Search Results (43,645)

Abstract: Background: Chondrosarcomas (CS) are a rare and heterogenic group of primary malignant bone tumors. In the literature, data on prognostic factors in chondrosarcomas are scarce, and most studies are limited by a short follow-up. The aim of this retrospective study was therefore to determine factors associated with the survival and local recurrence of chondrosarcomas and to compare the results with previous studies. Methods: We retrospectively evaluated 77 patients who were treated for chondrosarcoma of the extremities or pelvis at our tertiary sarcoma center between 1998 and 2007. Patient-related data (age, sex, etc.), tumor characteristics (localization, grading, presence of metastases, etc.), and treatment-related data (previous surgical treatment, type of local treatment, surgical margins, etc.) were evaluated and analyzed for possible correlation with patients’ outcomes. A statistical analysis was performed, including multivariate analysis. Results: The mean survival in our patients was 207 months, which resulted in a five-year survival rate of 76%. Negative prognostic factors for survival were histopathological grading, a patient aged over 70 years, and metastatic disease. The quality of the resection (clear or contaminated margins) negatively influenced both the development of local recurrence and survival too, at least in the univariate analysis. In contrast, factors such as tumor localization (extremities vs. pelvis), pathological fractures, or an initial inadequate resection elsewhere had no significant effect on survival. Conclusions: In accordance with results in the literature, the survival of patients with chondrosarcomas is mainly influenced by factors such as tumor grading, age, and metastases. However, complete resection remains paramount for the outcome in patients with chondrosarcoma—a primary malignant bone tumor with limited alternative treatment options. Full article

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy. Full article

Venous hemangiomas within the central nervous system (CNS) represent a rare pathological entity described by sporadic case reports so far. Comprehensive insights into their histological and imaging features, pathogenesis, natural course, and therapeutic modalities are lacking. This review article presents two patients with contrast-enhancing cerebellar lesions near the tentorium cerebelli lacking edema or diffusion restriction. Despite meticulous preoperative neuroradiological examination, diagnostic classification remained inconclusive. Confronted with both—progressive size and diagnostic uncertainty—surgical intervention was undertaken, resulting in uneventful and complete resection of the lesions. Histopathological analyses subsequently revealed a venous hemangioma in each case. In the literature, the term “hemangioma” is often misapplied and inaccurately used to describe a broad spectrum of vascular anomalies. Therefore, a precise identification is essential since the particular type of vascular anomaly affects its natural course and the treatment options available. We aim to contribute to the understanding of this diagnostically intricate entity by presenting the two cases and by providing a detailed overview of radiological and histopathological features of venous hemangiomas. Full article

With increasing urbanization and the demand for efficient, flexible transportation solutions, demand-responsive transportation services (DTRS) has emerged as a viable alternative to traditional public transit. However, determining the optimal fleet size to balance the investment and operational revenue remains a significant challenge for service providers. In this article, we address the optimization of fleet size in point-to-point shared demand DRTS, which widely operates within many cities. To capture the uncertain passenger demands in the future when planning the fleet size currently, we model this problem with a framework of two-stage stochastic programming with recourse. Fleet sizing decisions are made in the first stage before the uncertain demands are revealed. After the uncertainty is revealed, the second stage involves making additional decisions to maximize operational revenue. The objective is to optimize the total revenue of the first-stage decisions and the expected revenue of the recourse actions. To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. In the experiments, NTRDAC is able to produce results within seconds for transportation networks with over 30 nodes. In contrast, a commercial solver is only capable of solving networks with up to five nodes. The stability tests show that NTRDAC remains robust as the problem size varies. Lastly, the value of the stochastic solution (VSS) was evaluated, and the results indicate that it consistently outperforms the expected value solutions. Numerical experiments show that the performance of the NTRDAC algorithm is quite encouraging and fit for large-scale practical problems. Full article

Background: Busulfan is an FDA-approved alkylating drug used in the chemotherapy of advanced acute myeloid leukemia. The precise mechanisms by which Busulfan kills spermatogonia stem cells (SSCs) are not yet completely understood. Methods: Using a murine model, we evaluated Busulfan-induced apoptosis and DNA damage signaling between testis and ovary tissues. We executed RT-qPCR, analyzed single-nuclei RNA sequencing data and performed in situ hybridization for the localization of the gene expression in the tissues. Results: The results indicate that, in contrast to female germ cells, haploid male germ cells undergo significant apoptosis following Busulfan chemotherapy. Moreover, a gene enrichment analysis revealed that reactive oxygen species may activate the inflammatory response in part through the TNF-α/NF-κB signaling pathway. Interestingly, in the testis, the mRNA levels of TNF-α and TAF7 (TATA box-binding protein-associated factor 7) are downregulated, and testosterone levels suppressed. Mechanistically, the promoter of TNF-α has a conserved motif for binding TAF7, which is necessary for its transcriptional activation and may require further in-depth study. We next analyzed the tumorigenic function of TAF7 and revealed that it is highly overexpressed in several types of human cancers, particularly testicular germ cell tumors, and associated with poor patient survival. Therefore, we executed in situ hybridization and single-nuclei RNA sequencing, finding that less TAF7 mRNA is present in SSCs after chemotherapy. Conclusions: Thus, our data indicate a possible function of TAF7 in the regulation of SSCs and spermatogenesis following downregulation by Busulfan. These findings may account for the therapeutic effects of Busulfan and underlie its potential impact on cancer chemotherapy prognosis. Full article

