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Abstract The WHO’s classification of renal cell carcinoma (RCC) has identified loss of SMARCB1 as one of the driven mutations. Despite intensive postoperative interventions, the prognosis for SMARCB1-deficient medullary RCC remains poor, indicating insufficiency in current therapy. Herein, we reported the treatment outcomes of five patients with metastatic SMARCB1-deficient medullary RCC and molecular correlates. Four patients were treated with first-line immune checkpoint inhibitors (ICI) plus tyrosine kinase inhibitors (TKI) combination therapy with a median PFS (mPFS) of 12.3 months. Transcriptomic analysis revealed enrichment of immune-related pathways in SMARCB1-deficient medullary RCC compared to clear-cell and papillary RCC. Multiple immunofluorescence (mIF) revealed the association between the formation of mature tertiary lymphoid structures (TLSs) and the favorable response to ICI-based combination therapy. In conclusion, ICI-based combination therapy showed promising anti-tumor activity in SMARCB1-deficient medullary RCC patients. The presence of mature tertiary TLSs may partially elucidate the mechanism underlying treatment response.

ABSTRACT Background Non‐invasive brain stimulation (NIBS) has attracted significant attention as it has been proven to be effective in facilitating upper limb motor recovery in patients with stroke. This meta‐analysis evaluates the efficacy of dual‐site non‐invasive brain stimulation (DS‐NIBS) in improving upper extremity motor function after stroke. Methods A PRISMA systematic search was conducted for randomized controlled trials. Two authors independently extracted data, and the quality of included studies was assessed. Results Ten studies were included in the current review. DS‐NIBS demonstrated a significant effect on upper extremity motor function impairment. However, only two studies showed no clear effects of DS‐tDCS on upper extremity motor function after stroke. Due to the limited number of studies, the effects of DS‐NIBS remain inconclusive. Finding This review found evidence for the relatively higher efficacy of DS‐NIBS on post‐stroke upper extremity motor function impairment, compared to the sham and SS‐NIBS. Additionally, DS‐TMS was found to generate better improvement than DS‐tDCS.

Abstract Background Inflammation plays a pivotal role in the progression of prostate cancer (PCa). Several immune-inflammatory indices, including neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), lymphocyte to monocyte ratio (LMR) and platelet to lymphocyte ratio (PLR), lung immune prognostic index (LIPI), systemic inflammation response index (SIRI) and systemic immune inflammation index (SII), have demonstrated their prognostic values in several solid malignancies. However, Comparisons of superiority with these seven indices’ predictive efficacy within metastatic hormone-sensitive PCa (mHSPC) and metastatic castration-resistant PCa (mCRPC) remain uncertain. Methods We retrospectively included 407 patients diagnosed with mHSPC and 158 patients with mCRPC at West China Hospital from 2005 to 2022. The seven immune-inflammatory indices were computed based on hematological data of mHSPC at initial diagnosis and mCRPC at progression to CRPC. Prognostic value for castration-resistant prostate cancer-free survival (CFS), overall survival (OS), prostate-specific antigen progression-free survival (PSA-PFS) and prostate-specific antigen (PSA) response was assessed using Kaplan–Meier curves, Cox regression models, and chi-square tests. The predictive performance of each immune-inflammatory index was assessed using the area under the curve (AUC) in time-dependent receiver operating characteristic curve (ROC) analysis and C-index calculation. Results All seven immune-inflammatory indices were significantly associated with CFS and OS in the mHSPC cohort, as well as with PSA response, PSA-PFS, and OS in the mCRPC cohort. In the mHSPC cohort, LIPI consistently exhibited higher AUC values compared to NLR, dNLR, LMR, PLR, SII, and SIRI for predicting CFS and OS. This indicates that LIPI had a superior discriminative ability compared to the other indices (C-index of LIPI: 0.643 and 0.686 for CFS and OS, respectively). Notably, the predictive advantage of LIPI over other indices in the mHSPC stage diminished in the mCRPC stage. Conclusions This study firstly confirmed the prognostic value of SII, SIRI and LIPI in mHSPC and mCRPC, and revealed that LIPI had a higher predictive power than NLR, dNLR, LMR, PLR, SII and SIRI in mHSPC. These non-invasive indices can enable clinicians to quickly assess the prognosis of patients.

Abstract Background Poly (ADP- ribose) polymerase inhibitors (PARPi) has been increasingly adopted for metastatic castration-resistance prostate cancer (mCRPC) patients with homologous recombination repair deficiency (HRD). However, it is unclear which PARPi is optimal in mCRPC patients with HRD in 2nd -line setting. Method We conducted a systematic review of trials regarding PARPi- based therapies on mCRPC in 2nd -line setting and performed a Bayesian network meta-analysis (NMA). Radiographic progression-free survival (rPFS) was assessed as primary outcome. PSA response and adverse events (AEs) were evaluated as secondary outcomes. Subgroup analyses were performed according to specific genetic mutation. Results Four RCTs comprised of 1024 patients (763 harbored homologous recombination repair (HRR) mutations) were identified for quantitative analysis. Regarding rPFS, olaparib monotherapy, rucaparib and cediranib plus olaparib showed significant improvement compared with ARAT. Olaparib plus cediranib had the highest surface under cumulative ranking curve (SUCRA) scores (87.5%) for rPFS, followed by rucaparib, olaparib and olaparib plus abiraterone acetate prednisone. For patients with BRCA 1/2 mutations, olaparib associated with the highest probability (98.1%) of improved rPFS. For patients with BRCA-2 mutations, olaparib and olaparib plus cediranib had similar efficacy. However, neither olaparib nor rucaparib showed significant superior effectiveness to androgen receptor-axis-targeted therapy (ARAT) in patients with ATM mutations. For safety, olaparib showed significantly lower ≥ 3 AE rate compared with cediranib plus olaparib (RR: 0.72, 95% CI: 0.51, 0.97), while olaparib plus cediranib was associated with the highest risk of all-grade AE. Conclusion PARPi-based therapy showed considerable efficacy for mCRPC patients with HRD in 2nd -line setting. However, patients should be treated accordingly based on their genetic background as well as the efficacy and safety of the selected regimen. Trial registration : CRD42023454079.

