Search Results (1,270)

Esophageal cancer (EC), including esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), remains a highly lethal disease because of its late diagnosis, significant biological heterogeneity, and frequent resistance to therapy. Growing evidence indicates that microRNAs (miRNAs) are key posttranscriptional regulators involved in tumor initiation, progression, metastasis, and response to treatment. This review provides a comprehensive and updated overview of miRNA dysregulation in both ESCC and EAC, with a specific focus on its emerging clinical relevance in early detection, prognostic assessment, and prediction of therapeutic response. Multiple tissue-based and circulating miRNA signatures, some capable of distinguishing between Barrett’s esophagus (BE), dysplasia, and EAC, demonstrate promising diagnostic performance. In parallel, several miRNAs, including miR-21, miR-23a, miR-455-3p, and miR-196b, have been consistently associated with chemoresistance and radioresistance. Moreover, distinct miRNA expression patterns are correlated with tumor aggressiveness, metastatic potential, and the risk of recurrence, supporting their integration with conventional histopathological and molecular parameters for improved patient stratification. Overall, miRNAs represent a powerful class of biomarkers and potential therapeutic targets in EC, with increasing translational relevance in precision oncology. Full article

Designing robust drinking water treatment schemes for eutrophic sources requires shifting from considering each treatment step separately to considering the full treatment process as a connected system. This study evaluated how treatment configuration and arrangement influence microbial community dynamics, organic carbon removal, and biological stability in a full-scale drinking water treatment plant. A Dutch treatment plant was monitored, operating two parallel lines: one conventional (coagulation, sedimentation, and rapid sand filtration) and one advanced (ion exchange, ceramic microfiltration, and advanced oxidation), both converging into granular activated carbon (GAC) filtration. Microbial and chemical water quality was assessed across treatment stages and seasons. This plant experiences periods of discoloration, taste, and odor issues, and an exceedance of Aeromonas counts in the distribution network. Advanced oxidation achieved a high bacterial cell inactivation (~90%); however, it significantly increased assimilable organic carbon (AOC) (300–900% increase), challenging biological stability. GAC filtration partially reduced AOC levels (from 70 μg Ac-C/L to 12 μg Ac-C/L) but also supported dense (10⁵ cells/mL) and diverse microbial communities (Shannon diversity index 5.83). Moreover, Gammaproteobacteria, which harbor opportunistic pathogens such as Aeromonas, persisted during the treatment. Archaea were highly sensitive to oxidative and physical stress, leading to reduced diversity downstream. Beta diversity analysis revealed that treatment configuration, rather than seasonality, governed the community composition. The findings highlight that treatment arrangement, oxidation, GAC operation, and organic and microbial loads critically influence biological stability. This study proposes integrated strategies to achieve resilient and biologically stable drinking water production when utilizing complex water sources such as eutrophic lakes. Full article

Against the backdrop of the global protein crisis and the textural limitations of alternative proteins, microorganisms are increasingly recognized as versatile structural materials to address these challenges. This review systematically analyzes three key microbial strategies: employing mycelial solid-state fermentation to engineer fibrous meat analogues; utilizing bacterial cellulose scaffolds to enhance the texture of both cultured meat and plant-based products; and applying synthetic biology to design tailored functional proteins. Existing studies confirm that mycelial fermentation significantly improves product texture and production sustainability. In parallel, bacterial cellulose provides highly biocompatible nanoscaffolds, while synthetic biology enables the efficient production and nutritional enhancement of complex animal proteins. Although challenges in scaling production and optimizing flavor persist, advanced bioprocess optimization and genetic engineering offer promising solutions. Future breakthroughs are expected to transition from structural mimicry to true functional creation, establish decentralized production networks, and advance dynamic 4D-printed foods, which will collectively contribute to a more sustainable and resilient global food system. Full article