Background: Sarcopenia, characterized by muscle mass decline due to aging or other causes, is exacerbated by decreased estrogen levels after menopause in women. Isoflavones, a class of flavonoids acting on estrogen receptors, may have beneficial effects on metabolic disorders. We examined these effects in ovariectomized mice fed a high-fat, high-sucrose diet (HFHSD). Methods: At 7 weeks old, female C57BL6/J mice (18–20 g, n = 12) underwent bilateral ovariectomy (OVX), and were then fed a high-fat, high-sucrose diet starting at 8 weeks of age. Half of the mice received isoflavone water (0.1%). Metabolic analyses, including glucose and insulin tolerance tests, were conducted. Muscle analysis involved grip strength assays, next-generation sequencing, quantitative RT–PCR, and western blotting of skeletal muscle after euthanizing the mice at 14 weeks old. Additionally, 16S rRNA gene sequence analysis of the gut microbiota was performed. Results: The results demonstrated that isoflavone administration did not affect body weight, glucose tolerance, or lipid metabolism. In contrast, isoflavone-treated mice had higher grip strength. Gene expression analysis of the soleus muscle revealed decreased Trim63 expression, and western blotting showed inactivation of muscle-specific RING finger protein 1 in isoflavone-treated mice. Gut microbiota analysis indicated higher Bacteroidetes and lower Firmicutes abundance in the isoflavone group, along with increased microbiota diversity. Gene sets related to TNF-α signaling via NF-κB and unfolded protein response were negatively associated with isoflavones. Conclusions: Isoflavone intake alters gut microbiota and increases muscle strength, suggesting a potential role in improving sarcopenia in menopausal women. Full article

Two different microstructures of an Al-12Si (wt. %) alloy were produced, respectively, via a powder laser bed fusion (P-LBF) additive manufacturing and casting. Compared to casting, additive manufacturing of Al-based alloy requires extra care to minimize oxidation tendency. The role of the microstructure on the mechanical properties of Al-12Si (wt. %) alloy was investigated by in situ compression of the micro-pillars. The microstructure of additively manufactured specimens exhibited a sub-cellular (~700 nm) nature in the presence of melt-pool arrangements and grain boundaries. On the other hand, the microstructure of the cast alloy contains typical needle-like eutectic structures. This striking difference in microstructure had obvious effects on the plastic flow of the materials under compression. The yield and ultimate compressive strength of the additively manufactured alloy were 23.69–27.94 MPa and 75.43–81.21 MPa, respectively. The cast alloy exhibited similar yield strength (31.46 MPa); however, its ultimate compressive strength (34.95 MPa) was only half that of the additively manufactured alloy. The deformation mechanism, as unrevealed by SEM investigation on the surface as well as on the cross-section of the distorted micro-pillars, confirms the presence of ductile and quasi-ductile facture of the matrix and the Si needle, respectively, in the case of the cast alloy. In contrast, the additively manufactured alloy exhibits predominantly ductile fractures. Full article

Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach. Full article

Yaks are one of the important livestock on the Qinghai–Tibet Plateau, providing abundant dairy and meat products for the local people. The formation of these dairy and meat products mainly relies on the microbiota in their gastrointestinal tract, which digests and metabolizes plant feed. The yak’s gastrointestinal microbiota is closely related to the health and production performance of the host, but the molecular mechanisms of diet-induced effects in intensively farmed yaks remain to be elucidated. In this study, 40 chyme samples were collected from the four stomach chambers of 10 intensively farmed yaks, and the bacterial diversity and bile acid changes in the rumen (SFRM), reticulum (SFRC), omasum (SFOM), and abomasum (SFAM) were systematically analyzed using 16S rRNA sequencing and bile acid metabolism. Our results showed that the gastrointestinal microbiota mainly distributes in the four-chambered stomach, with the highest microbial diversity in the reticulum. There is a highly negative correlation among the microbiota in the four chambers. The dominant bacterial phyla, Bacteroidota and Firmicutes, were identified, with Rikenellaceae_RC9_gut_group being the dominant genus, which potentially helps maintain short-chain fatty acid levels in the stomach. In contrast, the microbiome within the four stomach chambers synergistically and selectively altered the content and diversity of bile acid metabolites in response to intensive feeding. The results of this study provide new insights into the microbiota and bile acid metabolism functions in the rumen, reticulum, omasum, and abomasum of yaks. This can help uncover the role of gastrointestinal microbiota in yak growth and metabolic regulation, while also providing references for improving the production efficiency and health of ruminants. Full article