Abstract Background TFE3-rearranged renal cell carcinoma (TFE3-rRCC) is a rare but highly heterogeneous renal cell carcinoma (RCC) entity, of which the clinical treatment landscape is largely undefined. This study aims to evaluate and compare the efficacy of different systemic treatments and further explore the molecular correlates. Methods Thirty-eight patients with metastatic TFE3-rRCC were enrolled. Main outcomes included progression-free survival (PFS), overall survival, objective response rate (ORR) and disease control rate. RNA sequencing was performed on 32 tumors. Results Patients receiving first-line immune checkpoint inhibitor (ICI) based combination therapy achieved longer PFS than those treated without ICI (median PFS: 11.5 vs. 5.1 months, P = 0.098). After stratification of fusion partners, the superior efficacy of first-line ICI based combination therapy was predominantly observed in ASPSCR1-TFE3 rRCC (median PFS: not reached vs. 6.5 months, P = 0.01; ORR: 67.5% vs. 10.0%, P = 0.019), but almost not in non-ASPSCR1-TFE3 rRCC. Transcriptomic data revealed enrichment of ECM and collagen-related signaling in ASPSCR1-TFE3 rRCC, which might interfere with the potential efficacy of anti-angiogenic monotherapy. Whereas angiogenesis and immune activities were exclusively enriched in ASPSCR1-TFE3 rRCC and promised the better clinical outcomes with ICI plus tyrosine kinase inhibitor combination therapy. Conclusions The current study represents the largest cohort comparing treatment outcomes and investigating molecular correlates of metastatic TFE3-rRCC based on fusion partner stratification. ICI based combination therapy could serve as an effective first-line treatment option for metastatic ASPSCR1-TFE3 rRCC patients. Regarding with other fusion subtypes, further investigations should be performed to explore the molecular mechanisms to propose pointed therapeutic strategy accordingly.

Short-chain fatty acids (SCFAs), a subset of organic fatty acids with carbon chains ranging from one to six atoms in length, encompass acetate, propionate, and butyrate. These compounds are the endproducts of dietary fiber fermentation, primarily catalyzed by the glycolysis and pentose phosphate pathways within the gut microbiota. SCFAs act as pivotal energy substrates and signaling molecules in the realm of animal nutrition, exerting a profound influence on the intestinal, immune system, and intestinal barrier functions. Specifically, they contibute to 60-70% of the total energy requirements in ruminants and 10-25% in monogastric animals. SCFAs have demonstrated the capability to effectively modulate intestinal pH, optimize the absorption of mineral elements, and impede pathogen invasion. Moreover, they enhance the expression of proteins associated with intestinal tight junctions and stimulate mucus production, thereby refining intestinal tissue morphology and preserving the integrity of the intestinal structure. Notably, SCFAs also exert anti-inflammatory properties, mitigating inflammation within the intestinal epithelium and strengthening the intestinal barrier’s defensive capabilities. The present review endeavors to synthesize recent findings regarding the role of SCFAs as crucial signaling intermediaries between the metabolic activities of gut microbiota and the status of porcine cells. It also provides a comprehensive overview of the current literature on SCFAs’ impact on immune responses within the porcine intestinal mucosa.

Accurate detection of tea leaf diseases and insects is crucial for their scientific and effective prevention and control, essential for ensuring the quality and yield of tea. Traditional methods for identifying tea leaf diseases and insects primarily rely on professional technicians, which are difficult to apply in various scenarios. This study proposes a recognition method for tea leaf diseases and insects based on improved MobileNetV3. Initially, a dataset containing images of 17 different types of tea leaf diseases and insects was curated, with data augmentation techniques utilized to broaden recognition scenarios. Subsequently, the network structure of MobileNetV3 was enhanced by integrating the CA (coordinate attention) module to improve the perception of location information. Moreover, a fine-tuning transfer learning strategy was employed to optimize model training and accelerate convergence. Experimental results on the constructed dataset reveal that the initial recognition accuracy of MobileNetV3 is 94.45%, with an F1-score of 94.12%. Without transfer learning, the recognition accuracy of MobileNetV3-CA reaches 94.58%, while with transfer learning, it reaches 95.88%. Through comparative experiments, this study compares the improved algorithm with the original MobileNetV3 model and other classical image classification models (ResNet18, AlexNet, VGG16, SqueezeNet, and ShuffleNetV2). The findings show that MobileNetV3-CA based on transfer learning achieves higher accuracy in identifying tea leaf diseases and insects. Finally, a tea diseases and insects identification application was developed based on this model. The model showed strong robustness and could provide a reliable reference for intelligent diagnosis of tea diseases and insects.

IntroductionArid and semi-arid regions are climate-sensitive areas, which account for about 40% of the world’s land surface area. Future environment change will impact the environment of these area, resulting in a sharp expansion of arid and semi-arid regions. Cotoneaster multiflorus is a multi-functional tree species with extreme cold, drought and barren resistance, as well as ornamental and medicinal functions. It was found to be one of the most important tree species for ecological restoration in arid and semi-arid areas. However, bioclimatic factors play an important role in the growth, development and distribution of plants. Therefore, exploring the response pattern and ecological adaptability of C. multiflorus to future climate change is important for the long-term ecological restoration of C. multiflorus in arid and semi-arid areas.MethodsIn this study, we predicted the potential distribution of C. multiflorus in China under different climate scenarios based on the MaxEnt 2.0 model, and discussed its adaptability and the major factors affecting its geographical distribution.ResultsThe major factors that explained the geographical distribution of C. multiflorus were Annual precipitation (Bio12), Min air temperature of the coldest month (Bio6), and Mean air temperature of the coldest quarter (Bio11). However, C. multiflorus could thrive in environments where Annual precipitation (Bio12) >150 mm, Min air temperature of the coldest month (Bio6) > -42.5°C, and Mean air temperature of the coldest quarter (Bio11) > -20°C, showcasing its characteristics of cold and drought tolerance. Under different future climate scenarios, the total suitable area for C. multiflorus ranged from 411.199×104 km² to 470.191×104 km², which was 0.8~6.14 percentage points higher than the current total suitable area. Additionally, it would further shift towards higher latitude.DiscussionThe MaxEnt 2.0 model predicted the potential distribution pattern of C. multiflorus in the context of future climate change, and identified its ecological adaptability and the main climatic factors affecting its distribution. This study provides an important theoretical basis for natural vegetation restoration in arid and semi-arid areas.

The concept of human-centric lighting is gradually emerging. People no longer only consider the lighting function of light, but also begin to pay attention to the comfort and health of light. In this article, we perform an investigation on the circadian regulation of four-primary light-emitting diode (LED) based displays for the people with various ages from 1 year old to 100 years old. The double-Gaussian function is used to mimic the spectral power distribution of monochromatic LED. The main figures of merit of LED display performance are color gamut of Rec. 2020, luminous efficacy of radiation, correlated color temperature (CCT), and also non-visual parameters, such as circadian stimulus and melanopic efficacy of luminous radiation provided by Commission Internationale de l'Eclairage (CIE) standard. Then, the genetic algorithm is adopted for performing the spectral optimization of four-primary LED display while many constraint conditions are set, such as CCT values (2700 K and 6500 K), CCT tolerance, and color distance (Duv < 0.01) in the CIE 1960 UCS color space. Related results of visual and non-visual characteristics are also compared with those of three-primary LED displays. This work would pave a way for the development of healthy four-primary LED display in consideration of various ages of people.