Bone metastases mark a critical and often terminal phase in cancer progression, where disseminated tumor cells (DTCs) manage to infiltrate and exploit the complex microenvironments of the bone marrow. While most current therapies focus on the well-known late-stage “vicious cycle” of osteolysis, they often overlook the earlier stages, namely, tumor cell colonization and dormancy. During these early phases, cancer cells co-opt hematopoietic stem cell (HSC) niches, using them as sanctuaries for long-term survival. In this review, we bring together emerging insights that highlight a trio of underappreciated cellular players in this metastatic takeover: megakaryocytes, erythroid lineage cells, and perivascular stromal subsets. Far from being passive bystanders, these cells actively shape the metastatic niche. For instance, megakaryocytes and platelets go beyond their role in transport; they orchestrate immune evasion and dormancy through mechanisms such as transforming growth factor-β1 (TGF-β1) signaling and the physical shielding of tumor cells. In parallel, we uncover a distinct “erythroid-immune” axis: here, stress-induced CD71⁺ erythroid progenitors suppress T-cell responses via arginase-mediated nutrient depletion and checkpoint engagement, forming a potent metabolic barrier against immune attack. Furthermore, leptin receptor–positive (LepR⁺) perivascular stromal cells emerge as key structural players. These stromal subsets not only act as anchoring points for DTCs but also maintain them in protective vascular zones via CXCL12 chemokine gradients. Altogether, these findings reveal that the metastatic bone marrow niche is not static; it is a highly dynamic, multi-lineage ecosystem. By mapping these intricate cellular interactions, we argue for a paradigm shift: targeting these early and cooperative crosstalk, whether through glycoprotein-A repetitions predominant (GARP) blockade, metabolic reprogramming, or other niche-disruptive strategies, could unlock new therapeutic avenues and prevent metastatic relapse at its root. Full article

Cable-driven parallel robots (CDPRs) are increasingly favored in rehabilitation, medical devices, and material transportation due to their flexible structure and large transmission distance. The CDPRs with a highly modular and flexible structure are usually easy to be quickly reorganized. It is important to study the dimension and shape optimization of the basis and moving platforms for rapidly reconstructing a high-performance CDPR. The influence of each parameter of CDPRs’ dimension and shape on performance is mutually coupled. Therefore, obtaining the global optimal result by simply superimposing each optimum parameter is usually difficult. To this end, the concepts of a constant stiffness space (CSS) and a cable-tension-constrained workspace (CTCW) and their calculation methods are introduced, and the CDPRs’ dimension and shape are optimized with the maximum CSS and CTCW volume as the optimization indicators. First, the response surface optimization model between CDPRs’ performance and multi-objective optimization parameters is established, taking into account the coupling relationship of each CDPR optimization parameter and the effect on performance, and it is solved by using the Latin hypercube design method. Then, the effect of CDPRs’ dimension and shape on performance is analyzed by using the response surface optimization model, and the CDPRs’ optimization dimensions are provided. Full article

In addition to its obvious benefits, mineral fertilisation also poses a number of threats to the environment. A four-year study was conducted to verify the possibility of integrating the application of a bacterial consortium to reduce the dose of mineral phosphorus (P) fertilisers in field-grown tomato crops without negative effects on yield. The combination of the microbial consortium with a 60% dose of both simple and complex P fertilisers did not show statistical differences in crop productivity and fruit quality compared to the full dose fertilisation each year, even when considering the cumulative yield. This was paralleled by a similar level of leaf chlorophyll index. Plants grown in rhizoboxes showed that the inoculation favoured, in the case of the complex fertiliser, a modification of the root system architecture, though not confirmed statistically. In the case of this kind of fertiliser, the inoculation induced a significant increase in the rhizospheric bacterial metabolic activity, which could be partly accounted for by the agronomic performance. However, this was not paralleled by a modification of the metabolic biodiversity of the bacterial population. The study demonstrated that, for highly demanding crops such as tomato, a valid agrononomic target for the application of microbial-based products integrated into a reduced mineral P fertilisation strategy could reach crop productivity not different from that obtained without them. Such a strategy could favour the adoption of an integrated nutrient management strategy by farmers, with positive impacts also on the environment. Full article

The hospital-at-home (HaH) model delivers hospital-level care to patients in their homes, with point-of-care ultrasonography (PoCUS) serving as a cornerstone diagnostic tool for respiratory illnesses such as pneumonia. This review—the third in a series—addresses the prognostic, synchronous, and potential overdiagnostic concerns of lung ultrasound (LUS) in managing pneumonia within HaH settings. LUS offers advantages of safety and repeatability, allowing clinicians to identify “red flag” sonographic findings that signal complicated or severe disease, including pleural line abnormalities, fluid bronchograms, absent Doppler perfusion, or poor diaphragmatic motion. Serial LUS examinations correlate closely with clinical recovery, showing progressive resolution of consolidations, B-lines, and pleural effusions, and thus provide a non-invasive method for monitoring therapeutic response. Compared with chest radiography, LUS demonstrates superior sensitivity in detecting pneumonia, pleural effusion, and interstitial syndromes across pediatric and adult populations. However, specificity may decline in tuberculosis-endemic or obese populations due to technical limitations and overlapping imaging patterns. Overdiagnosis remains a concern, as highly sensitive ultrasonography may identify minor or clinically irrelevant lesions, potentially leading to overtreatment. To mitigate this, PoCUS should be applied in parallel with conventional diagnostics and integrated into comprehensive clinical assessment. Standardized training, multi-zone scanning protocols, and structured image acquisition are recommended to improve reproducibility and inter-operator consistency. Full article

