geodiversity in determining the resilience of dryland ecosystems to droughts and climatic change.

Hp0521 is the number of cag pathogenicity island (cagPAI) family in Helicobacter pylori (H. pylori, Hp), which encoded Cag2 protein. The aim of this study was to investigate the role of hp0521 on the H. pylori 26,695 strain. We constructed the recombinant prokaryotic expression plasmid pET-32a-hp0521 and pET-32a-hpc0521. Then, we co-cultured the H. pylori wild strain 26,695 and Δhp0521 strain with GES-1 cells to detect CagA protein transport and IL-8 secretion. We found that Δhp0521 mutation increased the expression of cagA, rpoB and promoted the transportation of CagA protein in GES-1 cells. In addition, we also observed that Δhp0521 mutation had no effect on other cagPAI protein stability and the expression of IL-8. Our findings suggested that hp0521 may down-regulated the expression of cagA, rpoB and inhibited the transportation of CagA protein in GES-1 cells and had no effect on growth.

Background Artificial intelligence (AI) has promising applications in arthroplasty. In response to the knowledge explosion resulting from the rapid growth of publications, we applied bibliometric analysis to explore the research profile and topical trends in this field. Methods The articles and reviews related to AI in arthroplasty were retrieved from 2000 to 2021. The Java-based Citespace, VOSviewer, R software-based Bibiometrix, and an online platform systematically evaluated publications by countries, institutions, authors, journals, references, and keywords. Results A total of 867 publications were included. Over the past 22 years, the number of AI-related publications in the field of arthroplasty has grown exponentially. The United States was the most productive and academically influential country. The Cleveland Clinic was the most prolific institution. Most publications were published in high academic impact journals. However, collaborative networks revealed a lack and imbalance of inter-regional, inter-institutional, and inter-author cooperation. Two emerging research areas represented the development trends: major AI subfields such as machine learning and deep learning, and the other is research related to clinical outcomes. Conclusion AI in arthroplasty is evolving rapidly. Collaboration between different regions and institutions should be strengthened to deepen our understanding further and exert critical implications for decision-making. Predicting clinical outcomes of arthroplasty using novel AI strategies may be a promising application in this field.

Mesoscale eddy dipoles are oceanographic structures that can transport ocean water parcels horizontally and vertically in ways that differ from individual mesoscale eddies. The most conspicuous additional feature that presents in dipoles is the cold filament (CF) that can be spontaneously generated between a dipole’s anticyclonic eddy (AE) and cyclonic eddy (CE). A case study in this paper shows that the interaction between the CF and the CE component of the dipole is associated with the structural evolution of the dipole. This interaction is verified in synthesis and normalization studies of the CF dipoles. The CF-dipole interaction entrains CF water into the center of the dipole’ CE and leads to a cold and high chlorophyll center in the upper layer of the CE. The formation of this cold center changes the dipole’s structure by eliminating the phase difference between the thermal and dynamic centers of the dipole. The entrainment also provides a new mechanism for the development of high chlorophyll levels in the CE. In the analysis of the HYCOM (Hybrid Coordinate Ocean Model) simulation of a synthetic CF dipole, the AE has a three-zone structure while the CE only has two. The convergence of the CE’s outermost zone results in a biased interaction.