Background Pelvic ring disruption (PRD) is a serious trauma associated with high mortality and disability rates. Poor reduction can lead to complications such as pelvic deformity and delayed fracture healing. Here, we introduce a new technology using mixed reality surgical navigation (MRSN) with an unlocking closed reduction technique (UCRT) frame to assist pelvic fracture reduction and fixation. Methods Thirty patients with PRD were enrolled in this study. All of the patients underwent preoperative CT scans, with the pelvis and tracker segmented into three‐dimensional models. Under MRSN guidance, auxiliary reduction screws were inserted to grasp the pelvic bone. An ideal trajectory for closed reduction was planned, and suitable CS screws were used for stable fixation after good reduction. Operation time, fluoroscopy frequency, and both Matta and Majeed scores were analyzed. Results The mean follow‐up period was 10.8 months (7.5, 12.25 months) (range 6–24 months). The average duration of operation was 212.5 min (187.5, 272.8 min) (range 133–562 min), and the average reduction time was 23.0 min (15.0, 42.5 min) (range 10–70). The average fluoroscopy frequency was 34.0 times (31.5, 52.5 times) (range 23–68 times). One hundred and fifty screws were successfully inserted on the first attempt. All the fractures healed well with no complications. Excellent reduction quality (Matta score ≤4 mm) was achieved in 29/30 cases, and good reduction quality (Matta score between 4 and 10 mm) was achieved in 1/30 cases. All patients achieved bone healing after an average of 4.0 months (3.5, 5.9 months) (range 3–6), as well as good function recovery with an average Majeed score of 91.0 (87.8, 95.0) (range 71–100). Conclusion The MRSN technique described improved reduction accuracy and radiation exposure without considerable extension of operation time.

Abstract Background The mutational pattern of homologous recombination repair (HRR)‐associated gene alterations in Chinese urothelial carcinoma (UC) necessitates comprehensive sequencing efforts, and the clinical implications of HRR gene mutations in UC remain to be elucidated. Materials and Methods We delineated the mutational landscape of 343 Chinese UC patients from West China Hospital and 822 patients from The Cancer Genome Atlas (TCGA) using next‐generation sequencing (NGS). Data from 182 metastatic UC patients from MSK‐IMPACT cohort were used to assess the association between HRR mutations and immunotherapy efficacy. Comprehensive transcriptomic analysis was performed to explore the impact of HRR mutations on tumor immune microenvironment. Results Among Chinese UC patients, 34% harbored HRR gene mutations, with BRCA2, ATM, BRCA1, CDK12, and RAD51C being the most prevalently mutated genes. Mutational signatures contributing to UC differed between patients with and without HRR mutations. Signature 22 for exposure to aristolochic acid was only observed in Chinese UC patients. The presence of HRR mutations was correlated with higher tumor mutational burden, neoantigen burden, and PD‐L1 expression. Importantly, patients with HRR mutations exhibited significantly improved prognosis following immunotherapy compared to those without HRR mutations. Conclusions Our findings provide valuable insights into the genomic landscape of Chinese UC patients and underscore the molecular rationale for utilizing immunotherapy in UC patients with HRR mutations.

Computer vision in marine ranching enables real-time monitoring of underwater resources. Detecting fish presents challenges due to varying water turbidity and lighting, affecting color consistency. We propose a Foreground Region Convolutional Neural Network (FR-CNN) that combines unsupervised and supervised methods. It introduces an adaptive multiscale regression Gaussian background model to distinguish fish from noise at different scales. Probability density functions integrate spatiotemporal information for object detection, addressing illumination and water quality shifts. FR-CNN achieves 95% mAP with IoU of 0.5, reducing errors from open-source datasets. It updates anchor boxes automatically on local datasets, enhancing object detection accuracy in long-term monitoring. The results analyze fish species behaviors in relation to environmental conditions, validating the method’s practicality.

A large input delay, parametric uncertainties, matched disturbances and mismatched disturbances exist extensively in variable air volume air-conditioning systems, which can deteriorate the control performance of the room temperature and even destabilize the system. To address this problem, an adaptive-gain command filter control framework for the room temperature of variable air volume air-conditioning systems is exploited. Through skillfully designing an auxiliary system, both the filtered error and the input delay can be compensated concurrently, which can attenuate the effect of the filtered error and the input delay on the control performance of the room temperature. Then, a smooth nonlinear term with an adjusted gain is introduced into the control framework to compensate for parametric uncertainties, matched disturbances and mismatched disturbances, which relieves the conservatism of the controller gain selection. With the help of the Lyapunov theory, both the boundedness of all the system signals and the asymptotic tracking performance for the room temperature can be assured with the presented controller. Finally, the contrastive simulation results demonstrate the validity of the developed method.

Background: We aim to investigate the prognostic value of different pathological patterns of non-adenocarcinoma prostate cancers (PCa) in radical prostatectomy (RP) and external beam radiation therapy (EBRT). Methods: Data of 470,258 localized PCa patients between 2004 and 2016 were collected from the Surveillance, Epidemiology, and End Results database. Propensity score matching was performed to balance the baseline characteristics of patients in different groups. Kaplan–Meier curves and Cox regression were used for survival analysis. Overall survival (OS) and cancer-specific survival (CSS) were set as endpoints. Results: Totally, 1044 patients with non-adenocarcinoma patterns of PCa were included. Patients with small cell neuroendocrine carcinoma (SCNC) and neuroendocrine differentiation (NED) harbored the worst prognosis in both RP and EBRT among all pathological groups. RP exhibited superior effects to EBRT for this group of cases. Ductal carcinoma (DA) was also related to poorer survival outcomes versus PAC in both local therapies. Yet, for men with DA, both RP and EBRT still improved patients' prognosis against no local therapy (NLT), with RP being the superior modality. Cases harboring mucinous adenocarcinoma (MA) and signet ring cell carcinoma (SRCC) shared comparable clinical outcomes to men with PAC. However, for cases with MA, neither RP nor EBRT was related to better survival outcomes against NLT, while for patients with SRCC, both RP and EBRT prolonged patients’ survival with similar effects. Conclusions: Our study provided a comprehensive view of the treatment effect of RP and EBRT in non-adenocarcinoma PCa patients. These findings could facilitate clinicians in making therapeutic decision-making for non-adenocarcinoma patients.