Understanding the evolution and the effect of plasticity in plant responses to environmental changes is crucial to combat global climate change. It is particularly interesting in species that survive in distinct environments, such as Eugenia uniflora, which thrives in contrasting ecosystems within the Atlantic Forest (AF). In this study, we combined transcriptome analyses of plants growing in nature (Restinga and Riparian Forest) with greenhouse experiments to unveil the DEGs within and among adaptively divergent populations of E. uniflora. We compared global gene expression among plants from two distinct ecological niches. We found many differentially expressed genes between the two populations in natural and greenhouse-cultivated environments. The changes in how genes are expressed may be related to the species’ ability to adapt to specific environmental conditions. The main difference in gene expression was observed when plants from Restinga were compared with their offspring cultivated in greenhouses, suggesting that there are distinct selection pressures underlying the local environmental and ecological factors of each Restinga and Riparian Forest ecosystem. Many of these genes engage in the stress response, such as water and nutrient transport, temperature, light intensity, and gene regulation. The stress-responsive genes we found are potential genes for selection in these populations. These findings revealed the adaptive potential of E. uniflora and contributed to our understanding of the role of gene expression reprogramming in plant evolution and niche adaptation. Full article

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively. Full article

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R² scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models. Full article

The use of inertial measurement units (IMUs) as an alternative to optical marker-based systems has the potential to make gait analysis part of the clinical standard of care. Previously, an IMU-based system leveraging Rauch–Tung–Striebel smoothing to estimate knee angles was assessed using a six-degrees-of-freedom joint simulator. In a clinical setting, however, accurately measuring abduction/adduction and external/internal rotation of the knee joint is particularly challenging, especially in the presence of soft tissue artefacts. In this study, the in vivo IMU-based joint angles of 40 asymptomatic knees were assessed during level walking, under two distinct sensor placement configurations: (1) IMUs fixed to a rigid harness, and (2) IMUs mounted on the skin using elastic hook-and-loop bands (from here on referred to as “skin-mounted IMUs”). Estimates were compared against values obtained from a harness-mounted optical marker-based system. The comparison of these three sets of kinematic signals (IMUs on harness, IMUs on skin, and optical markers on harness) was performed before and after implementation of a REference FRame Alignment MEthod (REFRAME) to account for the effects of differences in coordinate system orientations. Prior to the implementation of REFRAME, in comparison to optical estimates, skin-mounted IMU-based angles displayed mean root-mean-square errors (RMSEs) up to 6.5°, while mean RMSEs for angles based on harness-mounted IMUs peaked at 5.1°. After REFRAME implementation, peak mean RMSEs were reduced to 4.1°, and 1.5°, respectively. The negligible differences between harness-mounted IMUs and the optical system after REFRAME revealed that the IMU-based system was capable of capturing the same underlying motion pattern as the optical reference. In contrast, obvious differences between the skin-mounted IMUs and the optical reference indicated that the use of a harness led to fundamentally different joint motion being measured, even after accounting for reference frame misalignments. Fluctuations in the kinematic signals associated with harness use suggested the rigid device oscillated upon heel strike, likely due to inertial effects from its additional mass. Our study proposes that optical systems can be successfully replaced by more cost-effective IMUs with similar accuracy, but further investigation (especially in vivo and upon heel strike) against moving videofluoroscopy is recommended. Full article

The continued success of any conventional sweet potato breeding program is limited by knowledge of the inheritance of the traits under study, such as flesh color and tuberous root shape, because of the difficulty of segregating color frequencies by visual separation. The objective of this study was to understand the mode of inheritance of these genetic traits. The cross blocks were established at the Research Institute of Tropical Roots and Tuber Crops (INIVIT-Cuba). Eight parental genotypes of known compatibility were selected, with contrasting phenotypic characteristics to develop segregating populations. To express color objectively, the CIE L*a*b* color space was used (L*: lightness; a* and b*: chromatic coordinates), and four morphometric variables related to the shape and dimensions of the tuberous root were evaluated. From 2419 reciprocal crosses, 2045 botanical seeds and 1764 seedlings were obtained. Incomplete dominance of the white and purple flesh colors over the orange color was observed, as well as transgressive segregation for purple, orange, and white flesh colors and for the shape of the tuberous root. The results allowed us to propose a genetic model of biparental crosses for the improvement of the flesh color of sweet potato (Ipomoea batatas [L.] Lam.), as well as a predictive formula of the progeny to be selected. Full article

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: body mass index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 12 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33, 0.11–0.87) but increased risk in the BFP model (OR: 1.66, 1.01–2.76). The BMI model demonstrates exceptional predictive ability (AUC = 0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions: BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model. Full article