Resource provisioning and scheduling are essential challenges in handling multiple workflow requests within cloud environments, particularly given the constraints imposed by limited resource availability. Although workflow scheduling has been extensively studied, few methods effectively integrate resource provisioning with scheduling, especially under cloud resource limitations and the complexities of multiple workflows. To address this challenge, we propose an innovative two-stage genetic-based optimization approach. In the first stage, candidate cloud resources are selected for the resource pool under the given resource constraints. In the second stage, these resources are provisioned and task scheduling is optimized on the selected resources. A key advantage of our approach is that it reduces the search space in the first stage through a novel encoding scheme that enables a caching strategy, in which intermediate results are stored and reused to enhance optimization efficiency in the second stage. The proposed solution is evaluated through extensive simulation experiments, assessing both resource selection and task scheduling across a diverse range of workflows. The results demonstrate that the proposed approach outperforms existing algorithms, particularly for highly parallel workflows, highlighting its effectiveness in managing complex workflow scheduling under resource-constrained cloud environments. Full article

Detecting vehicles in remote sensing images of construction sites captured by Unmanned Aerial Vehicles (UAVs) faces severe challenges, including extremely small target scales, high inter-class visual similarity, cluttered backgrounds, and highly variable imaging conditions. To address these issues, we propose BSGNet (Biomimetic Sharpening and Global Receptive Field Network)—a novel detection architecture that synergistically fuses biologically inspired visual mechanisms with global receptive field modeling. Inspired by the Sustained Contrast Detection (SCD) mechanism in frog retinal ganglion cells, we design a Perceptual Sharpening Module (PSM). This module combines dual-path contrast enhancement with spatial attention mechanisms to significantly improve sensitivity to the high-frequency edge structures of small targets while effectively suppressing interfering backgrounds. To overcome the inherent limitation of such biomimetic mechanisms—specifically their restricted local receptive fields—we further introduce a Global Heterogeneous Receptive Field Learning Module (GRM). This module employs parallel multi-branch dilated convolutions and local detail enhancement paths to achieve joint modeling of long-range semantic context and fine-grained local features. Extensive experiments on our newly constructed UAV Construction Vehicle (UCV) dataset demonstrate that BSGNet achieves state-of-the-art performance: obtaining 64.9% APs on small targets and 81.2% on the overall mAP@0.5 metric, with an inference latency of only 31.4 milliseconds, outperforming existing mainstream detection frameworks in multiple metrics. Furthermore, the model demonstrates robust generalization performance on public datasets. Full article

With the continuous increase in energy density of lithium-ion batteries, thermal safety has become a critical constraint on their further development. To address the limitations of mechanical brittleness and high-temperature infrared transparency in SiO₂ aerogels for thermal safety applications in lithium-ion batteries, this study developed a novel nanofiber aerogel composite by incorporating chitosan and MXene into a SiO₂ aerogel matrix. This material retains the characteristics of being ultra-lightweight and highly elastic while significantly enhancing mechanical strength and high-temperature insulation performance. It exhibits a thermal conductivity of 0.034 W/m K at room temperature and 0.053 W/m K at 400 °C, alongside a compressive strength of 1.172 MPa. In battery thermal runaway propagation tests, the aerogel successfully prevented propagation in serially connected and electrically isolated systems, and delayed thermal runaway propagation by 35 s in a parallel system, demonstrating excellent thermal runaway suppression capability. This work provides an effective material solution for the practical application of high-performance thermal insulation aerogels in battery safety protection and offers inspiration for developing new insulating ceramic aerogels. Full article

Two rearing systems are used for Plectropomus leopardus: sea-cage culture and the land-based flow-through aquaculture system. Cages approximate natural conditions and yield many high-quality eggs but offer limited control over ovarian development; the land-based system is highly controllable yet ovaries develop slowly and seldom reach full maturity. We compared these systems by analyzing growth–gonad relationships, monthly hormone profiles (GnRH, E2, GnIH), and hypothalamic transcriptomes in 14- and 18-month-old females. Within each system, body weight did not predict gonadal stage and energy allocation was size-independent. In cages, ovaries reached full maturity with normal histology; in tanks, gonads of all sizes remained at stage III, indicating arrested development. Serum GnRH and E2 displayed parallel increases from 12 to 14 months, declined at 16 months and surged at 18 months in both systems, while GnIH fluctuated inversely, suggesting antagonistic control. Transcriptome analysis identified fshr, cyp11a1 and sox17 as key down-regulated genes in tank-reared fish. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment implicated GnRH, oxidative phosphorylation, ribosome and Wnt pathways in ovarian progression. These findings elucidate reproductive constraints under artificial conditions and provide molecular targets for controllable breeding of P. leopardus. Full article