Abstract Chirality has been a property of central importance in physics, chemistry and biology for more than a century. Recently, electrons were found to become spin polarized after transmitting through chiral molecules, crystals, and their hybrids. This phenomenon, called chirality-induced spin selectivity (CISS), presents broad application potentials and far-reaching fundamental implications involving intricate interplays among structural chirality, topological states, and electronic spin and orbitals. However, the microscopic picture of how chiral geometry influences electronic spin remains elusive, given the negligible spin-orbit coupling (SOC) in organic molecules. In this work, we address this issue via a direct comparison of magnetoconductance (MC) measurements on magnetic semiconductor-based chiral molecular spin valves with normal metal electrodes of contrasting SOC strengths. The experiment reveals that a heavy-metal electrode provides SOC to convert the orbital polarization induced by the chiral molecular structure to spin polarization. Our results illustrate the essential role of SOC in the metal electrode for the CISS spin valve effect. A tunneling model with a magnetochiral modulation of the potential barrier is shown to quantitatively account for the unusual transport behavior.

Abstract Background The objective of this study was to establish the criterion-related validity of the session-rating of perceived exertion (s-RPE) method in adolescent athletes. Methods According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, a meta-analysis (PROSPERO ID: CRD42022373126) was performed using Stata 15.1 software. Eight databases using the following terms: (‘s-RPE’ OR ‘Rating Perceived Exertion session’ OR ‘RPE session’ OR ‘RPE’ OR ‘Rate of Perceived Exertion’ OR ‘Rated of Perceived Exertion’) AND (‘Adolescen*’ OR ‘Youth*’ OR ‘Teen*’) AND (‘validity’ OR ‘correlation’ OR ‘concurrent validity’) were searched up to 2022. Articles meeting the inclusion criteria were screened and adopted the “Methodological Index for Non-Randomized Studies (MINORS)” to evaluate the risk of bias. Results An initial 1798 studies using the s-RPE method were identified and finally, a total of 16 studies were included for further analysis. The relationship between assessment instruments CR-10 or CR-100 modified methods of s-RPE and the heart rate measures of these selected studies were calculated using correlation coefficient (r values) and Fisher’s z-score. A strong to very strong correlation between s-RPE and HR was observed (overall: r = 0.74; CR-10: r = 0.69; CR-100: r = 0.80). CR-100 scale (Fisher’s z = 1.09) was shown to have a higher criterion validity than that of the CR-10 scale (Fisher’s z = 0.85). Conclusion Preliminary findings showed that s-RPE using either CR-10 or CR-100 scales can be used "stand-alone" for monitoring internal training load for children and adolescent athletes. Future studies should focus on whether CR-100 could better perform than CR-10 for junior and children athletes in different age groups and sports as well as the causes leading to potential scoring biases.

Summary: With the rapid development of quantum computing, a variety of quantum convolutional neural networks (QCNNs) are proposed. However, only 1/2n2 features of an n-qubits input are transferred to the next layer in a quantum pooling layer, which results in the accuracy reduction. To solve this problem, a QCNN with a degressive circuit is proposed. In order to enhance the ability of extracting global features, we remove the parameters sharing strategy in the quantum convolutional layer and design a quantum convolutional kernel with global eyesight. In addition, to prevent a sharp feature reduction, a degressive parameterized quantum circuit is adopted to construct the pooling layer. Then the Z-basis measurement is only performed on the first qubit to control the operations on other qubits. Compared with the state-of-the-art QCNN, i.e., hybrid quantum-classical convolutional neural network, the accuracy of our model increased by 0.9%, 1%, and 3%, respectively, in three tasks: quantum state classification, binary code recognition, and quaternary code recognition.

The integration of the primary, secondary, and tertiary industries in rural areas, known as three-industry integration, is a crucial strategy for developing rural industries and implementing the rural revitalization initiative in China. The government’s fiscal support for agriculture serves as a cornerstone for the sustainable development of agriculture and rural regions. This study investigates the mechanisms through which fiscal support for agriculture facilitates the integration of the rural three-industry sectors by driving industrial innovation, enhancing the circulation of production factors, and optimizing resource utilization in rural areas. Using panel data from 30 provinces in China spanning from 2008 to 2020, we evaluate the level of three-industry integration in rural areas using an entropy method and analyze the effects of fiscal support for agriculture on this integration. Our findings reveal that: (1) fiscal support for agriculture significantly promotes the incorporated development of rural three-industry integration in China by acting as a catalyst for horizontal and vertical integration; (2) fiscal support enhances rural infrastructure quality, fosters market connectivity, and attracts business clusters, while also optimizing factor markets and facilitating the efficient allocation of land, finance, and resources, thereby enabling new business entities, such as leading enterprises, to benefit from economies of scale and to expand the rural industrial value chain; (3) the effects of fiscal support for agriculture exhibit significant regional and agricultural development heterogeneity, with Central China and major agricultural provinces demonstrating the most pronounced role in promoting rural three-industry integration.

Summary: The carcinogenesis and progression of hepatocellular carcinoma (HCC) are closely related to viral infection and intestinal bacteria. However, little is known about bacteria within the HCC tumor microenvironment. Here, we showed that intratumoral Mycoplasma hyorhinis (M. hyorhinis) promoted the initiation and progression of HCC by enhancing nuclear ploidy. We quantified M. hyorhinis in clinical tissue specimens of HCC and observed that patients with high M. hyorhinis load had poor prognosis. We found that gastrointestinal M. hyorhinis can retrogradely infect the liver through the oral-duodenal-hepatopancreatic ampulla route. We further found that the increases in mononuclear polyploidy and cancer stemness resulted from mitochondrial fission caused by intracellular M. hyorhinis. Mechanistically, M. hyorhinis infection promoted the decay of mitochondrial fusion protein (MFN) 1 mRNA in an m6A-dependent manner. Our findings indicated that M. hyorhinis infection promoted pathological polyploidization and suggested that Mycoplasma clearance with antibiotics or regulating mitochondrial dynamics might have the potential for HCC therapy.

Osteochondral defects (OCD) cannot be efficiently repaired due to the unique physical architecture and the pathological microenvironment including enhanced oxidative stress and inflammation. Conventional strategies, such as the control of implant microstructure or the introduction of growth factors, have limited functions failing to manage these complex environments. Here we developed a multifunctional silk-based hydrogel incorporated with metal-organic framework nanozymes (CuTA@SF) to provide a suitable microenvironment for enhanced OCD regeneration. The incorporation of CuTA nanozymes endowed the SF hydrogel with a uniform microstructure and elevated hydrophilicity. In vitro cultivation of mesenchymal stem cells (MSCs) and chondrocytes showed that CuTA@SF hydrogel accelerated cell proliferation and enhanced cell viability, as well as had antioxidant and antibacterial properties. Under the inflammatory environment with the stimulation of IL-1β, CuTA@SF hydrogel still possessed the potential to promote MSC osteogenesis and deposition of cartilage-specific extracellular matrix (ECM). The proteomics analysis further confirmed that CuTA@SF hydrogel promoted cell proliferation and ECM synthesis. In the full-thickness OCD model of rabbit, CuTA@SF hydrogel displayed successfully in situ OCD regeneration, as evidenced by micro-CT, histology (HE, S/O, and toluidine blue staining) and immunohistochemistry (Col I and aggrecan immunostaining). Therefore, CuTA@SF hydrogel is a promising biomaterial targeted at the regeneration of OCD.

A numerical and optimization study has been well performed on both the circadian effect and color gamut of laser-based displays (LBDs) consisted of multiple-primary laser diodes (LDs) to realize the ultra-wide color gamut. The LBDs mainly consist of red (R), green (G), and blue (B) LDs (namely by 3-LD scheme), and also consist of red (R), yellow (Y), green (G), and blue (B) LDs (namely by 4-LD scheme). The Gaussian function is adopted to simulate the spectra of LDs due to their extremely narrow full-width at half maximum (FWHM < 2 nm). In order to evaluate the circadian effect of such LBDs, the circadian parameters, such as melanopic efficacy of luminous radiation (MELR), circadian action factor (CAF), and circadian stimulus (CS), are considered and employed in this investigation. The genetic algorithm (GA) is used to search for the optimal values of circadian parameters at a certain thresholds of color gamut. The Rec. 2020 standard is used to evaluate the color gamut, and 70%, 80%, 90%, 95%, and 100% Rec. 2020 are five selected thresholds. The optimal results are across the correlated color temperatures (CCTs) from 2700 K to 6500 K. The circadian tunablity is deduced by the ratio of the maximum MELR at 6500 K CCT to the minimum MELR at 2700 K CCT. The circadian performances are compared with the light-emitting diode (LED)-based displays. It is believed that this work could provide a useful guidance for the circadian regulation of people by the multiple-primary laser-based displays.

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics.

Abstract Background Phellinus linteus (PL), which is a typical medicinal fungus, has been shown to have antitumor and anti-inflammatory activities. However, studies on the effect of anti-photoaging are limited. Studies have shown that exosome-like nanovesicles are functional components of many medicinal plants, and miRNAs in exosome-like nanovesicles play a cross-kingdom regulatory role. At present, research on fungi exosome-like nanovesicles (FELNVs) is few. Results We systematically evaluated the anti-aging effects of PL. FELNVs of PL were isolated, and the functional molecular mechanisms were evaluated. The results of volunteer testing showed that PL had anti-aging activity. The results of component analysis showed that FELNVs were the important components of PL function. FELNVs are nanoparticles (100–260 nm) with a double shell structure. Molecular mechanism research results showed that miR-CM1 in FELNVs could inhibit Mical2 expression in HaCaT cells through cross-kingdom regulation, thereby promoting COL1A2 expression; inhibiting MMP1 expression in skin cells; decreasing the levels of ROS, MDA, and SA-β-Gal; and increasing SOD activity induced by ultraviolet (UV) rays. The above results indicated that miR-CM1 derived from PL inhibited the expression of Mical2 through cross-kingdom regulation and inhibited UV-induced skin aging. Conclusion miR-CM1 plays an anti-aging role by inhibiting the expression of Mical2 in human skin cells through cross-species regulation. Graphical abstract

Abstract Stiffness is an important physical property of biomaterials that determines stem cell fate. Guiding stem cell differentiation via stiffness modulation has been considered in tissue engineering. However, the mechanism by which material stiffness regulates stem cell differentiation into the tendon lineage remains controversial. Increasing evidence demonstrates that immune cells interact with implanted biomaterials and regulate stem cell behaviors via paracrine signaling; however, the role of this mechanism in tendon differentiation is not clear. In this study, polydimethylsiloxane (PDMS) substrates with different stiffnesses are developed, and the tenogenic differentiation of mesenchymal stem cells (MSCs) exposed to different stiffnesses and macrophage paracrine signals is investigated. The results reveal that lower stiffnesses facilitates tenogenic differentiation of MSCs, while macrophage paracrine signals at these stiffnesses suppress the differentiation. When exposed to these two stimuli, MSCs still exhibit enhanced tendon differentiation, which is further elucidated by global proteomic analysis. Following subcutaneous implantation in rats for 2 weeks, soft biomaterial induces only low inflammation and promotes tendon‐like tissue formation. In conclusion, the study demonstrates that soft, rather than stiff, material has a greater potential to guide tenogenic differentiation of stem cells, which provides comprehensive evidence for optimized bioactive scaffold design in tendon tissue engineering.

BackgroundIntimate partner violence (IPV) has been associated with an elevated risk of multiple adverse birth outcomes, yet little is known about how specific IPV influences adverse birth outcomes. The aim of this study was to examine the association between IPV during pregnancy and adverse birth outcomes (i.e., preterm birth, low birth weight, and stillbirth).MethodsSystematic searches were conducted using four databases: EMBASE, Web of Science, PubMed, and CINAHL for observational studies published from 1 January 2011 to 31 August 2021. Two reviewers independently carried out the literature search, study selection, data extraction, assessment of the study, and risk of bias assessment; disagreements were resolved by a third reviewer. A random-effect model was used to calculate the odds ratio (OR) with a 95% confidence interval (CI) for preterm birth, low birth weight, and stillbirth. I2 statistic accompanied by chi-square p-value was used to assess heterogeneity, and funnel plot and Peter's test were used to assess publication bias.ResultsIn total, 23 studies met the inclusion criterion. IPV was associated with preterm birth (OR = 1.84; 95% CI: 1.37–2.49; I2 = 88%), low birth weight (OR = 2.73; 95% CI: 1.66–4.48; I2 = 95%), and stillbirth (OR = 1.74; 95% CI: 0.86–3.54; I2 = 64%). We attained comparable results among all specific IPV including physical, sexual, emotional, and mixed.ConclusionIntimate partner violence and specific IPV during pregnancy were significantly associated with adverse birth outcomes, especially for physical IPV. An urgent need for greater action to prevent or intervene in IPV during pregnancy is warranted.Systematic review registrationCRD42021282936, https://www.crd.york.ac.uk/prospero/.

Using kinematic data from the Gaia Data Release 3 catalog, along with metallicity estimates robustly derived from Gaia BP/RP spectra, we have explored the Galactic stellar halo in search of both known and potentially new substructures. By applying the Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering algorithm in integrals-of-motion space (i.e., E , L _z , and L _⊥ $=\sqrt{{L}_{x}^{2}+{L}_{y}^{2}}$ ), we identified five previously known substructures: Gaia-Sausage-Enceladus (GSE), Helmi streams, I'itoi and Sequoia, and the hot thick disk. We additionally found NGC 3201 and NGC 5139 in this work, and NGC 3201 shares similar distributions in phase space and metallicities to Arjuna, which possibly implies that they have the same origin. Three newly discovered substructures are Prograde Substructure 1 (PG1), Prograde Substructure 2 (PG2), and the Low Energy Group. PG1, with a higher V _ϕ than typical GSE member stars, is considered as either a low-eccentricity and metal-rich part of GSE or part of the metal-poor disk. PG2, sharing kinematic similarities with Aleph, is thought to be its relatively highly eccentric component or the mixture of Aleph and the disk. The Low Energy Group, whose metal-poor component of metallicity distribution function has a mean value [M/H] ∼ −1.29 (compared to that of Heracles [M/H] ∼ −1.26), may have associations with Heracles.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956–0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.