This paper proposes the integration of the phase-field method (PFM) with deep neural networks (DNNs) for UAV navigation in smart city environments. Using the proposed approach, simulations of an intelligent navigation and obstacle avoidance framework for drones in complex urban environments have been presented. Within the unified PFM-DNN model, phase-field modeling provides a continuous spatial representation, allowing for highly accurate characterization of boundaries between free space and obstacles. In parallel, the deep neural network component offers semantic perception and intelligent classification of environmental features. The proposed model was tested using the 3DCity dataset, which comprises 50,000 urban scenes under diverse environmental conditions, including fog, low light, and motion blur. The results demonstrated that the proposed system achieves high performance in classification and segmentation tasks, outperforming modern models such as DeepLabV3+, Mask R-CNN, and HRNet, while exhibiting high robustness to sensor noise and partial obstructions. The framework was evaluated within a simulated environment, and no real-world UAV drone tests were performed. This framework proves its effectiveness as a promising solution for intelligent drone navigation in future cities thanks to its ability to adapt and respond in dynamic environments. Full article

The accelerating industrial demand for high-speed manipulation has necessitated the development of robotic architectures that effectively balance dynamic performance with workspace size. While serial SCARA robots offer large workspaces and parallel Delta robots provide high acceleration, existing architectures fail to combine these benefits effectively for specific four-degree-of-freedom (4-DoF) Schoenflies motion tasks. This study introduces and characterizes a novel 4-DoF parallel topology, having a symmetrical build-up, which is distinguished by its use of modular 2-DoF linear drive units. The research methodology entails the structural synthesis of the kinematic chain followed by kinematic analysis using vector algebra to derive closed-form inverse geometric models. Additionally, the Jacobian matrix is formulated to evaluate velocity transmission and systematically classify singular configurations, while the dexterity index is defined to assess the rotational capabilities of the mechanism. Numerical simulations of pick-and-place trajectory were also conducted, varying trajectory curvature to analyze kinematic behavior. The results demonstrate that the proposed modular architecture yields a highly symmetric and homogeneous workspace that can be scaled simply by adjusting the drive module lengths. Furthermore, the singularity and dexterity analyses reveal a substantial, singularity-free operational workspace, although tighter trajectory curvatures were found to impose higher velocity demands on the joints. In conclusion, the proposed mechanism successfully achieves the targeted Schoenflies motion, offering a solution for automated industrial tasks. Full article

Positron Emission Tomography (PET) is undergoing a profound transformation. Driven by the convergence of highly sensitive long-axial field-of-view (LAFOV) total-body PET systems and an expanding portfolio of targeted radiopharmaceuticals, PET is progressively evolving beyond its traditional role in oncologic diagnosis and staging. Ultra-sensitive scanners enable whole-body imaging with markedly reduced radiotracer doses, rapid acquisition times, and true dynamic multiparametric imaging across all organs simultaneously. In parallel, molecularly targeted radioligands support tumour phenotyping, theranostic applications, and personalized dosimetry. Together, these advances position PET as a systemic imaging platform capable of interrogating whole-body tumour biology, guiding precision therapies, and potentially enabling early detection or surveillance strategies in selected high-risk populations. This narrative review summarizes the technological foundations of total-body PET, reviews current clinical and translational applications, discusses opportunities and limitations for early detection and surveillance, and outlines a research and implementation roadmap to responsibly translate this paradigm into clinical oncology. Full article

Anomaly detection in financial transactions is a challenging task, primarily due to severe class imbalance and the adaptive behavior of fraudulent activities. This paper presents a reinforcement learning framework for fraud detection (RLFD) to address this problem. We train a deep Q-network (DQN) agent with a long short-term memory (LSTM) encoder to process sequences of financial events and identify anomalies. On a proprietary, highly imbalanced dataset, 10-fold cross-validation highlights a distinct trade-off in performance. While a gradient boosted trees (GBT) baseline demonstrates superior global ranking capabilities (higher ROC and PR AUC), the RLFD agent successfully learns a high-recall policy directly from the reward signal, meeting operational needs for rare event detection. Importantly, a dynamic orthogonality analysis proves that the two models detect distinct subsets of fraudulent activity. The RLFD agent consistently identifies unique fraudulent transactions that the tree-based model misses, regardless of the decision threshold. Even at high-confidence operating points, the RLFD agent accounts for nearly 30% of the detected anomalies. These results suggest that while tree-based models offer high precision for static patterns, RL-based agents capture sequential anomalies that are otherwise missed, supporting for a hybrid, parallel deployment strategy. Full article