BackgroundFatigue is oftentimes induced by high-intensity exercise potentially via the exceeded amount of reactive oxygen species, leading to diminished functions (e.g., aerobic capacity) and increased risk of injuries. Studies indicate that molecular hydrogen (H2), with antioxidant and anti-inflammatory properties, may be a promising strategy to alleviate fatigue and improve aerobic capacity. However, such effects have not been comprehensively characterized.ObjectiveTo systematically assess the effects of in taking H2 on fatigue and aerobic capacity in healthy adults.MethodsThe search was conducted in August 2022 in five databases. Studies with randomized controlled or crossover designs that investigated the rating of perceived exertion (RPE), maximal oxygen uptake (VO2max), peak oxygen uptake (VO2peak), and endurance performance were selected. The data (mean ± standard deviation and sample size) were extracted from the included studies and were converted into the standardized mean difference (SMD). Random-effects meta-analyses were performed. Subgroup analysis was used to analyze potential sources of heterogeneity due to intervention period, training status, and type of exercise.ResultsSeventeen publications (19 studies) consisting of 402 participants were included. The pooled effect sizes of H2 on RPE (SMDpooled = −0.38, 95%CI −0.65 to −0.11, p = 0.006, I2 = 33.6%, p = 0.149) and blood lactate (SMDpooled = −0.42, 95% CI −0.72 to −0.12, p = 0.006, I2 = 35.6%, p = 0.114) were small yet significant with low heterogeneity. The pooled effect sizes of H2 on VO2max and VO2peak (SMDpooled = 0.09, 95% CI −0.10 to 0.29, p = 0.333, I2 = 0%, p = 0.998) and endurance performance (SMDpooled = 0.01, 95% CI −0.23 to 0.25, p = 0.946, I2 = 0%, p > 0.999) were not significant and trivial without heterogeneity. Subgroup analysis revealed that the effects of H2 on fatigue were impacted significantly by the training status (i.e., untrained and trained), period of H2 implementation, and exercise types (i.e., continuous and intermittent exercises).ConclusionsThis meta-analysis provides moderate evidence that H2 supplementation alleviates fatigue but does not enhance aerobic capacity in healthy adults.Systematic review registrationwww.crd.york.ac.uk/PROSPERO/, identifier: CRD42022351559.

The purpose of this research was to determine if the scientific research device combined heart rate variability combined with an acceleration sensor (Firstbeat Bodyguard 2, BG2) was valid and reliable for time spent in different intensity zones in free-living. A total of 55 healthy participants performed 48-h physical activity (PA) monitoring with BG2, ActiGraph GT3X+ (GT3X+), and completed Bouchard Physical Activity Diary (Bouchard) every night. In the available studies, GT3X+ is considered the gold standard scientific research device for PA monitor. We compared BG2 and Bouchard with GT3X+ by difference, correlation, and agreement of PA and energy expenditure (EE) in free-living. The results showed that BG2 estimated PA more accurately than Bouchard, with a modest correlation (r > 0.49), strong agreement (τ > 0.29), and they had the lowest limits of agreement when estimating moderate to vigorous physical activity (MVPA). The EE estimated by Bouchard was the highest among the three methods, and the correlation and agreement between the three methods were high. Our findings showed that the BG2 is valid and reliable for estimating time spent in different intensity zones in free-living, especially in MVPA.

Objective: This study examined the effects of 12-week complex training (CT) programs on professional firefighters’ occupational activities, strength, and power.Methods: Thirty men professional firefighters were randomly assigned to the CT group (n = 15) and control group (n = 15). The CT group performed complex training and the control group completed resistance training (RT) twice a week over 12 weeks. The occupational activities, strength, and power were assessed at baseline and immediately after the intervention by measuring the performance of 100 m load-bearing run (100 m LR), 60 m shoulder ladder run (60 m SLR), 5 m × 20 m shuttle run (5 m × 20 m SR), 4th-floor climbing rope (4th-floor CR), countermovement jump with arm swing (CMJas), seated medicine-ball throw (SMT), one-repetition maximum bench press (1RM BP), and one-repetition maximum back squat (1RM BS).Results: The results showed that compared to RT, CT induced significantly greater improvements in 60 m SLR (p = 0.007), 4th-floor CR (p = 0.020), CMJas (p = 0.001), and SMT (p < 0.001).Conclusion: These findings suggest that CT is a novel intervention with great promise of improving professional firefighters’ occupational activities, strength, and power.

Abstract Riemerella anatipestifer causes epizootic infectious disease in poultry resulting in serious economic losses especially to the duck industry. In our previous study, R. anatipestifer was found to lyse duck erythrocytes in vitro. In the present study, a random Tn4351 mutagenesis library of hemolytic R. anatipestifer strain SX containing 4000 mutants was constructed to investigate the genetic basis of hemolytic activity. Thirty mutants with reduced hemolytic activity and one with increased hemolytic activity were screened and insertions in 24 genes were identified. Of these genes, four were predicted to encode outer membrane proteins, one encoded a cytoplasmic membrane protein, 11 encoded cytoplasmic proteins, and eight encoded proteins with unknown locations. Based on current annotations of the R. anatipestifer genomes, of the 24 genes, 7 (29.17%) were involved in iron utilization. The hemolytic activities of the complemented strains M2 (pRES-Riean_0790) and M18 (pRES-Riean_0653) were restored, indicating that both Riean_0653 and Riean_0790 are involved in the hemolytic activity of strain SX. However, the recombinant proteins rRiean_0317, rRiean_0790, rRiean_0653, rRiean_1027, rRiean_1143, and rRiean_1561 had no hemolytic activity, suggesting that none were hemolysins.

IntroductionAccurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance.ObjectiveThe present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies.MethodsThe data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method.ResultsThe results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals.ConclusionCompared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Endogenous repair of osteochondral defect is usually limited by the insufficient number of cells in the early stage and incomplete cell differentiation in the later stage. The development of drug delivery systems for sequential release of pro-migratory and pro-chondrogenic molecules to induce endogenous bone marrow-derived mesenchymal stem cells (BMSCs) recruitment and chondrogenic differentiation is highly desirable for in situ osteochondral regeneration. In this study, a novel, all-silk-derived sequential delivery system was fabricated by incorporating the tunable drug-loaded silk fibroin (SF) nanospheres into a SF porous matrix. The loading efficiency and release kinetics of biomolecules depended on the initial SF/polyvinyl alcohol (PVA) concentrations (0.2%, 1% and 5%) of the nanospheres, as well as the hydrophobicity of the loaded molecules, resulting in controllable and programmed delivery profiles. Our findings indicated that the 5% nanosphere-incorporated matrix showed a rapid release of E7 peptide during the first 120 h, whereas the 0.2% nanosphere-incorporated matrix provided a slow and sustained release of Kartogenin (KGN) longer than 30 days. During in vitro culture of BMSCs, this functional SF matrix incorporated with E7/KGN nanospheres showed good biocompatibility, as well as enhanced BMSCs migration and chondrogenic differentiation through the release of E7 and KGN. Furthermore, when implanted into rabbit osteochondral defect, the SF nanosphere matrix with sequential E7/KGN release promoted the regeneration of both cartilage and subchondral bone. This work not only provided a novel all-silk-derived drug delivery system for sequential release of molecules, but also a functional tissue-engineered scaffold for osteochondral regeneration.

With the continuous development of manufacturing technology of micro/nano devices based on GaN, focused gallium ion beam has become a momentous method for machining complex GaN structures. Therefore, this study focuses on the characterization and simulation of sputtering etched profile by Ga FIB on GaN substrate. First, the profile characterization experiments are carried out to analyze the influence of ion dose, dwelling time, and scan area, and the results reflect that the sputtering etched regularity on GaN substrate differs from that on Si substrate, especially in redeposition effect, milling rate, and droplet phenomenon. In addition, the forming mechanism of droplet phenomenon is discussed, and the milling experiments are conducted to research the droplets avoidance method, which shows that the appearance of droplets is impacted by ion dose and dwelling time. Finally, the sputtering yield of GaN under focused gallium ion beam is measured and sorted, and the simulation program is developed based on continuous cellular automaton (CCA) model. This work can provide a guidance for the further application of GaN-based materials.

Swirling flame oscillation, with a local extinguishment-and-reignition phenomenon in advanced low-pollution lean premixed combustion technology, remains a challenge in understanding the underlying physics and predict in technical combustors. Here, a prediction method on swirling flame lean blowout (LBO) is proposed from flame image morphological features. In this method, flame features are first extracted by performing morphological algorithms on flame images. Then, the information of the time series of images is included. By designing the blowout state judgment criterion and the blowout state description method, the typical binary judgment is transformed into a numerical prediction. Finally, a random forest regression model is applied to build a predictive model for the swirling flame LBO. The results show that, with the data set from nine operating conditions, the model can achieve a determination coefficient of 0.9766 and a root mean square error of 3.78 on the 10% test set, which shows a strong generalization ability. This method exhibits potential for practical application in LBO control due to its simplicity and efficiency.

Summary: Myeloid-derived suppressor cells (MDSCs) enhance the proliferation of endothelial cells to stimulate angiogenesis. However, many aggressive malignant tumors do not have endothelial cell-dependent blood vessels in the early stage and instead generate microcirculation by forming vasculogenic mimicry (VM). To date, the relationship between MDSCs and tumor cells remains the focus of ongoing studies. In this work, MDSCs were co-cultured with mouse melanoma cells and can enhance proliferation and VM formation of melanoma cells. For MDSCs targeting, doxycycline (DOX) was found to selectively suppress PMN-MDSCs but has no influence on T cells. In addition, DOX pretreatment substantially reduced the promoting ability of MDSCs for the VM formation of B16-F10 cells. DOX also inhibited tumor growth and enhanced the antitumor activity of PD-1 inhibitors in C57BL6 and BALB/c mice subcutaneously inoculated with B16-F10 and 4T1 cells, respectively. In conclusion, the combination of DOX and PD-1 inhibitor could be an anticancer strategy.

Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis.

Recent efforts in the network function virtualization (NFV) field have targeted ameliorating end-to-end latency of service function chains (SFCs) using network function (NF) composition. NF composition breaks the NF into building blocks and decides the appropriate combination for these blocks. However, two issues remain in current NF composition methods: (1) In the sequential scope, the fine-grained function's block-level composition eliminates redundancy, but with the drawback of flexibility restriction. (2) In the vertical scope, complete NF parallelism adds overhead in packet copying and reordering. To reconcile this, here we present ParaGraph, a subgraph-level NF composition with delay-balanced parallelism. ParaGraph has three main components: an NF subgraph-extraction module that extrapolates right-grained core function subgraphs from NFs; an orchestrator that dynamically composes subgraphs with delay-balanced parallelism; and an infrastructure performing lightweight packet copying and merging. We implement a ParaGraph prototype based on Click and the Data Plane Development Kit (DPDK); extensive evaluations show that with minimum overhead, ParaGraph reaches line-speed packet processing and reduces latency by up to 55% compared to state-of-the-art methods.

Tennis is an individual sport that requires a specialized training and match preparation for every player. Former studies in tennis have tried many approaches to analyze player’s performance using descriptive statistics (such as: match time, rally duration, or game number) and match-related statistics (such as: first and second serve percentage, aces, double faults, or net points won). Although helpful in providing general information of match characteristics and evaluating player performance, there is scarce consideration over how elite male players behave on point-by-point basis according to different contextual variables. This study aimed to assess predictors of point outcome (win/lose) related to year, tournament types, round, set, quality of opposition, game status, serve and rally by using match data of 2011-2016 four Grand Slam. A total of 29675 points were recorded and analyzed through classification tree analysis (exhaustive CHAID). The results showed that the performance of tennis player was conditioned by the familiarity with court surfaces as well as other contextual variables, such as game type, quality of opposition, match status, serve and return and rally length (p< 0.05). These results provide insight for coaches and players when planning the game strategy, allowing more appropriate tactics under different game status.

For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response.

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and the similar appearances of fish. This article proposes a robust underwater fish-school tracking algorithm (FSTA). The FSTA is based on the tracking-by-detection paradigm. To solve the problem of low recognition accuracy in an underwater environment, we add an amendment detection module that uses prior knowledge to modify the detection result. Second, we introduce an underwater data association algorithm for aquatic non-rigid organisms that recombines representation and location information to refine the data matching process and improve the tracking results. The Resnet50-IBN network is used as a re-identification network to track fish. We introduce a triplet loss function based on a centroid to train the feature extraction network. The multiple-object tracking accuracy (MOTA) of the FSTA is 79.1% on the underwater dataset, which shows that it can achieve state-of-the-art performance in a complex real-world marine environment.

The performance of professional tennis players in the four major Grand Slam tournaments has always been an important research topic, which advances the understanding of the current development of tennis. However, there is little known about the difference between higher-ranked and lower-ranked players considering match performance statistics. The study was aimed to explore the technical, tactical, and physical performance indicators that best discriminate seeded and non-seeded male players in Grand Slams. A total of 549 matches played by 189 individual players during 2015–2017 Grand Slam men’s singles were sampled, with corresponding match statistics gathered for each player observation, concerning players’ serving, returning, net point, break point, efficiency, and physical performance. The results showed that the seeded players outperformed the non-seeded players in serve and return, break point, net point, and efficiency-related indicators, while the following indicators contributed most to the separation of two player categories: serve and return of serve points won (%), ace (%), peak serve speed, net points won (%), break point per return game, break point saved, winner and unforced error ratio, and dominance ratio. The research findings evidenced the decreased competitive balance in men’s competition during Grand Slams due to a rank-based seeding system, whereas coaches could use the information to fine-tune the training benchmarks and match planning.

Detecting foot-ground contact is important for inertial motion capture systems because it can provide kinematic constraints to improve human motion capture accuracy. The popular contact detection methods based on zero-velocity detection are only applicable to applications with regular movement patterns, such as pedestrian dead reckoning and gait analysis. As for arbitrary locomotion in motion capture, reliable foot-ground contact detection for universal inertial motion capture is challenging in the presence of motion kinematics and dynamics estimation errors. This paper proposes a novel foot-ground contact detection method for inertial motion capture by the fusion of inaccurate estimations of body kinematics and dynamics with a Naive Bayes probabilistic contact model. Based on physical analysis of contact, a series of kinematic and dynamic motion features calculated from motion capture are considered as observations of contact status classification. The proposed framework consists of offline model training based on a constructed labeled multi-person foot contact dataset, real-time contact detection, and an online model adaptation mechanism for enhancing detection performance of different users and setups. Quantitative evaluations of the proposed contact detection method are presented, and the resulting average foot-ground contact detection accuracy among various locomotion is 95.8%.

The purpose of this research was to determine if the commercially available Perception Neuron motion capture system was valid and reliable in clinically relevant lower limb functional tasks. Twenty healthy participants performed two sessions on different days: gait, squat, single-leg squat, side lunge, forward lunge, and counter-movement jump. Seven IMUs and an OptiTrack system were used to record the three-dimensional joint kinematics of the lower extremity. To evaluate the performance, the multiple correlation coefficient (CMC) and the root mean square error (RMSE) of the waveforms as well as the difference and intraclass correlation coefficient (ICC) of discrete parameters were calculated. In all tasks, the CMC revealed fair to excellent waveform similarity (0.47–0.99) and the RMSE was between 3.57° and 13.14°. The difference between discrete parameters was lower than 14.54°. The repeatability analysis of waveforms showed that the CMC was between 0.54 and 0.95 and the RMSE was less than 5° in the frontal and transverse planes. The ICC of all joint angles in the IMU was general to excellent (0.57–1). Our findings showed that the IMU system might be utilized to evaluate lower extremity 3D joint kinematics in functional motions.

Annual draft combine test of National Basketball Association (NBA) is a key player testing process where prospective players with extraordinary athletic abilities are evaluated and the assessment results would further inform the determination of prospective players for the league during draft day. Nonetheless, key attributes from the combine test that distinguished successful players in the draft from those unselected has yet to be investigated. The study was aimed to: (i) compare the difference between NBA drafted and undrafted players from five playing positions, considering anthropometric characteristics and physical fitness ability during draft combine test; and (ii) determine the key combine test factors that most effectively discriminate between draft groups. A total of 3,610 players participating in the 2000–2018 NBA draft combine test were included. Independent t-test was applied to compare difference between drafted and non-drafted players in variables related to anthropometrics, and strength and agility test. A descriptive discriminant analysis was subsequently used to identify which variables could best discriminate between two draft groups in each playing position. The significance level was set at p < 0.05. The drafted players from five positions outperformed the undrafted in height, wingspan, vertical jump height and reach, line agility and three-quarter sprint test (p < 0.01, ES = 0.26–0.87). The discriminant functions for each position (p < 0.001, Λ = 0.81–0.83) were emphasized by specific variables that discriminated both draft groups. The findings revealed that in addition to height and wingspan, leg power served as key determinants for being drafted as guards, as did agility and speed for power forwards and centers.

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.

Pulsed waterjet can break rocks effectively by taking advantage of the water hammer effect, and is thus widely used in mining, petroleum, and natural gas fields. With the aim to further clarify the flow field characteristics of pulsed jets induced by a Helmholtz oscillator, large eddy simulation was conducted under different operating pressures. The velocity distribution, mean flow field, and the coherent structure were examined using the oscillators of different cavity lengths and diameters. The results clearly showed that the major frequency of jet pulsation gradually increased with the increase of operating pressure. A stable periodic velocity core was formed at the outlet of the Helmholtz oscillator, while the external flow field was subjected to periodic impact. As a result, the ambient fluid was strongly entrained into the jet beam. With the increase of the cavity length, the length of the core segment decreased while the energy loss caused by the cavity increased, which was also accompanied by a rapid attenuation of the axial velocity at the jet outlet. The coherent structure of the jet in the oscillator with small cavity diameter was more disordered near the nozzle outlet, and the vortex scale was larger. The effect of cavity diameter can be reflected in the feedback modulation of the jet in the cavity. Compared with the conical nozzle, the length of the core section of the jet was shorter, but the jet had better bunching, a smaller diffusion angle, and better mixing performance. These results provide a further understanding of the characteristics of pulsed water jet for better utilizations in the fields of energy exploitation.

Oil recovery, including conventional and viscous oil, can be improved significantly by flooding with polymer solutions. This chemical flooding method can increase oil production, and it can improve the macrodisplacement efficiency and microsweep efficiencies. In this study, we establish physical models that include the dead-end and complex models based on the pore-network pattern etched into glass, using the snappyHexMesh solver in OpenFOAM. These models capture the complexity and topology of porous media geometry. We establish a mathematical model for transient flows of viscoelastic polymers using computational fluid dynamics simulations, and we study the distributions of pressure and velocity for different elasticity scenarios and different flooding process. The results demonstrate that the pressure difference increases as the relaxation time decreases, before the flow reaches its steady state. For a steady flow, elasticity can give rise to an additional pressure difference, which increases with increasing elasticity. Thus, the characteristics of pressure difference vary before and after the flow becomes steady; this phenomenon is very important. Velocity contours become more widely spaced with elasticity increase. This suggests that elasticity of the polymer solutions contributes to the microsweep efficiency. The results of the study provide the necessary theoretical foundation for laboratory experiments and development of methods for polymer flooding and can be helpful for the design and selection of polymers for polymer flooding.